Paul Wilton

Data Language are trusted experts delivering knowledge graph products and services to global brands since 2010.

Knowledge Graph Engineering experts - Data Language

Data Language has exceptional experience in the design, implementation and
deployment of knowledge graphs, linked data, and semantic web technologies. We
have been working with graph databases since our inception, when our founders
worked on possibly the UK’s first enterprise scale knowledge graph for BBC News
and Sport. Since then we have helped global organisations such as Cochrane,
Wellcome, and Euromoney implement them too. Our typical approach to implementing
a knowledge graph solution is:

Domain Driven Design
Through collaborative domain modelling exercises, we work with stakeholders to
gain a broad understanding of the target domain, iteratively refining and
validating ontology models to achieve the ideal level of detail to deliver the
best outcomes, business value and utility.

Technical and Data Architecture
Not all graph databases are equal. We are wholly vendor-agnostic, and our deep
experience in designing scalable, maintainable, and evolvable knowledge graph
solutions, means we will always select the right graph technology to deliver
your use-cases. We understand the difference between label-property graphs and
RDF based graph databases. We have a total grasp on the practical and pragmatic
use of semantics and inference to ensure your solution is not over-engineered
but delivers just the right level of complexity to optimise your total cost of
ownership, and ensure your solution can evolve as your business evolves.

Integration - Data and Software Engineering
We can help you integrate the knowledge graph with your business systems and
workflows, either by helping your own software engineering team with querying
the graph using query languages such as SPARQL or Gremlin, through to developing
microservices and web interfaces that encapsulate your target use-cases. We are
skilled at data engineering, and can help you populate your knowledge graph,
transforming and loading your own business data, and linking to or ingesting
open-data as required.

Automation and Deployment
We can work with and guide your own infrastructure team, or take on the task of
engineering, integration and deployment entirely ourselves. We are skilled at
contemporary best practices for deployment of infrastructure in the cloud or
on-prem. Our devops engineers are skilled in the use of infrastructure-as-code
for fully automating the deployment and maintenance of the entire knowledge
graph solution.

Graph databases

Knowledge Graph Engineering

Matt Shearer

Domain-driven design, or domain modelling is about optimising a team's knowledge and understanding of the domain it works in. Find out how DDD can benefit your project here.

What is Domain Driven Design and why do you need it?

What is Domain-Driven Design?
Domain-Driven Design (DDD) is a method for developing a team's understanding of
a problem space. It emphasises placing the primary focus of a project on the
core area of the business (the core domain). This often takes the form of
facilitated workshops with domain experts and the development of a shared set of
conceptual models. DDD has the additional benefit of creating a shared
understanding (ubiquitous language) between teams when designing technical
solutions.

Skilled facilitation of domain modelling sessions helps our clients get to grips
with their domain. Through collaborative domain modelling exercises, we work
with stakeholders to gain a broad understanding of the target domain. We
typically focus on the high value elements of the domain, iteratively refining
and validating models to achieve the right level of detail to deliver the most
business value.

Would my project benefit from Domain-Driven design?
Common reasons that you would look to Domain Driven development would be
projects that need to distill the knowledge of domain experts into a product.
These types of projects can often suffer from a disconnect in domain
understanding. Discovering this disconnect in code or in data models is
expensive to fix. DDD can address this by tackling differences in domain
understanding before implementation begins. It can create a perfect bridge of
understanding between the business stakeholders, the problem-space, and the
technical teams.

Another application of DDD is in projects that require strong information
architecture. Where the intention is to organise a product around the entities
and relationships of the domain. This is often seen in approaches that build on
taxonomies or knowledge graphs.

We see Domain-Driven Design as a critical starting point for all our knowledge
graph implementations, and on the majority of our project engagements, including
development of our own products.

News, features and updates about our product Data Graphs. Find out how this intuitive knowledge graph product is easy to setup and connect. Brought to you from the team that was foundational in the earliest large scale knowledge graph projects in the UK.

Data Graphs - News, features and product updates from Data Language

Data Graphs

Domain-Driven Design

Resolute Square launches, using Data Graphs as a knowledge graph platform, combining CMS, VOD, DAM, SCV & MDM, complete with powerful APIs.

Resolute Square - launches with our knowledge graph SaaS

We are pleased to announce the launch of Resolute Square
[https://resolutesquare.com/?utm_source=datalanguage&utm_medium=newsPost&utm_campaign=launchPost]
, a pro-democracy media organization using Data Graphs [https://datagraphs.com/] 
to provide its knowledge graph -powered Linked Media Platform.

We will publish more detail about the technology in future posts.

Why did Resolute Square choose Data Graphs?
Firstly, Data Graphs enabled the team to combine CMS, VOD, DAM, SCV, MDM, live
streaming, and a knowledge graph to connect the media, all in one place. This
enabled the project to launch quickly, with an enterprise media publishing
platform, complete with powerful APIs, without the need to integrate 4+ vendor
solutions for this central purpose. Speed to Market!

Secondly, this approach enables the Resolute Square team to interlink its media
in order to deliver rich user experiences, and to innovate and pivot rapidly on
an API-first platform. This kind of capability is not available anywhere else in
the market in an integrated SaaS; projects normally need to pin together several
services to achieve this. 

The Knowledge Graph combines Single Customer View with Single Subject View
The domain model joins Creative Works with People, Places, Organisations, and
the Single Customer View "User" model.Another innovation in this project - Video
Streaming
The Resolute Square platform also includes an innovative ultra-low-latency
participatory live video streaming experience, provided by our friends at 
Perfogram
[https://perfogram.com/?utm_source=datalanguage&utm_medium=newsPost&utm_campaign=launchPost]
.

More soon!

Resolute Square Launches, powered by Data Graphs for speed to market

Alexi Buckingham

Taken from our newsletter which you can subscribe to here
[https://datalanguage.com/newsletter].

Last week we presented our knowledge graph product, Data Graphs
[https://datagraphs.com/?utm_source=website&utm_medium=kgc-newsletter-blog&utm_campaign=intro] 
at The Knowledge Graph Conference [https://www.knowledgegraph.tech] at Cornell
Tech on Roosevelt Island, New York.

Based on the presentation topics and workshops at the conference, and backed up
by the "2nd Annual Knowledge Graph Survey", here are our 3 takeaway messages for
you:

Takeaway 1: Organisations are struggling to get started
We heard a lot about the challenges of skills gaps, organisational inertia, and
governance gaps, and that when trying to adopt knowledge graphs, many
organisations just don’t know where to start. This is reflected in the 2nd
Knowledge Graph Annual Survey with 86% of responses saying that 'Organisational
Inertia' was an important inhibitor to their adoption of knowledge graphs.

Organisations can break through the inertia by using a reference implementation
Our preferred approach to breaking through this inertia is to build a reference
implementation rapidly. Using Data Graphs, you can have a working reference
implementation ready within a day - we did it in 13 minutes here
[https://datalanguage.com/news/13-mins-to-set-up-a-knowledge-graph-with-netflix-disney-amazonprimevideo-and-hulu]
.

A working reference implementation has 3 main benefits:

 1. It helps stakeholders grasp the value of data meaning and interconnection,
    hands-on.
 2. Seeing the knowledge graph visually, and being able to explore it by simply
    clicking from node to node sparks new ideas for all disciplines.
 3. A working version gives teams a real basis on which to iterate and answer
    key questions, including “What KG features do we need?”, “What model works
    well?”, “What kind of inference and path analysis do we need?”, and more.

Have a look at the Knowledge Graph Explorer
[https://media-graph.datagraphs.io/media-demo/people/concepts/urn:media-demo:Person:3Vn1Bxu4W6yzLNL54eB4b5/explore?utm_source=website&utm_medium=kgc-newsletter-blog&utm_campaign=reference-implementation] 
feature of our product Data Graphs below. A reference implementation like this
helps organisations to break through the inertia, and save time spent wallowing
in indecision.

Knowledge Graph Explorer
[https://media-graph.datagraphs.io/media-demo/people/concepts/urn:media-demo:Person:3Vn1Bxu4W6yzLNL54eB4b5/explore?utm_source=email&utm_medium=newsletter&utm_campaign=internal-inertia]
Click here to try out the Knowledge Graph Explorer
[https://media-graph.datagraphs.io/media-demo/people/concepts/urn:media-demo:Person:3Vn1Bxu4W6yzLNL54eB4b5/explore?utm_source=email&utm_medium=newsletter&utm_campaign=internal-inertia]
Takeaway 2: Organisations are looking for key features to enable their plans
In this slide from the KG Annual Survey, you can see the areas here that reflect
what knowledge graph users are interested in, for example 'Search', 'Better
Ontology Management', 'Integration' and 'No-Code/Low-Code'.

Features of interest to KG Survey Respondents

Search
Data Graphs' Advanced Search feature allows you to build complex queries by
chaining and grouping a series of boolean and numeric expressions.

Try Data Graphs' Advanced Search here
[https://media-graph.datagraphs.io/media-demo/_all/concepts?aq=c[director:Person:2mdyLPMIMopbPB0kVUvghw:Quentin%20Tarantino].(c[cast:Person:3ENPqEkRhLUCxj3W3RG1Hp:Samuel%20L.%20Jackson])&pn=1&ps=15&sf=_label&sd=asc&layout=list&viewport=s000000000,1&utm_source=website&utm_medium=kgc-newsletter-blog&utm_campaign=search]

Better Ontology Management
This area has always been complex and somewhat expert-dependent. "No code"
schema development is now in demand. In our demo, you can see the domain model
(the output of our no-code custom schema tools) for shows on Netflix, Disney+,
Amazon Prime Video, and Hulu.

Try Data Graphs' Domain Model here
[https://media-graph.datagraphs.io/media-demo/_domain-model?utm_source=website&utm_medium=kgc-newsletter-blog&utm_campaign=ontology-management]

Integration
Using APIs and webhooks, how can we help developers integrate with minimum
friction? This step-by-step guide shows how to set up your knowledge graph
quickly and effectively.
[https://support.datagraphs.io/knowledge-base/get-started/building-a-knowledge-graph?utm_source=website&utm_medium=kgc-newsletter-blog&utm_campaign=integration]

Building a Knowledge Graph: Step by Step Guide
[https://support.datagraphs.io/knowledge-base/get-started/building-a-knowledge-graph?utm_source=website&utm_medium=kgc-newsletter-blog&utm_campaign=integration]

No-Code/Low-Code
How might businesses get their knowledge graph up and running without having to
dedicate engineers to coding? The tools in this space are improving, and you can
see how we set up a knowledge graph with Data Graphs using data from Netflix,
with APIs and fully featured management UI production-ready, in 12 minutes here.

Creating a Knowledge Graph in 12 Minutes
[https://datalanguage.com/blog/creating-a-knowledge-graph-in-minutes&utm_source=website&utm_medium=kgc-newsletter-blog&utm_campaign=integration]

Takeaway 3: Organisations want to reduce 'Time to Value'
In order to get to market rapidly, and be able to iterate based on real customer
needs, organisations are aiming to get their knowledge graph platforms launched
faster. From inception to commercial operation, how do we do this? Whether it is
for a reference implementation, or for commercial use, tools to enable rapid
model building, data ingest, and ready-to-go APIs are front of mind.

This video shows how a knowledge graph with Netflix, Disney+, Amazon Prime Video
and Hulu data can be built, with APIs and powerful UI, in just 13 minutes.

Watch: 13 minutes to setup the media graph
[https://datalanguage.com/news/13-mins-to-set-up-a-knowledge-graph-with-netflix-disney-amazonprimevideo-and-hulu&utm_source=website&utm_medium=kgc-newsletter-blog&utm_campaign=time-to-value]

But Remember... Preparation is Key
One thing we consistently emphasise is that good preparation is essential. The
speed and relative simplicity of the setup of Data Graphs is only possible with
solid information preparation - data that was already clearly structured and
well organised. Designing your domain model is an essential step in establishing
the shape of your data before embarking on your first implementation. It will
change, no doubt, but do avoid rushing in without a plan. Spend time thinking
about the specifics of your data, the core entities in your domain, the key
relationships between them, and this will enable you to make an effective start.

More on why good preparation is key
[https://datalanguage.com/blog/creating-a-knowledge-graph-in-minutes?utm_source=website&utm_medium=kgc-newsletter-blog&utm_campaign=preparation]

A knowledge graph is a collation of information and data typically within the domain of your business context. The information is stored in a database, often a graph database such that it can be queried, maintained, and interacted with by humans or machines. We have exceptional experience in the design, implementation and deployment of knowledge graphs. We have been working with graph databases since our inception and have delivered knowledge graphs solutions for global organisations.

We have exceptional experience in the design and implementation of knowledge graphs and have delivered knowledge graphs solutions for global organisations.

Knowledge Graphs - our Work, Features, News and Updates

Knowledge Graphs

Reflections and Insights from Knowledge Graph Conference 2022

This video was aired on 6th May at the Knowledge Graph Conference 2022 in NYC, as part of the "Knowledge Graph Tools Track".

The 13-minute knowledge graph: Netflix, Disney, Amazon, Hulu

This video was aired today at the Knowledge Graph Conference 2022 in NYC, as
part of the "Tools Track".

This video was aired live at KGC2022We showed participants how they can create a
Knowledge Graph in 13 minutes using open Netflix, Disney+, AmazonPrimeVideo, and
Hulu data from Kaggle.

It ends with a graph path analysis of how Kevin Bacon connects with Sandra Oh,
via shows and casts, using the graph that we implemented in Data Graphs in 13
minutes.

NB - we had made some careful model design and data preparation before setting
up the knowledge graph.

We took our Knowledge Graphs as a Service product "Data Graphs" to The Knowledge Graphs Conference 2022, loaded with Netflix, AmazonPrimeVideo, Hulu and Disney+ data.

Knowledge Graph Conference 2022

The 13-minute knowledge graph, with Netflix, Disney+, AmazonPrimeVideo, and Hulu data

Find out about our feature at KGC2022, and explore this Netflix, Hulu, and Amazon Prime knowledge graph, using our product "Data Graphs".

Data Graphs at the Knowledge Graph Conference 2022

We've launched a read-only version of Data Graphs for participants at the
Knowledge Graph Conference 2022 to have a go, hands-on.

Data Graphs is our Knowledge Graphs as a Service product. It is used by global
brands to provide a knowledge graph platform to accelerate product
time-to-value.

Lowering the barrier to entry for Knowledge Graph Platforms
Often knowledge graphs are seen as a high-cost and high-risk strategy that only
the largest of companies can implement, over a very long time. 

With Data Graphs, we've ensured that businesses can see value rapidly.
Specifically, this means speeding up the ability to do proof of concepts, and
simple but informative demos that allow organizations to realise early value at
low cost and low risk. Here's an example of one of those demos right now.

13 mins to set up:
Netflix, Disney+, AmazonPrime, and Hulu
When developing Data Graphs, we wanted to make the setup and creation of a
knowledge graph, simple and quick. 

This video, prepared for KGC2022, shows how we created a knowledge graph,
complete with secure API and powerful UI tools, ready for production, in 13
minutes:

Try it yourself! - play with the Data Graphs demo here
[https://media-graph.datagraphs.io/media-demo/_all/concepts/?q=&pn=1&ps=15&sf=_label&sd=asc&layout=list&viewport=s000000000,1&utm_source=website&utm_medium=kgc-post&utm_campaign=faceted-search]
This demo version was created using Netflix, Disney+, AmazonPrime, and Hulu
data, and gives you a good view of just some of the key Data Graphs features.

1. Create your Domain Model for your Knowledge Graph
After you have designed and created your domain model with the business entities
and the relationships that link them together, Data Graphs generates a visual
representation of the model so that you can see clearly how the knowledge of
your business interconnects. 

Check out the Domain Model for Shows, Cast, Genres, Ratings here
[https://media-graph.datagraphs.io/media-demo/_domain-model/?utm_source=website&utm_medium=kgc-post&utm_campaign=domain-model]
.

2. Use our powerful Faceted Search over your knowledge graph
The search capabilities of Data Graphs are stellar and allow you to find and
explore your data precisely and most importantly, quickly. With the Advanced
Search feature, you can build complex queries by chaining and grouping a series
of boolean and numeric expressions, to pinpoint the targets of your search. 

Test the search capabilities of Data Graphs here, on 115k concepts including
shows, cast, directors, genres, countries, and more
[https://media-graph.datagraphs.io/media-demo/_all/concepts/?utm_source=website&utm_medium=kgc-post&utm_campaign=faceted_search]
.

As well as an intuitive visual search tool, all the Data Graphs search features
are available via API so you can integrate them with your business systems and
apps.

Explore the Data Graphs API here [https://support.datagraphs.io/].

3. Use the Graph Explorer to visualise your knowledge graph
The graph explorer is one of the star features of Data Graphs. You can now
visualise your knowledge graph in an interactive representation of your data,
that you can then explore and dive into, using the nodes. By visualising your
entire knowledge graph, from any concept in your dataset, we open up a wealth of
possibilities for your structured data. 

Interact with the knowledge graph explorer here, starting from the "Stranger
Things" node
[https://media-graph.datagraphs.io/media-demo/shows/concepts/urn:media-demo:TvShow:netflix-s3686/explore/?utm_source=website&utm_medium=kgc-post&utm_campaign=knowledge-graph-explorer]
.

4. Use the Graph Path Analysis to gain insights
Data Graphs lets you visually explore paths between any entities in the graph,
in order to find the shortest paths, and to specify constraints on both nodes
and properties that make up these paths. A key feature of knowledge graphs, this
type of analysis is typically used in compliance, fraud detection, and supply
chain analytics. 

You can try out the graph path analysis here
[https://media-graph.datagraphs.io/media-demo/_all/_graph-search/?utm_source=website&utm_medium=kgc-post&utm_campaign=graph-path-analysis]
.

About the Data Graphs demo
The Data Graphs demo is a read-only view of the full version of Data Graphs
[https://datagraphs.com]. The complete product provides a full suite of tools
for curating, ingesting, editing, and maintaining data in a knowledge graph,
along with APIs for integration of your graph data with apps and business
systems.

About the Media Graph data
The media graph data has been sourced from the open data provided by Netflix,
Hulu, Amazon Prime, and Disney+ via kaggle.com here
[https://www.kaggle.com/datasets/shivamb/netflix-shows]. The data sets from
these 4 publishers have been cleaned, loaded, and linked together in our graph
to join 72k actors and directors across 21k  TV shows and movies, all faceted by
genre, rating, country, and year of production.

Data Graphs at The Knowledge Graph Conference 2022

We are holding a team hack event this week with themes around blockchain, AI,
and knowledge graphs. We're solving some problems, and having fun with new tech.

A "Hack event" is a useful way of rapidly exploring, and testing new ideas. The
basic format uses a framed challenge and a time constraint.  Participants
usually "hack" in teams, and the emphasis is on creating working prototypes to
test hypotheses.

In stark contrast to a formal product delivery process, hack events participants
get to try any route without the need for production-grade code, or the
restriction of "business as usual" concerns. 

More often than not the prototypes created are not "the next unicorn"-type
projects, but this is ok! The point is to explore the problem spaces, try novel
approaches, and test new ideas efficiently. 

The ideas are tested in the context of our products, and concepts may make it
into our core product set.

Data Language Hack Event

Charlie Furniss

We have been hard at work improving our Knowledge Graph as a Service product 
Data Graphs [https://datagraphs.com] - in fact it is in a state of continuous
improvement. We have rolled out a number of new features in the first quarter of
2022, and here is a brief roundup on what they are:

Advanced search
Data Graphs Advanced Search feature allows you to build complex queries by
chaining and grouping a series of boolean and numeric expressions. For example
for finding a set of concepts in the graph where they are of Type A and Type B
or Type C, with a date property after a specified date, and a property equal to
some specified value. These searches are available in both the Data Graphs API
and now in the app too.

Saved searches
Saved search enables users to label, store and recall search criteria – great
when you are regularly using complex queries, particularly with Advanced Search
where building up items and groups can be time consuming and you want to return
to the same search results later.

Custom validation rules
When creating a property, custom validation allows you to choose from a set of
configurable rules that can be applied to certain data types and used to control
the property’s data, for example an age limit, an expiry date or a control on
input text. These rules include min/max characters (for text, keyword types),
regular expressions, before/after date (for date, dateTime) and min/max value
(for integer, decimal, latitude, longitude). You can also define cardinalities
on arrays and relationships in the graph.

Enum data types
The new enum data type allows you to create a property with a predetermined list
of options, from which users choose when setting its value. This will be useful
for any field whose value you want to limit to a small controlled vocabulary
that you may not want in your graph as entities in their own right. In the Data
Graphs Concept Editor the enum options are presented as a drop-down list on the
property.

Pre-commit web hooks
Data Graphs pre-commit web hooks allow you to perform a remote impact assessment
of a given event, for example, merging two Concepts, or deleting a Concept when
integrated with some target business system. When one of these actions occurs,
Data Graphs makes an HTTP request with the event’s details in the payload to an
endpoint in your environment, allowing you to analyse the downstream impact of
this change and respond by either accepting or declining the event or by
providing a warning (as illustrated). A full integration of these at Cochrane
has been discussed in this case study here
[https://datalanguage.com/blog/data-graphs-at-cochrane-a-case-study].

Improved geospatial visualisations
As well as the default map view of geospatial data, you can now view concepts
geospatially on dark, light, outdoors, and satellite views with automated entity
clustering based on the zoom-level of the map:

New Features in Data Graphs - Q1 2022

How Cochrane has integrated Data Graphs [https://datagraphs.com] with their own
enterprise graph database of evidence-based healthcare as a complete linked data
management solution.

Population
Data Language has been working with Cochrane as a technical partner for a number
of years now, and we have blogged extensively
[https://datalanguage.com/blog/how-knowledge-graph-technology-is-helping-cochrane-respond-to-covid-19] 
on the digital transformation at Cochrane, including how linked data and an RDF
based knowledge graph have become a core information backbone for their business
processes.

The Cochrane vocabularies of some 300k concepts provide the linked reference
data that describes the PICO domain of evidence-based healthcare, describing the
Populations (and their conditions, ages, sexes, social context), the
Interventions (drugs, devices, procedures, materials etc), Comparisons, and the
Outcomes. These concepts are annotated onto clinical evidence as PICO
micrograph
data structures [https://data.cochrane.org/ontologies/pico/index-en.html] to
describe the questions being answered in clinical evidence. All of Cochrane’s
vocabularies, content metadata and PICO annotations are stored in an enterprise
graph database, and exposed by a cohesive set of microservice APIs that can be
consumed and recombined to allow rapid innovation.

Data Language has provided the technical architecture, design and implementation
of the knowledge graph, microservices, APIs, and tooling that allows the
Cochrane subject matter experts to curate and QA these PICO data structures as
part of their daily business processes. This linked data platform has allowed
Cochrane to rapidly innovate: enhancing the Cochrane Library with linked data
query capability; and build at lightning speed, the largest Covid-19 database of
clinical evidence in the world, comprising more than 100k studies, described
with well-structured PICO metadata,  serving high volumes of requests and
queries to researchers globally.

Condition
While tooling and processes for Cochrane’s SMEs to create and maintain PICO
graphs describing the Cochrane systematic reviews and studies is state of the
art, the tooling around the management of the reference data vocabularies is
less sophisticated. The Cochane vocabularies consist of a highly curated set of
linked data concepts that form a graph in their own right with class and
property semantics, transitivity, and multi-parent taxonomic structures.
Although Cochrane has an existing visual editor tool to perform operations on
individual concepts in the reference data, it is limited in functionality, and
does not allow for bulk ingest and updates, rich search and discovery operations
for quality assurance, data cleansing, and other operations such as merge,
migrate, and workflow.

To solve the vocabulary management problem, we have integrated our product Data
Graphs [https://datagraphs.com] into the Cochrane linked data ecosystem.

What is Data Graphs ?
Data Graphs is a SaaS product designed to lower the barrier of entry to
Knowledge Graphs. While it can be used purely as a Knowledge Graph in its own
right, it can also be used as a linked data vocabulary management tool, and
integrated with an enterprise graph database, or other business systems using
its API and Web-hook event triggers.  It has an intuitive user experience for
managing linked data, and powerful search, discovery and visualisation
capability, effectively making an ideal vocabulary management tool for Cochrane.

Data Graphs allows you to design or replicate any domain model or ontology, and
populate it with linked data, so it was a straightforward task to replicate the
existing Cochane concept model
[https://data.cochrane.org/ontologies/core/index-en.html] and ingest their
vocabularies. The screenshot below shows the cochrane model recreated in Data
Graphs. 


Cochrane Core Concept Model
Data Graphs provides a number of benefits to Cochrane for managing their linked
data vocabularies :

 * Fully decoupling the workflow and processes around managing their linked
   reference data from their business critical systems.
 * Being able to flexibly and easily modify their core concept model without
   impacting their downstream processes.
 * An intuitive user experience for search, discovery, match, merge and
   migration of data.
 * Graph visualisation for exploring their linked data, and identifying
   anomalies and circular relationships.
 * Easy identification of duplicates
 * A full audit history of all changes made to the vocabularies
 * Injection workflow states into the linked data concepts - for managing
   pre-publication, publication and deprecation of terms.

Cochrane’s Data Architecture
Cochrane’s linked data architecture is centred around an enterprise graph
database (Ontotext’s RDF-based GraphDB
[https://www.ontotext.com/products/graphdb/]), a set of microservices and online
tools for annotating systematic reviews and clinical studies with creating well
formed PICO graphs that describe the evidence. A slice of the logical
architecture  is shown below and is discussed in more detail here
[https://datalanguage.com/blog/how-knowledge-graph-technology-is-helping-cochrane-respond-to-covid-19]
:

Cochrane Linked Data ArchitectureIntervention
Integrating Data Graphs
In order to integrate Data Graphs we needed an integration pattern that is
robust and also allows Cochrane SME’s to understand the impact of changes they
may make to the vocabularies, such that they can make an informed decision
whether or not they should proceed with the updates.

For integration with an organisation’s business systems, Data Graphs comes with
an expressive, contemporary REST API, serving JSON-LD payloads (pull), and a
suite of webhooks that can be used for event-driven integrations (push) that can
be configured for different core operations, Create, Update, Delete, Merge etc.
For Cochrane’s purposes the webhooks are ideal. 

Pre-Commit Hooks
Furthermore, for each web-hook, a pre-commit remote endpoint can be configured,
which will be invoked before a write operation is committed in Data Graphs, on
which the target business system can respond with a payload of information that
Data Graphs will render to the user as an impact-assessment if the transaction
is completed. This gives the user an opportunity to back out. These pre-commit
hooks allows the target system to respond for a variety of scenarios :

 1. Silent commit - no impact, just commit the transaction silently, the user is
    not blocked.
 2. Force reject - in which the target system determines the transaction should
    not proceed, and the user is not allowed to commit the change.
 3. An impact assessment, with a risk score, an optional summary of the impact,
    and an optional table of data, that the Data Graphs user will be able to
    make an informed decision upon.

Resilience
When consuming web-hooks (essentially an HTTP endpoint or API in a target
business system, that is configured and invoked on some trigger condition), we
need to ensure to all reasonable endeavours that the event fired will not be
lost due to unforeseen circumstances (eg target system failures, connectivity
issues etc.).  To achieve this, the target system endpoint needs to be as robust
as possible and ideally decoupled from other downstream systems. 

The best way of achieving this in the target system is to build an extremely
lightweight microservice that simply receives the HTTP event payload and places
it on a topic or queue. This queue can then be consumed at the target system's
leisure and can cope with failure downstream (the message will not be lost if
the consumer fails temporarily). The target system can take this further with
dead-letter queues and retries to cope with a variety of challenging
circumstances. This is the pattern adopted at Cochrane and is a pattern we would
generally recommend for consuming webhooks between other products and platforms.
You can see the logical architecture for this below :

Data Graphs Integration Architecture at CochraneThe key integration points
(numbered in red)  within the Cochrane technical stack are :

 1. The Pre-commit web-hook handler. This is a simple webservice that is invoked
    by Data Graphs for a specific transaction event before the transaction is
    committed (for example a Concept Delete). The service makes a query against
    the Cochrane evidence graph to ascertain the impact the change will have.
    For example if a concept is used on thousands of PICO graphs, it is likely
    the user should not be deleting this concept, and the service can respond
    with a straight reject, or return a table of the PICOs affected.  This
    information is presented back to the user to make an informed decision.
 2. The Web-hook handler. This is a lightweight service that is invoked by Data
    Graphs for a specific transaction type (Concept Delete, Merge, Update etc)
     after the change is committed. This service simply places the data event on
    a queue, ready to be processed.
 3. Concept Event Handler. This service does the heavy lifting, taking events
    off the queue, and writing updates to concepts and PICOs in Cochrane’s Graph
    Database using SPARQL. If this fails for whatever reason, the message is
    retained on the queue, and can be tried later. For Cochrane much of this
    code already existed in the legacy LDGraphs API, which was performing a
    similar function for their original linked data editor, so was a case of
    porting this into the event handler.

The webhooks in Data Graphs are easy to configure, as can be seen on the
following screenshots.

First, choose an event type to trigger a web-hook, then configure the endpoint
in the target system, an optional pre-commit hook, and any headers / security
tokens that need to be sent with the request :

Webhook Configuration in Data GraphsMigrating the vocabularies into Data Graphs
The final step was to load the Cochrane linked data vocabularies into Data
Graphs, conforming the domain model we created earlier. This can be done in
different ways

 * Using the Data Graphs REST API
 * Using CSV upload using the CSV to Ontology mapping tool within Data Graphs

In this case, the API was the easiest approach as we could read pages of JSON-LD
concepts from Cochrane’s concepts API, and pipe them into the Data Graphs write
API. As the ontology model’s in both were identical, this was a relatively
trivial migration script.

Outcome
With this in place, Cochrane now has a state of the art linked data management
toolkit. The fully whitelabelled user experience engenders user-ownership.
Cochrane’s information experts can organise their core entities in logical
datasets for rapid navigation:

Cochrane Datasets
Drilling into each dataset, a SME can search for concepts, and easily find
potential duplicates through a complex query or use the label/property facets :

Faceted SearchAnd merge those duplicates with an intuitive UI :

Merging Concepts in Data Graphs
The resulting merge transaction will be handled by the webhook event, not just
to update and merge the concepts in the Cochrane Graph Database, but also update
all PICO graphs in which the source concept was previously used to reference the
target concept instead.  Similarly, deletes will remove the concept reference
from all content and PICO annotations.

Making any changes, such as deleting a concept results in the pre-commit hooks
requesting an impact assessment, so the user can make an informed decision to
continue or cancel:

Pre-commit Impact AssessmentsThe knowledge graph can be explored visually to
help find and correct anomalies in the vocabulary, such as circular
relationships :

Graph Explorer in Data GraphsThe Cochrane bioinformatics SMEs now have many more
capabilities at their disposal taking their ability to deliver the next
generation of evidence-based healthcare innovations to a new level.

.

Data Graphs at Cochrane: a Case Study

Fabio Colasanti

We have recently completed some new "Inline Entity Extraction" benchmarks. This
is a sophisticated "Named Entity Recognition" (NER) product that we launched as
part of our Text AI Services suite in 2020
[https://datalanguage.com/news/inline-entity-extraction-text-ai-ner]. It is
optimized for Knowledge Graph platforms in high-value knowledge organizations.
It identifies entities inline within text media and attributes rich metadata.

The Problem: Automated Tagging in Custom Domains
Our customers are high-value media publishers, knowledge organizations, and DaaS
providers. 

 * A huge portion of their business asset value is locked in their unstructured
   mixed media assets.
 * To liberate this value, accurate inline text classification of 100s of 1,000s
   of entities is required to enable structured connection with bespoke
   information architecture models and/or knowledge graphs.
 * Generic off-the-shelf NLP SaaS solutions do not recognize these custom
   entities, so a mapping exercise is required, and is often a too low
   resolution to provide value.
 * Many NLP solutions do not provide the rich, domain model-aware metadata,
   because they are not trained for the custom domain.
 * Other custom NLP solutions require larger training sets and golden corpora
   that cover the entire entity set.
 * "Build it yourself" becomes very expensive, with a large data scientist and
   engineering overheads, while organizations find themselves re-solving the
   same challenges that we have already solved.
 * "Manage it yourself" has a surprisingly large data science and engineering
   overhead, to overcome production challenges that we have already solved.

Testing methodology
We trained our Inline Entity Extraction SaaS to understand around 180,000
entities from a custom domain.  We then benchmarked it in a controlled
environment where two teams of annotators were asked to annotate 500 documents:-

 * The first team used the old-fashioned method of manually annotating a
   document.
 * The second team moderated the output of the model that had already scanned
   the documents and picked up entities, only needing to confirm or reject
   pre-determined results.

Results: High F1 Score
Here’s how our Data Language Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] Inline
Entity Extraction SaaS performed:

 * Precision: 0.94
 * Recall: 0.90
 * F1: 0.92

We have proven we can train a highly accurate model, specialized for a custom
domain, that can cope with hundreds of thousands of entities with very
high-performing metrics, using a golden corpus that covers only a small subset
of the entities. 

Achieving such high results on the classification of such a high number of
classes is very rare for a model like this in the wild.

Results: Huge Workflow Efficiency Gains
The findings of this exercise were that the error rate of the team that used the
Data Language AI model to pre-annotate the documents was substantially lower
than the team that worked without the model. 

 * The team using "Data Language AI" demonstrated a 57% drop in error rate.
 * The team using "Data Language AI" demonstrated a 53% increase in annotation
   coverage.

The measurable benefits were clearly efficiency and accuracy and added to the
coverage of entities being substantially larger, it’s clear that using our model
was a great success.

Should we keep Humans in the loop, or not?
We were also analyzing whether there is benefit in keeping humans in the loop
and this result proved surprising. While it was clear that helping humans out in
entity extractions tasks by pre-running the content through a model could
significantly assist their operation, we actually found that the error rate of
the model was actually slightly higher when humans were involved than when the
results were totally automated. In this case, humans reviewing results actually
makes things slightly worse!

There are some cases where perfect results are not mission-critical - e.g.
upstream automated tagging to simplify downstream workflow, where "the machines
do the heavy lifting". In these cases a certain amount of error is acceptable
and a totally automated process will work. 

In other cases, where errors are less acceptable, the confidence and evidence
metadata in our SaaS can be used to allocate "less certain" predictions to SMEs
and expert analysts for decisions and feedback loop optimizations.

Comparison to other Text Analytics NER Approaches
So what does our competition offer to compete with our results in the Named
Entity Recognition (NER) space? 

Well, it’s difficult to compare apples to apples in this scenario, as our
competitors suffer from the same problem outlined above: Complex custom domains
with small training corpora.

This is the exact advantage of our SaaS that these benchmarks emphasize. 

To put these results into the context of standardized testing of NER performance
elsewhere in the wider world and industry, one usually finds that most tests of
F1 approach the value of 1, only where these models work by identifying a small
handful of classes. Have a look - you will likely see "People, Places,
Organizations, Events"... the usual suspects, and this problem itself has been
solved daily for the last 12 years by almost every computer science student.

The Benefits of Data Language Text AI Services
The fact that our model can cope with a far greater number of classes than most
other providers, and still see such impressive results, is a strong
differentiator.

Another advantage of our Data Language AI Services
[https://datalanguage.com/products/datalanguageai] that we offer is that they
work out of the box at scale for your production platforms.

Our Inline Entity Extraction SaaS comes with all these benefits immediately: 

 * Ability to classify text content to 100s of 1,000s of entities in a custom
   domain.
 * A rich API for interacting with the machine learning service metadata.
 * Automatic management of machine learning model ‘fallout’.
 * Self-optimisation of the machine learning models, on the fly.
 * Capability to cope with an extremely high number of entities.
 * The ability to scale up indefinitely.
 * Production-ready engineering, out of the box.

All the engineering is done, and the security & maintenance are on us!

AI Relationship Extraction for Knowledge Graph Platforms
Later in 2022, we will launch a new commercial Knowledge Graph Relationship
Extraction service as part of Text AI Services - early testing has impressive
benchmarks for high-value knowledge organizations. More to come soon.

For more information on Data Language AI Services, check out the links below.

Data Langauge AI [https://datalanguage.com/products/datalanguageai] | Text AI
Services [https://datalanguage.com/lp/text-ai-services]

New Benchmarks from our Named Entity Recognition (NER) Text AI SaaS Product

Silver Oliver

Data Graphs [https://datagraphsa.com] is a product we have designed to lower the
barrier of entry for organizations to launch a knowledge graph platform. We have
shortened the time to launch from months down to hours (in a recent event, we
built a Netflix knowledge graph from scratch with Data Graphs in just 12
minutes).

In Gartner's recent report
[https://www.gartner.com/en/articles/the-4-trends-that-prevail-on-the-gartner-hype-cycle-for-ai-2021] 
on the significance of knowledge graphs, they listed some obstacles that many
companies encounter when trying to implement and maintain them successfully. In
this piece, we'll be explaining how our new knowledge graph product, Data Graphs
handles each of these obstacles.

Gartner's Obstacle 1:

Data Graphs Solution:

Lowering the barrier of entry to knowledge graphs will make it easier for
businesses to see value quickly, and therefore pushing them higher up the
priority list. Specifically, this means speeding up the ability to do proof of
concepts, and do simple but informative demos that allow organizations to
realise early value at low cost and low risk. 

Gartner's Obstacle 2:

Data Graphs Solution:

Data Graphs is an out-of-the-box product. All the engineering and system
integration expertise, that Gartner points out as an obstacle to most companies,
is already taken care of. Data Language has already addressed the main
engineering challenge and initial setup within the product, and engineered the
knowledge graph capability. Integrating with Data Graphs is light, easy, and
based on common web standards and well constructed, robust APIs which saves you
having to wrestle with complicated graph data standards.

Gartner's Obstacle 3:

Data Graphs Solution:

This is a slightly more nuanced obstacle that in summary deals with the fact
that there are many different types of knowledge graph. Any one product,
including Data Graphs, is not a one-size-fits-all solution so companies must be
careful to choose the right knowledge graph for their use-case. 

Data Graphs, for example, is not trying to be an RDF Triple Store, which will
provide the infrastructure for a great knowledge graph for some use-cases, but
less suitable for others. 

In Data Graphs we’ve distilled-down what we’ve seen as the most useful
attributes of a knowledge graph and vastly simplified their use for businesses
that don’t want/don’t have the resources to engineer a full RDF triple store or
a property-label  graph solution.

Over our 10-15 years experience in implementing knowledge graph solutions in the
enterprise, we find that a large percentage of solutions don’t need RDF based
platforms. Instead, we have tried to create the perfect cocktail of enough
semantics to deal with the use cases that most businesses have, but at the same
time keeping it simple enough so as not to get bogged down in complexities that
the majority will never need. Data Graphs does provide RDF interoperability
through its JSON-LD APIs, and Data Graphs does not preclude you from using an
enterprise-scale graph database too. Data Graphs can also be used as a graph
entity editing tool, that can be integrated with an RDF or property graph
database, and this is a soluiton pattern used by some of our customers. 

Gartner's Obstacle 4:

Data Graphs Solution:

To tackle this obstacle we have to go back to the main lesson we always try to
impress on our knowledge graph clients: preparation is key. You have to
understand your data and design it well, solid domain modelling is always the
imperative first step. We often see projects get distracted by the technology
when in reality some of the most important work when creating a knowledge graph
is the preparation, making sure your data is described, structured and
understood clearly. Desgining a quality domain model/ontology for your own
use-cases is critical to unlock the value in sharing linked-data. Data Graphs
allows you to construct your domain model and visulize it within the tool, and
also allows you to export the domain model as an RDF/OWL ontology. Data Graphs
uses linked-data at its heart, to enable interoperability through JSON-LD and
JSON-LD APIs.

Find out more about Data Graphs and our knowledge graph experience below.

Data Graphs [https://datagraphs.com/] | Knowledge Graph Engineering
[https://datalanguage.com/knowledge-graph-engineering]

How Data Graphs addresses Gartner’s obstacles to Knowledge Graphs

After a long history of working together on industry standards, Data Language
has become an official member of the IPTC.

The IPTC is the global standards body for news & media. They provide the
technical foundation for the news ecosystem and involved in setting schemas or
models for the exchange of content in news media. For example, this could be
deciding how the wires are delivered, working on metadata values, controlled
lists of values, and many other functions.

15 Years of Collaboration
Collectively at Data Language we've been working with IPTC on their standards
for the last 15 years, and have been actively involved at many stages. We were
involved in rNews [https://iptc.org/standards/rnews/] which was the schema.org
for news standards, and more recently we've been contributing to their sports
models. Silver Oliver, Data Language's Head of Information Architecture, has
been an active member of the sports model working group, developing those models
to discover what the next generation might be. 

Metadata Innovation in the UK publishing industry
So why did we decide to formalise our involvement with the IPTC? 

Their work is highly valuable in the industry and we want to solidify our
contribution to the innovation, maintenance, support and development of their
standards. We want to help define standards from a Data Language point of view,
and with that be able to encourage the adoption of those standards. As we are
one of the leading agencies supporting metadata efforts in the UK publishing
industry, and given our client expertise, this new relationship with the IPTC is
a great fit.

The opportunity to show our knowledge graph product Data Graphs
[https://datagraphs.com] as an example of good industry standards to other
members of the IPTC is another reason why we wanted to partner even more closely
with them. Having the prestige and quality reputation of the IPTC associated
with Data Language and our push to deliver brilliant knowledge graphs offers
great benefits. We are also always looking at new patterns and tooling in this
space so this membership is an opportunity to validate some of our thinking and
our toolsets against what the IPTC is identifying as the direction that they
think the industry should be going. It's  a really positive way for us to keep
in touch with how the industry itself talks about its needs and what trends are
likely to arise.

Find out more at the links below:

IPTC [https://iptc.org/]| Data Graphs [https://datagraphs.com/] | Knowledge
Graph Engineering [https://datalanguage.com/knowledge-graph-engineering]

Data Language Joins the IPTC

Janes is the latest in a group of high-value, knowledge-driven organisations to
develop their information backbone using rapid innovation in data management
with Data Language.

18th November 2021, London:  Data Language, the data innovation consultancy and
solution provider, today announces it is supporting the interconnection of Janes
120+ years of open-source defence intelligence to create Janes Intara, which
enables the integration and normalisation of disparate data sources, allowing
Janes customers to exploit all intelligence sources in an actionable, shareable
manner.

Data Language has worked as part of a joint team with Janes data and technology
experts to launch a new knowledge graph platform to power the Janes Intara
solution. The platform creates the foundations for ongoing and rapid innovation
of Janes foundational interconnected intelligence.

Ben Conklin, Chief Product Officer at Janes, comments: 

> “Our partnership with Data Language enabled us to augment our development of
Janes Intara, a solution that interconnects millions of assured data points
across Janes foundational intelligence, delivering contextualised insight to our
customers. Working with Data Language on our Knowledge Graph has been imperative
to accelerating the development of our first generation product, further
supporting our integration into customer workflows worldwide.”
Paul Wilton, CEO, Data Language, adds: 

> “The impact that Knowledge Graph and AI technologies can have on high-value
information organisations is significant in terms of both enterprise efficiency
and rapid innovation, enabling Janes to deliver state-of-the-art intelligence
data capability to their customers. Data Language is a global leader in
Knowledge Graph solution delivery, and we're delighted to be working with Janes
to develop their Intara platform.”
Data Language has unique expertise and years of experience working with large
organisations to devise and develop world-class data management and innovative
data products, including Knowledge Graph and AI-based tools.

> Pleased that our data and technology experts worked with Data Language to build
the knowledge graph for Janes Intara! Learn more here: https://t.co/RhAtUiVNdN 
https://t.co/ecWCtOQgVh

— Janes (@JanesINTEL) November 18, 2021
[https://twitter.com/JanesINTEL/status/1461351890243833859?ref_src=twsrc%5Etfw]
About Data Language

Data Language is a UK-based data innovation consultancy and solution provider
that has worked with organisations such as News UK, Syngenta and Cochrane to
deliver digital transformation solutions using knowledge graphs and AI. Learn
more at www.datalanguage.com [http://www.datalanguage.com/].

About Janes

The Janes stamp of trust means supplying our customers with timely, validated,
unbiased and relevant intelligence in a world crowded with increasingly
unreliable information. With an unmatched legacy of more than 120 years, Janes
has adapted, expanded, and developed its unique tradecraft while transitioning
from a traditional military publisher to the leading global agency for
open-source defence intelligence. Janes Intara interconnects millions of assured
data points across Janes foundational intelligence, with the ability to
integrate and contextualise multiple sources, delivering the single source of
truth. Learn more: www.janes.com/whatwedo

To find out more visir Data Language Knowledge Graphs
[https://datalanguage.com/knowledge-graph-engineering] | Janes Intara
[https://www.janes.com/intara-interconnected-intelligence]

Data Language & Janes: A Knowledge Graph Partnership

Last week we presented a talk at the online Knowledge Graph Forum
[https://www.ontotext.com/company/event/knowledge-graph-forum-2021/] hosted by 
Ontotext [https://www.ontotext.com/]. Here’s an overview of our objectives for
this talk, and a summary of some of the things we spoke about. 

Our Aim
We assumed that our audience were people who were either interested or actively
working on knowledge graphs and feeling their way through the problem space.

Our main aim therefore with this presentation was to show the amazing
capabilities of our own knowledge graph product Data Graphs
[https://datagraphs.com/], and how quickly it helps you create a knowledge graph
for a domain.

Common Knowledge Graph Challenges
Before we started with the process of building the knowledge graph, we wanted to
highlight three main challenges that previous clients had raised time and time
again when faced with this task:

1. Semantics in your Knowledge Graph

If you start using an enterprise graph database and RDF, the semantics can get
complicated quickly and it can be easy to get bogged down in a lot more
complexity than you actually need. The core question then is: ‘How much
semantics is enough?’. One of our main objectives for this talk was to show that
we believe Data Graphs has just the right amount of semantics to demonstrate how
we can lower the barrier of entry for knowledge graphs.

2. Technologies

There's a whole suite of technologies which are generally new to most
organisations, they don't have this expertise internally around RDF and
semantics.

3. Insufficient Information Management

Many of the knowledge graph initiatives that we see are technology-driven. Many
companies buy the knowledge graph technology, overlooking the essential
information management side of things. Your knowledge graph is only as good as
your domain model and this must be made up of well curated data. Often companies
that have a knowledge graph will have neglected their information management and
the data quality is poor. We'll return to this later.

The Main Act
We took attendees through a step by step process of creating a knowledge graph
in just 12 minutes with Data Graphs, using the open source data that Netflix has
made available on Kaggle. We started by showing the domain model that we wanted
to replicate and then began the process of building the knowledge graph. 

The first step was to create datasets and concept classes, which is effectively
our project canvas. Then using Data Graphs’ schema builder we can replicate our
domain model. We built out the classes and their relationships and then finally
we have a visual view of our domain model, replicated within Data Graphs. The
next step was to load in the data. Taking individual CSV files prepared from the
Kaggle Netflix data we populated our domain model one concept class at a time,
and let Data Graphs stitch them together with identifiers that we had
predefined. Once all the data was loaded we had a ‘big reveal’, through the Data
Graphs explorer which allows you to navigate and visualise your knowledge graph. 

The audience was able to see all Netflix’ data on movies, shows, actors,
directors, countries etc. presented in an interconnected, structured, and
manageable way.

The Rewards
The rewards can be reaped immediately. Directly within the UI for Data Graphs
you can start giving people access to it to explore, search, query and facet the
data conforming to your domain model. You can also use immediately use the Data
Graphs APIs to build your own applications on top of the data. 

Key Message? Preparation Before Creation
The key message we were trying to impress on our audience was that for knowledge
graphs, good preparation is essential. The speed and relative simplicity of the
setup that we had demonstrated was only possible with solid information
preparation - data that was already clearly structured and well organised.
Pre-defining your domain model is an essential step in establishing the shape of
your data before embarking on a knowledge graph project. Many people jump into
ontologies well too quickly without spending enough time thinking about the
specifics of their data, the core entities in your domain, the key relationships
between them that will enable to you to answer the right questions of your data.

For example, in our case, before this talk we had gone through several questions
that we knew we would need to know the answers to when creating the knowledge
graph: 

 * What will the shape of the data be?
 * What ubiquitous language should we use to describe our model?
 * What identifiers will each of the data points have?
 * What sort of questions will be want our knowledge graph to answer?
 * What will the pros and cons of different approaches be?

And so on...

Making those decisions upfront and doing the work outside of the technology was
essential in allowing that knowledge graph creation to run smoothly. This
preparation is agnostic to the technology solution so once that is aligned, if
you have the right tool, which in this case was Data Graphs, it becomes simple
to put it all together. When that thinking doesn’t happen before the process of
creating a knowledge graph starts, you end up having to work out many imperative
and foundational design decisions within your already-chosen technology and thus
implementation becomes more complex.

For more information of knowledge graphs and how to implement them correctly,
visit the links below.

Data Graphs [https://datagraphs.com/] | Knowledge Graph Engineering
[https://datalanguage.com/knowledge-graph-engineering]

Creating a Knowledge Graph in 12 minutes at Ontotext’s Knowledge Graph Forum

Get hands-on and try the Graph Explorer yourself. Explore the data and interconnections, and help everyone understand your knowledge graph.

Graph Explorer - To demonstrate your Knowledge Graph

The Graph Explorer feature on Data Graphs [https://datagraphs.com/] allows you
to navigate through your knowledge graph visually and interact with your data in
a new light. Being able to present your information visually in the Graph
Explorer helps provide the context, meaning, and interconnection of your data,
an essential asset to your digital transformation.

Demonstrate your Knowledge Graph
A clickable graph user interface enables a visual way of explaining how your
knowledge graph can work for your stakeholders and users. This approach enables
a clickable journey around your business data so that it brings the key
information to life. In our experience this approach generates that "lightbulb
moment" that you have been looking for!

Try it yourself on our Media Demo [https://media-graph.datagraphs.io/media-demo]
Click here [https://media-graph.datagraphs.io/media-demo] to try out our Media
Demo, a read-only version of our product made solely for exploration purposes so
that you can try out the amazing capabilities of the product with no strings
attached. This knowledge graph is populated by thousands of data points from
Netflix, Disney, Hulu, and Amazon and has a plethora of features available to
try out including Search
[https://media-graph.datagraphs.io/media-demo/_all/concepts], Advanced Search
[https://media-graph.datagraphs.io/media-demo/_all/concepts?aq=], Path Analysis
[https://media-graph.datagraphs.io/media-demo/_all/_graph-search], and Domain
Model [https://media-graph.datagraphs.io/media-demo/_domain-model].

Kevin Bacon on Graph Explorer
Click here
[https://media-graph.datagraphs.io/media-demo/people/concepts/urn:media-demo:Person:3Vn1Bxu4W6yzLNL54eB4b5/explore] 
to try out the exact same action being completed in the gif below - exploring
the all media data that links to Kevin Bacon... With Graph Explorer you can
navigate visually through his films, the countries of those films, other actors
he's worked with and much more.

[https://media-graph.datagraphs.io/media-demo/people/concepts/urn:media-demo:Person:3Vn1Bxu4W6yzLNL54eB4b5/explore]
What's the shortest path between Sandra Oh and Ricky Gervais?
Click here
[https://media-graph.datagraphs.io/media-demo/_all/_graph-search/?from=Sandra+Oh%28urn%3Amedia-demo%3APerson%3A6XCMzX4fttIARGWlEPynkN%29&to=Ricky+Gervais%28urn%3Amedia-demo%3APerson%3A2AafOAewdEpIBHxVTKAOHg%29&constraints=0%7Bi%5Byear%3Ar%3A1990%3A2020%5D-%3Ecast-%3E*%7D&view=graph?utm_source=socials&utm_medium=twitter&utm_campaign=shortest-path] 
to complete the action below - finding the shortest path between Sandra Oh and
Ricky Gervais, with path constraints on other actors and shows between 1990 and
2020, and then clicking the Graph Explorer button to visualise the path.

[https://media-graph.datagraphs.io/media-demo/_all/_graph-search/?from=Sandra+Oh%28urn%3Amedia-demo%3APerson%3A6XCMzX4fttIARGWlEPynkN%29&to=Ricky+Gervais%28urn%3Amedia-demo%3APerson%3A2AafOAewdEpIBHxVTKAOHg%29&constraints=0%7Bi%5Byear%3Ar%3A1990%3A2020%5D-%3Ecast-%3E*%7D&view=graph?utm_source=socials&utm_medium=twitter&utm_campaign=shortest-path]
And this entire knowledge graph can be created in just 13 minutes in Data
Graphs... Watch this video to find out how.

Find out more about Data Graphs here [https://datagraphs.com/].

Graph Explorer - Demonstrate your Knowledge Graph

Data Language announces promising results from this Innovate UK -funded AI
project, building on their long-running technology partnership with Cochrane. 

Innovate UK Project
Title: “AI Classification of COVID-19 studies to ensure clinical guidance and
healthcare policy is informed by current meta-analysis of all available evidence
”

This Innovate UK grant-funded project ran from August 2020 to April 2021.

Data Language has worked with Cochrane since 2014, designing and delivering a
microservice-based linked data platform that encodes an authoritative knowledge
graph of healthcare clinical evidence. This has included work to refine, iterate
and scale the platform whilst creating a suite of applications to publish
evidence in healthcare as structured data. The platform enabled Cochrane to
rapidly deploy their Covid 19 Study Register in response to the coronavirus
pandemic in 3 weeks last March.

This Innovate UK -funded AI Project has delivered a new capability in Data
Language’s Text AI services to enable automated creation (“clinical coding”) of
PICO Graphs, using values from Cochrane vocabularies and standard medical
vocabularies assigned to Population, Intervention, Comparison and Outcome
structures for clinical analyses.

The specific outcomes from the initial phase of project work can be summarised
as:

 1. We created a working Machine Learning approach.
 2. We have a clear set of next steps.

Following the successful conclusion of the project work to develop the AI
components, the next steps are to focus on key optimizations to move it into
productionization and commercialization. Timeframes are yet TBC.

Results from our Text AI Services Covid-19 Project with Cochrane

In my last blog post, I introduced our new SaaS product Data Graphs
[https://datalanguage.com/blog/data-graphs-fully-managed-knowledge-graphs] - a
cloud hosted Knowledge Graphs as a Service platform - effectively lowering the
barriers to entry into knowledge graph adoption. Data Graphs
[https://datagraphs.com] not only makes it trivially easy to spin up a knowledge
graph for a domain or project, but provides a very intuitive user interface for
curating and managing your data within, along with search, discovery and
integration using a richly featured API.

Since then we have been busy rolling out new features, the most striking of
which are the new visualisations. Data Graphs now lets you automatically
visualize your domain model, and lets you navigate your graph data visually too.

The Domain Model
This visualization is a superb way to verify the domain (or ontology) model you
have created in Data Graphs accurately reflects the true nature of your domain
and the semantic relationships between classes of concepts are as you intended.
This visualisation lets you see the structure of your schema, including all
primitive data properties, relationship properties, and any subclasses you have
defined. 

A typical pattern is to draw your domain model first in some external tool, or
on paper or whiteboard, then construct the schema in Data Graphs. Now you can
visually verify if the models align.

The domain model is visualized using a force-directed graph. If the automatic
render is not perfect,  you can use the controls to adjust the layout. Each node
can be pinned and moved simply by dragging it to the position you prefer.  You
can pin all nodes using one of the control bar buttons on the left of the
diagram. Subclass relationships can be toggle on / off, and you can also
remove/prune classes using the sidebar that lists all the concept types in your
project. When happy with the layout, you can save it, so next time you view it,
it will retain the visual changes you have made.

Graph Explorer
You can explore your knowledge graph visually starting from any concept in your
datasets.

There is an option in the concept view screen to “explore graph”. Clicking this
will open the data visualization using the concept selected as the starting
node.

The graph opens and you then have the ability to explore the graph. Clicking on
a node in the graph opens all its inbound and outbound relationships. It also
renders the data about the node selected in the sidebar on the right.

If a concept has a preview image defined, these will be rendered as the
canonical image for each node.

To navigate at a more granular level, right clicking on a node will open a
context menu where you have the option to open or close specific relationships,
or prune the node itself from the graph visualization.

Like in the domain model, holding and dragging a node pins it the place you move
it to.

I hope these are helpful. We have plans to continually improve these
visualization features over the coming months.

Knowledge Graph Visualization in Data Graphs

Our knowledge graph product Data Graphs [https://datagraphs.com/] has a brand
new feature. You can now view your entire knowledge graph in the 'Domain Model'
view. This allows you to see your data presented as a spider diagram and explore
it interactively by moving each data point to wherever you like. 

Being able to present your data visually is an essential facet in your company's
digitisation journey and this new Data Graphs feature makes this simple.

Find out more about Data Graphs and Data Visualisation here
[https://datagraphs.com/].

Domain Modelling

New Data Graphs Feature: 'Domain Model' View

Data Graphs is the latest Data Language baby. Data Graphs
[https://datagraphs.com] was born out of need, a need for really easy structured
data management. Our thoughts on this evolved to eventually produce a SaaS
product that allows a team to curate a knowledge graph for any domain, from
simple flat concept collections to complex structured domain data. This is all
wrapped up in an extremely intuitive user experience, combined with a fully
comprehensive API and web-hooks to give you extremely efficient integration with
applications and business systems. You can check it out here : 
https://datagraphs.com

What does it do ?
Put simply it provides a dead simple way to create knowledge graph data models,
populate them with data, and interrogate (query) and integrate that data with
your applications using a flexible and easy API.  In essense, it is Knowledege
Graphs as a Service (KGaaS).

Building your own knowledge graph platform can take months, requites highly
specialised expertise and lot of $$, and that is before you start populating it
with data. Even when you build your own KG platform, the information management
tools dont come for free, and are typically sorely lacking in utlity.

We have short-circuited that complex engineering process, and done all the hard
yards for you. Futhermore we have added a stunning information management user
experience. With Data Graphs you can be up and running in minutes, start
creating your graph data models, and managing your data.

You don’t have to worry about scaling, engineering, backups, it’s all handled
for you. But best of all is the super-intuitive user interface for managing,
searching, and maintaining data over time. The API lets you search, query,
filter, and facet on your data - slice and dice it. The API is rocket fast and
easy to use, and also geospatially aware.

We think Data Graphs addresses a lot of use-cases, from straight-forward data
catalogs or vocabulary management, to easy to setup and maintain knowledge
graphs. You could use it for managing structured tagging data for a publishing
system, integrating it with your CMS, or even for maintaining time series
structured data powering charts in WordPress for data journalism. It is ideal as
a central metadata or reference data repository for your enterprise business
systems. You could even use it for maintaining a nicely categorised and faceted
product catalog to integrate with your e-commerce store.

Schemas, Concepts, and Datasets
The Data Graphs model is based around three fundamental ideas - 

Datasets - these are collections of Concepts - organised into groupings of your
choice. Datasets can hold concepts of different types, or concepts all of the
same type. Datasets can also be public or private -

 * a public dataset means the data it holds is accessible via API using only an
   API key, so it could be directly accessible via anyone on the internet, who
   has access to your API key.
 * a private dataset means the data it holds can only be accessed using Open ID
   / oAuth security credentials

Schemas - this is your data model, definitions of the things (or concepts) in
your domain, their properties, and how they are linked together, the
relationships between the things. Data Graphs has an intuitive user interface
that lets you quickly design a data structure that reflects your own domain
model.  Each Concept type in your domain has its own schema definition where you
define the properties of a concept and the data types of those properties
(dates, integers, text etc). For example, a simple schema of a Person might have
a givenName (text), familyName (text), and a dateOfBirth (date), and a headShot 
(image).  Rather than explain it in depth here, if you need to know more, we
have made a short video
[https://datagraphs.com/demos/how-to-setup-your-data-sets] that shows how
quickly you can create a schema for your domain model.

Concepts - the data itself. Concepts are the data instances that you load into
Data Graphs conforming to your schemas. You can create and edit concepts via the
user interface, bulk import them from for example CSV files or external sources,
or load them via the Data Graphs UI. Once created, you can search , discover,
and maintain the data via the UI.

SaaSification
Data Graphs is fully SaaSified - you can sign up online [https://datagraphs.com]
, frictionless, and get started immediately with a free trial. You can manage
your own users, roles, your own backups, your own API keys and oAuth
credentials. 

The User Experience
Delivering a great data management experience was our primary goal when creating
Data Graphs. In almost all the knowledge graph and information management
projects we have worked on, data management and curation tooling was always an
area that was a challenge. We set out to build something extremely intuitive to
use, with a UI that would be self-aware of the data types, the relationship
semantics, and make it super easy for users to explore, find, and navigate data.

Dog Food
We are great fans of the “Eat your own dog food” approach to product
development. If our APIs are not good enough for ourselves to build an
application upon, how can we expect them to be good enough for others? With this
in mind, all the features in the Data Graphs Application are built upon the same
APIs exposed to the Data Graphs client, and with that in mind, let’s talk about
integration..

Integration
There is no point having a fabulous user experience for ingesting and managing
your data if you cannot integrate it. As with most SaaS products Data Graphs
comes with an API. A set of REST endpoints for searching, filtering, querying,
faceting, retrieving and creating data. It is fast, it is flexible.

With the API you can access data only in specified datasets, or you can federate
a query across all your data.

If used with only your API key, you access only the data in public dataset. If
used with your oAuth credentials, you can access both public or private
datasets.

Web Hooks - Triggered
The API gives us pull integration, web hooks provide us with push. You can
configure web hooks for Concept create, update, delete, and also candidate
(concept) approval. You define optional endpoints (typically in your  own
platform) that are invoked whenever a specified Data Graphs event is triggered.
For example you could trigger some downstream system when a Concept of a
specific type is created or updated. For example if an Organization concept is
updated, trigger some workflow in an existing business system. The web-hook
events supply the entire concept data in its payload, which means you could use
this to push data into another database or application.

Querying data - The Data Graphs Filter Expression Language
As well as features such as sort and facet (by concept property) the API lets
you query your data using a flexible filter expression language (often called
NQL).  In simple terms, this lets us filter data like this 
filter=property1:value1,property2:value2

A filter expression is a string which provides a (Concept)  property, operator
and value in the form where property is a JSON path representing the field to
filter on (using JSON dot notation) and operator is optional , and tells the API
how to compare values. value is the value to match against.

Operators can be -(not), >, >=,<<=, and also a test if a value is in a group of
values [value,value,...]

Boolean expressions are also supported including parentheses for grouping
expressions together, and overriding operator precedence. 

Full text search is supported using the traditional q parameter. We index all
the text fields in concepts, and boost the canonical labels.

Combining all these together lets us build extremely high utility data queries,
and very flexible integrations.

You can query within a specified dataset, or you can federate across all your
data, and using the filter expression language it means you can ask extremely
useful questions of your data.

The API is fast - you can expect all requests to be returned in less than a few
hundred milliseconds.

Geospatial
We also support geospatial data. We have primitive datatypes for latitude, 
longitude and geohash - so both our API and UI can be geospatially aware for
concepts with location information. As well as being able to explore geospatial
data in the UI on maps, you can also query geospatially using point-radius,
bounding-box, and bounding -polygon queries. Any geospatial query can be
combined with other API filters and full text search.

This means you can ask questions of your data such as “Find me all the concepts
within 50km of New York, classified by X, with property P greater than Y, and
the word “hello” in their label”. Nice.

For example, given a geospatial data set of Airports with relationship to
airlines , get all airports within a 50km radius of London, serviced by EasyJet,
we might use an API request like :

/airports?filter=_near((51.51, -0.12), 50km),airlines:urn:demo:airlines:easyjet

Client Library
For developers we have published a Javascript client library for Data Graphs to
make integration even easier. The client library is particularly useful for
consuming the API using OpenID credentials, as the machine-machine OpenID
authorization flow is all taken care of.  The client library documentation is
published in NPM here : 
https://www.npmjs.com/package/@datalanguage/datagraphs-client

Sub-classing, Inheritance, and Transitive Inference
While Data Graphs is not a full blown graph database, it is a fully managed
structured data solution that can be used to build a high-utility highly
performant knowledge graph for your organization without having to manage any
infrastructure.  There are some key features in knowledge graphs that add
significant value. 

Subclassing and class inheritance is one of those features. In our (extensive)
experience of working with graph databases, inference is a feature that is
widely misunderstood. The set of potential inference rules available in the full
RDF and OWL specification is considerable. However in most practical
implementations of knowledge graphs (outside of academia), there are just one or
two core inferences that add significant value in most business applications.
Subclassing and transitivity is top of that list. Data Graphs has full support
for this.

When you create a new Concept schema definition, you have the option of
declaring a parent type (class) and inheriting its properties. 

Create a Concept with a parent typeOnce you have created your concept schemas,
we have a neat class structure management tool like this :

Concept relationships (and the User Experience) then become class structure
aware, with transitive inference.  When a concept property defines a
relationship to another concept type/class the UI allows you to select concept
instances of that type and all its subclasses.

Transitive relationship propertiesAPI queries also obey the usual semantics of
transitive inference across a class hierarchy. 

Linked Data and Graph Databases
Data Graphs has support for JSON-LD. API responses can be requested with an 
application/ld+json mime-type such that the data is returned in JSON-LD with a
context dynamically constructed from your schema.

This lets you transform data directly to RDF for loading into your own graph
database, or another RDF compliant system. 

So while functioning capably as a standlone fully managed knowledge graph, Data
Graphs could thus also be used as a tool-suite for editing data in a downstream
enterprise graph database / triple store. Using the web-hooks, it is fairly
trivial to build a service that updates entities in a graph database when the
web-hook is triggered from a concept create/update operation in Data Graphs.

The Road Ahead
Data Graphs already has a plethora of great features
[https://datagraphs.com/features], but we have some great new ones lined up in
our road map. Not least is data and model visualisation.

Building on the class structure visualisation we plan to render detailed domain
model / ontology diagrams dynamically from your schema. We also plan a dynamic,
navigable graph visualisation of the instance data itself. On the back-end, we
plan to offer regional data storage options.

We think we have produced something really special with Data Graphs, something
that really delivers utility and value for information management in your
organisation - but don’t take my word for it, go ahead try it out for free
[https://datagraphs.com].

Data Graphs - Fully Managed Knowledge Graphs

Data Language is proud to be featured in today's 'AI for Business' supplement in
The Times (10th December 2020)

Created by Raconteur, this supplement covers a range of AI solutions across a
spectrum of enterprise use cases. In it we offer our view on how to implement AI
successfully: 

 1. You need a consistent source of reliable data to feed the AI systems.
 2. You need leadership within the organisation who understand the need to make
    faster, data-driven decisions.

The Times : "AI for Business" - 10th December 2020.Read more:
 1. Raconteur AI Section
    [https://www.raconteur.net/topic/technology/artificial-intelligence/]
 2. Data Language Explainable AI Platform
    [https://datalanguage.com/products/datalanguageai/explainable-ai-platform]
 3. Data Language Text AI Services
    [https://datalanguage.com/products/datalanguageai/text-ai-services]

Data Language featured in The Times' AI for Business Supplement

Data Graphs [https://datagraphs.com/] is a knowledge graph SaaS product by Data
Language that provides rapid knowledge graph setup and API provisions. It
enables you to set-up, manage and build apps on your knowledge graph in minutes;
an essential tool in Digital Transformation
[https://datalanguage.com/about/digital-transformation].

What is Data Graphs?

Data Graphs is our brand new a structured data solution that makes creating, managing and organising your data easy. Start your free trial now.

Launching Data Graphs | Our new knowledge graph product

We are very proud to announce the launch of our latest SaaS product - Data
Graphs! We wanted to create a data solution that could provide really easy
structured data management and we believe we've done that with this new
knowledge graph product.

As a fully managed SaaS solution, Data Graphs [https://datagraphs.com/] combines
an intuitive user experience with flexible integration. It allows a team to
curate a knowledge graph for any domain, from simple flat concept collections to
complex structured domain data. This is all wrapped up in an extremely intuitive
user experience, combined with a fully comprehensive API and web-hooks to give
you extremely efficient integration with applications and business systems. 

After working for years with many companies on structured data solutions, we are
thrilled to have now produced a product that can provide businesses with the
structured data they need to embark on their digitisation journey.

Here's how you can explore Data Graphs [https://datagraphs.com/]:

Read more [https://datagraphs.com/] | Sign up for a free trial
[https://datagraphs.com/] | Book a 15-minute discovery call
[https://calendly.com/datalanguage/data-graphs-intro-call]

Launching Data Graphs

Tolga Ergin

Datagraphs Logo on Dark

Data Graphs provides effortless knowledge graphs. Set up, manage and build apps on your knowledge graph in one easy-to-use product. Ideal for MVP launches.

Data Graphs - Effortless Knowledge Graphs

Data Graphs provides you with ‘effortless knowledge graphs’. Whether you are in
publishing, agriculture, finance, law, government or any other sector, this is a
foundational knowledge graph tool for your digital transformation.

You can find out more, and sign up for a free trial of Data Graphs at 
https://datagraphs.com

Setup is Easy
Setup takes minutes, and this easy-to-use SaaS product can be shared with your
team to easily integrate with whichever systems, apps and services you use. 

Intuitive User Interface
As a web application, you can import, create, organize, edit, or consolidate
your data effortlessly with its intuitive user experience.

Powerful API
Connect your data via its powerful and fast API, define your business critical
data and enable your team to explore new opportunities, all managed through the
elegant user interface.

The best way to deploy a Knowledge Graph rapidly
From our years of experience building knowledge graphs for global brands, Data
Graphs enables you to get your knowledge graphs launched, build apps, and
connect services in minutes and hours, rather than days and months!

Data Graphs Strapline

We are developing our Text AI Media Classification Products to help Cochrane automatically classify Clinical Studies onto Snomed-CT and PICO structured data

AI Classification of Clinical Studies with Text AI to fight Covid-19

We've just received the green light on this public-funded innovation project,
bringing two areas of significant Data Language expertise into play to help
combat Covid-19.

The mission combines two of our favourite things:
1. Clinical Evidence Knowledge Graphs
We've been working with Cochrane on their knowledge graph and data strategy
[https://datalanguage.com/blog/how-knowledge-graph-technology-is-helping-cochrane-respond-to-covid-19] 
since we started the project with them in 2014. As part of this, we recently
helped them launch their Covid-19 Study Register
[https://datalanguage.com/blog/covid-19-rapid-response-technical-delivery-at-cochrane]
, which summarises the current state of Covid-19 clinical evidence from around
the world that has been released into the public domain via authoritative trial
registries and journal databases.

2. Our Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] for
automated text classification
Over that time, we have also cemented our position as industry leaders in
machine learning text classification and analytics, and have developed
particular expertise in high volatility domains (e.g. academic research,
breaking news, R&D) and complex ML engineering challenges. This led us to
creating our Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services].
[https://datalanguage.com/tagmatic-ai-document-classification]

Mission: to ensure Covid-19 clinical decision making is informed by the latest,
most comprehensive reliable evidence possible.
With the help of the UKRI [https://www.ukri.org/], Digital Catapult's Machine
Intelligence Garage
[https://www.digicatapult.org.uk/for-startups/acceleration-programmes/machine-intelligence-garage]
, and Innovate UK [https://www.gov.uk/government/organisations/innovate-uk], we
are now bringing our Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] into play
with our subject matter expert partner Cochrane to help the fight against
Covid-19.

We will be launching this new HealthTech AI beta before December. 

Get in touch if you want to know more.

News, features and updates about our Data Language AI Products. Find out how our AI Products solve common specialist and scaling problems.

News, features and updates about our Data Language AI Products. Find out how our AI Products solve common digital transformation challenges.

Data Language AI - News and AI Product Updates

Data Language AI

Automated Classification of Healthcare Evidence

Datagraphs Logo

Data Graphs website

We are proud to announce the new member of our Text AI Services
[https://datalanguage.ghost.io/products/datalanguageai/text-ai-services] family:
Inline Entity Extraction. This new AI NLP service was created for high-value
knowledge organizations who want to accurately classify their text media at a
granular, inline-level.

The Problem: Automated Tagging in Custom Domains
We created this service to solve the problems that other Named Entity
Recognition (NER) NLP SaaS - and "build it yourself" approaches - could not
solve.

Our customers are high-value media and knowledge organizations. A huge portion
of their business asset value is locked in their unstructured media assets,
which they must leverage to add value to their subscription, DaaS, targeted
advertising, and other business models. This unstructured content must be
connected to their custom business domain via knowledge graphs and other
structured data systems:-

 * Complexity: To liberate this value, accurate inline text classification of 
   100s of 1,000s of entities is required to enable structured connection with
   bespoke information architecture models and/or knowledge graphs.
 * Custom Domain: Generic off-the-shelf NLP SaaS solutions do not recognize
   these custom entities, so a mapping exercise is required, and is often a too
   low resolution to provide value.
 * Rich Metadata: Other NLP solutions do not provide the rich, domain
   model-aware metadata, because they are not trained for the custom domain.
 * Small Training Sets: Other custom NLP solutions require larger training sets
   and golden corpora that cover the entire target entity set.
 * Cost of Build: "Build it yourself" becomes very expensive, with a large data
   scientist and engineering overheads, while organizations find themselves
   re-solving the same challenges that we have already solved.
 * Cost of Maintenance: "Manage it yourself" has a surprisingly large data
   science and engineering overhead, to overcome production challenges that we
   have already solved.

Our Solution: Text AI Services Inline Entity Extraction
Our Inline Entity Extraction SaaS handles a far greater number of classes than
other NER NLP solutions, while delivering impressive performance results. It
also works out of the box at scale for your production platforms.

Our Inline Entity Extraction SaaS comes with all these benefits immediately: 

 * Custom Domains at Scale: It classifies text content to 100s of 1,000s of
   entities in a custom domain.
 * Powerful API: It provides a rich API for interacting with the machine
   learning service metadata.
 * Does its own ML housekeeping: It automatically manages machine learning model
   ‘fallout’.
 * Self-Optimizing: It self-optimizes its machine learning models, on the fly.
 * Scalable: It is able to scale up indefinitely.
 * Production-Ready: It is Production-ready, out of the box.

All the engineering is done, and the security & maintenance are on us!

For more information on Data Language AI Services, check out the links below.

Data Langauge AI [https://datalanguage.com/products/datalanguageai] | Text AI
Services [https://datalanguage.com/lp/text-ai-services]

Product Launch: Inline Entity Extraction SaaS - AI Custom Domain Auto-Tagging

Analysis of 1 month of video stats from Jamie Oliver Christmas videos shows 10x CTR on Data Moments, and extremely high engagement rates.

Data Moments shows benchmark-beating stats on Jamie Oliver videos

Data Moments is a new type of video experience, where users can interact with
moments in video. For Jamie Oliver, this means getting more from each step in a
recipe video, and highlighting products that are used in the video so that
viewers can buy them directly.

This image shows a "Making thyme flavoured oil" video moment, that had an
interaction rate of 62%.From a data sample of 1 month of Data Moments on Jamie Oliver video
[https://datalanguage.com/news/jamie-oliver-christmas-videos-enhanced-with-luminery]
, here are some highlights:

Video Moment Engagement
 * Challenge: Video experiences haven't changed for years. Can we get a new,
   user-centred experience that dramatically improves how viewers use Jamie
   Oliver videos?
 * Solution: Identify steps in the recipe, and the techniques, products and
   ingredients in those steps. Activate the steps as structured "How To" moments
   using Luminery. Add important products so that users can find them directly.
 * Results: 92 new "moment interactions" per 100 video views. 9.3% Interaction
   Rate with HowTo steps (compared to the current ZERO).

Video Marketing Conversion Performance
 * Challenge: Jamie Oliver produces a range of products, and creates incredible
   recipe videos. How do we drive better product sales from these videos?
 * Solution: Identify products in the recipe and activate the moments to help
   users directly reach and buy the products at the right moments using Data
   Moments (saving huge conversion fall-off due to searching and uncertainty
   barriers).
 * Results: 5.4% CTR (clicks as % of video views) generated by the "product
   moments" (compared to 0.5% of standard pre-roll Ads). 9.6% conversion rate at
   the Ecommerce vendor destination for Jamie Oliver products.

What does this tell us?
This demonstrates, again, the benefit of using structured video moments to
activate new value in Video. We've demonstrated new engagement and revenue
-driving opportunities, and a fresh model for video publishing.

News, features and updates about our video moment product Data Moments. Find out how this innovative product harnesses structured data in video. Brought to you from the team that was foundational in the earliest large scale linked data projects in the UK.

Read news, features and updates about our video moment product Data Moments. Find out how this innovative product harnesses structured data in video.

Data Moments - News and Video Product Updates

Data Moments

Data Moments - Get the latest on this Video Data Moment Product

Data Moment video Stats from Jamie Oliver Christmas videos

Who is Cochrane?
Cochrane [https://www.cochrane.org/] is the global not-for-profit organisation
whose core mission is to put evidence at the heart of health decision-making all
over the world. Over 90% of WHO guidelines are informed by Cochrane’s systematic
reviews.

What is the Covid-19 Study Register?
The Register is a precisely scoped, authoritative source of the latest clinical
evidence from around the world concerning the Covid-19 pandemic, that is being
used to inform point-of-care clinical decision making and evolving healthcare
policy internationally.

Healthcare professionals can use it to locate all relevant, reliable clinical
evidence about specific healthcare questions from the latest Randomised
Controlled Trial (RCT), observational, modelling and other studies to support
the fight against Covid-19.

This crucial, trusted resource is essential at a time when the evidence base is
developing rapidly, as healthcare professionals require structured and reliable
up-to-date information.

Partners with Cochrane since 2014
We first worked with Cochrane in 2014 on a digital transformation project,
designing and delivering a radical new platform architecture centred around
linked-data and knowledge graph technologies. Since then we have worked to
refine, iterate and scale their platform to create a suite of applications to
publish evidence in healthcare using structured data.

Solid data architecture makes rapid innovation possible
This Covid-19 Studies Register is the latest example of how Cochrane is able to
innovate rapidly on the scalable knowledge graph platform architecture delivered
by Data Language. The structure and flexibility means that new use cases can be
delivered rapidly and efficiently.

See the Covid-19 Study Register here [https://covid-19.cochrane.org/].

Find out more
 * Cochrane's Covid-19 Resources and News
   [https://www.cochrane.org/coronavirus-covid-19-cochrane-resources-and-news] 
   (page updated regularly).
 * More about Cochrane and the rapid delivery of the Covid-19 Study Register
   project
   [https://datalanguage.com/blog/covid-19-rapid-response-technical-delivery-at-cochrane]
   .
 * Our work with Cochrane since 2014 - Information Architecture, PICO Ontology
   and more
   [https://datalanguage.com/blog/how-knowledge-graph-technology-is-helping-cochrane-respond-to-covid-19]
   .
 * More about our Knowledge Graph Engineering
   [https://datalanguage.com/knowledge-graph-engineering].

Launching the Cochrane Covid-19 Study Register

Our pitch included using our Explainable AI Platform to provide London's analysts with explainable AI (XAI) decision support.

Explainable AI Platform at London Civic Innovation Challenge

We've just been at City Hall for London's Civic Innovation Challenge Awards
Ceremony. Sadly we didn't win on this occasion, but we were proud to be in the
finalists for the Counter Violent Extremism Social Tech Challenge.

Explainable AI Social Tech to help Londoners
Our pitch included using our Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] to
provide London's analysts with explainable AI (XAI) decision support.

Explainable AI decision support - in this case for violent extremism specialists
Thanks to the GLA Team and Social Tech Trust!
The finalists with the GLA team and Social Tech Trust.

News and Product Updates about our Explainable AI Platform (XAI)

Explainable AI (XAI) means that the process, reasoning and decisions made by the AI are transparent and clear to the users and teams involved. Even before this became a trending technical jargon term, we were delivering Explainable AI products and services.

Updates and news about our Explainable AI Platform (XAI). We are specialised in moving new AI services to production rapidly, avoiding pain points in XAI

News and Product Updates about our Explainable AI Platform (XAI)

Explainable AI Platform

Proud Finalists in London's 2020 Civic Innovation Challenge

Data Language chosen for the Scaleup Programme.
We are delighted to announce that we have been chosen for the Innovate UK and
EEN Scaleup Programme. This programme supports a select group of companies on a
journey to exponential growth, providing them with countless opportunities with
bespoke, fully-funded one-to-one support.

We are immensely proud of this achievement as admission onto the Scaleup
Programme is a highly selective process. For the first cohort 2,800 companies
made up the initial long list of applications with only 30 selected, so to be
chosen as one of the successful companies in the new cohort has given us even
greater confidence in our ambition.

We can’t wait to get to work with the Scaleup team to start bursting through
barriers to growth and taking Data Language to its full potential.

Scaling Up

The Jamie Oliver team is using Data Moments - our structured data video moment SaaS - to create new "How To", promotion and Product promotion video moments.

Jamie Oliver uses Data Moments for Christmas Videos

We're really pleased to announce the launch of the Jamie Oliver Christmas "How
To" video feature, powered by Data Moments
[https://datalanguage.com/products/datamoments], our SaaS structured data video
moments platform.

Jamie Oliver is using our platform to activate and enhance the valuable moments
within his Christmas videos.

Data Moments contextual commerce "Product Moments"
A product promotion "moment" on Jamie Oliver's Christmas Video Feature.Data
Moments "How To" video moments.
The Jamie Oliver team are interested in whether users used videos "to cook
along" - we are using our HowTo Video Moments to help users navigate between
important steps.

Jamie Oliver uses Data Moments

In September 2019, we enhanced Rugby World Cup video using our Data Moments product to activate their video. 50% of their key moments were in Google top 10

Video Micro-Moment Performance in Rugby World Cup 2019

Data Moments [https://datalanguage.com/products/datamoments] is a new type of
video experience, where users can interact with moments in video. These are
unique opportunities we call Video Micro-Moments, where we can use Data Moments
to meet user needs
[https://datalanguage.com/video-micro-moments-meet-user-needs-at-their-strongest] 
in the actual moment that they occur.

For the Rugby World Cup in 2019, this means enabling rugby fans to discover,
navigate and enjoy the great sporting video moments with ease.

Video Moment Reach
 * Challenge: J SPORTS have unique sporting moments within their videos, that
   cannot be discovered by search engines or users with normal video publishing.
 * Solution: Markup the key sporting events, teams and stadiums with Data
   Moments [https://datalanguage.com/products/datamoments] structured data video
   key moments that are optimised for SERPs (Video SEO).
 * Results: 45% of video moments created for J SPORTS Rugby World Cup ranked in
   the first page of Google SERPs (Search Engine Results Pages). This created a
   completely new and significant SEO surface area, growing reach by 37%, and
   creating Google Discover opportunities too (with 4.2% CTR), 

Video Marketing Conversion Performance
 * Challenge: J SPORTS run an E-Commerce store selling sporting goods, plus a
    Sports SVOD product, and want to increase the traffic from video highlights
   through to conversions.
 * Solution: Create "Buy in the moment" contextual commerce moments, linking
   directly to products at key video moments, meeting the user's "I want to buy"
   video micro-moment need.
 * Results: 1.1% CTR - significantly beating the 0.5% standard for conventional
   video advertising.

Read the latest news and updates about Data Moments video key moments
[https://datalanguage.com/about/datamoments].

Data Moments Stats - Video Key Moments in the Rugby World Cup

J SPORTS is using Data Moments - our video key moment SaaS product - to activate video key sporting moments and brand moments for the Rugby World Cup.

We're really pleased to announce that J SPORTS is using Data Moments
[https://datalanguage.com/products/datamoments] (our SaaS Video Enhancement
Platform) to enhance video moments for their Rugby World Cup highlights.

We have been working on this project with our innovation partners at EAI
[https://www.entertainmentai.co.uk/] to help J SPORTS use our innovative video
moment enhancement technology for SVOD, E-commerce and Sport Event micro-moment
promotions.

Why is J SPORTS using Luminery?
J SPORTS were particularly excited about the Data Moments
[https://datalanguage.com/products/datamoments] activation of video moments that
effectively meet user needs exactly when they arise. Google's marketing
evangelists call these moments "Micro moments
[https://www.thinkwithgoogle.com/marketing-resources/micro-moments/micro-moments-understand-new-consumer-behavior/]
".

Sporting Event Micro-Moments - "I want to know"
With Data Moments [https://datalanguage.com/products/datamoments], each sporting
moment - tries, conversions, line-outs, penalties etc - has a new opportunity
listed on SERPS, shareable, and brand activated.E-Commerce Micro-Moments - "I
want to buy"
With Data Moments [https://datalanguage.com/products/datamoments], each brand
activated moment can feature Contextual Commerce activated promotions for
products, when they are actually relevant to the user!J SPORTS Enhanced Video
Homepage
At: video.jsports.co.jp [https://video.jsports.co.jp] - the videos are only
available for users in Japan, due to RWC rights restrictions.

The J SPORTS With Data Moments [https://datalanguage.com/products/datamoments] 
Video HomepageGet in touch [https://datalanguage.com/contact-us] if you want to find out more.

Data Moments activates video moments for The Rugby World Cup

Tom Fox from the "Hi Growth Unit" at KPMG awarded us with the KPMG Prize at
Venturefest in Oxford for our contextual video moment innovation pitch.

(Update: Luminery has rebranded as "Data Moments
[https://datalanguage.com/products/datamoments]")

Selected from an impressive spread of startups 
The pitching participants.Luminery Demo
The team demonstrate Luminery enhanced video moments at VenturefestBranding
Update @ 2020
"Luminery" has been re-branded as "Data Moments
[https://datalanguage.com/products/datamoments]".

News and Product updates about Luminery, our state-of-the-art Video Micro-Moment SaaS product. Luminery enables Publishers, Brands and Creators to leverage the value of video micro-moments for increased reach, revenue and engagement.

Read News and Product updates about Luminery - the Video Micro-moment SaaS Platform that increases video reach, engagement, revenue and social capabilities.

Luminery - The Video Micro-Moment Platform - News & Product Updates

Luminery

Luminery wins KPMG Prize at VentureFest

Data Language has been accepted onto the Digital Catapult Machine Intelligence
Garage [https://www.digicatapult.org.uk/projects/machine-intelligence-garage] 
program.

This is fantastic news for us. Through MI Garage, Digital Catapult is going to
provide us with plenty of high performance infrastructure and technical support
to power our machine learning and AI product R&D throughout 2019.

This will allow us to rapidly advance innovation and development across all our
products, running our ML models at scale, and on GPU kit. It will enable us to
expand our Tagmatic [https://datalanguage.com/tagmatic] ML product suite for
automated content classification; our deep learning image classification
platform, SCOPA; and help with automation on our Contextual Commerce platform, 
Luminery
[https://datalanguage.ghost.io/2019/02/06/digital-catapult-machine-intelligence-garage/luminery.com]
.

2019 is going to be a great year for Data Language!

AI is a core discipline at Data Language. We have an expert team skilled in machine learning and deep learning, and importantly we also have the engineering and deployment skills to deliver AI at scale for your production systems.

AI is a core discipline at Data Language: Our expert team is skilled in machine learning and the engineering skills needed to deliver AI at scale.

Artificial Intelligence delivered at scale - our core discipline.

Artificial Intelligence

Digital Catapult - Machine Intelligence Garage

We had some more great news. Innovate UK
[https://www.gov.uk/government/organisations/innovate-uk] has awarded Data
Language a new innovation grant for further funding experimental development and
proving the market of our Luminery [https://luminery.com] platform to deliver
crowd and machine sourced contextual commerce- and brand enhanced- video
moments. 

Our aims are to drastically improve the advertising revenue publishers can make
from video, to improve discovery and reach at a video moment -level, and also to
provide a fair reward mechanism for crowd workers.

You can see a working prototype [https://luminery.com/food-example] here
demonstrating how our contextual commerce works.

Innovate UK

Innovate UK funding for Luminery (Data Moments)

As part of our Text AI Services SaaS product range
[https://datalanguage.ghost.io/products/datalanguageai/text-ai-services], we are
proud to announce the launch of our cutting-edge Text Classification service.

The Problem: Automated Tagging in Custom Domains
We created this service to solve the problems that other NLP Text Classification
products could not solve.

Our customers are high-value media organizations. A huge portion of their
business asset value is locked in their unstructured text content:-

 * Aboutness: To liberate this value, an accurate classification of content
   "Aboutness" is required to classify content to the organization's bespoke
   information architecture models and/or knowledge graphs.
 * Custom Domain: Generic off-the-shelf NLP SaaS solutions do not recognize
   these custom aboutness classifications.
 * Rich Metadata: Other NLP solutions do not provide the rich metadata, because
   they are not trained for the custom domain.
 * Rapid Learning: Other NLP solutions can't learn on the fly, and require
   expensive and time-consuming training cycles to learn new concepts.
 * Cost of Build: "Build it yourself" becomes surprisingly expensive, with a
   large data scientist and engineering overheads, and organizations find
   themselves re-solving the same unforeseen challenges that we have already
   solved.
 * Cost of Maintenance: "Manage it yourself" has a surprisingly large data
   science and engineering overhead, to overcome production challenges that we
   have already solved.

Our Solution: Text Classification AI
Our Text Classification SaaS trains itself and learns your team's in-house style
while delivering impressive performance results. It also works out of the box at
scale for your production platforms.

Our Text Classification SaaS comes with all these benefits immediately: 

 * Customized to your Domain: It classifies text content to 100s of 1,000s of
   entities in a custom domain.
 * Powerful API: It provides a rich API for interacting with the machine
   learning service metadata.
 * Does its own ML housekeeping: It automatically manages the machine learning
   model ‘fallout’.
 * Self-Optimizing: It self-optimizes its machine learning models, on the fly.
 * Scalable: It is able to scale up indefinitely.
 * Follows your in-house style: It learns the tagging style of your SME team.
 * Production-Ready: It is Production-ready, out of the box.

All the engineering is done, and the security & maintenance are on us!

Data Language are specialists in AI Text Classification. This includes automated tagging, machine learning, language agnostic systems, self-optimisation, and specialist text classification areas.

Read about products and projects in AI Text Classification at Data Language. This includes automated tagging, machine learning, language agnostic and specialist text classification areas.

AI Text Classification - News, Technology and Product Updates

AI Text Classification

Data Language AI: Text Classification SaaS

Tagmatic is a service for automatically tagging your content.

It uses sophisticated machine learning techniques to learn how you tag your
content on-the-fly and then will tag new content automatically.

Tagmatic uses your own tag dictionaries. Whether you tag with simple text terms,
identifiers, taxonomies, or linked data, Tagmatic will tag with those. 

There is no data mapping exercise needed, just send your content & tags to
Tagmatic and it gets to work. Tagmatic quickly learns new tags too.

It is incredibly easy to integrate into your CMS or editorial workflows via two
simple web service interfaces.

If you are looking for an automated tagging solution, then look no further.

If you are developing your own automated tagging solution, then pause, give
Tagmatic a try - and be up and running quickly.

Update @ 2020: 
Tagmatic has been renamed as "Text AI Services: Text Classification
[https://datalanguage.com/products/datalanguageai/text-ai-services]" as part of
our "Data Language AI" product suite
[https://datalanguage.com/products/datalanguageai].

News, Blog Posts and Innovation updates about Tagmatic - our sophisticated AI automated content classification SaaS product. We have taken care of all the engineering and data science for you: Tagmatic learns on the fly, is self-optimising, and language-agnostic.

Tagmatic is an AI automated text classification SaaS product by Data Language. It learns on the fly, is self-optimising, and language-agnostic.

Tagmatic - News and Product Updates about our Text Classification AI

Tagmatic

Introducing Tagmatic

Text AI Services combine machine learning and AI to provide world-class text classiciation. Self-optimising, language agnostic and full managed.

Launching Text AI Services | Efficient Text Classification

We have just launched our Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services], a fully
managed, SaaS-based set of text services using AI and Machine Learning to
automate key processes. As essential tools in any digital transformation
journey, our Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] are the next
generation in NLP and text analytics.

With decades of experience in understanding and solving pain points in
structured data, we have now developed a world-class service for ai text
classification. Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] are perfect
for out of the box machine learning to make your text tlassification and
recommendations more logical, accurate and efficient.

Find out more about Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] | Book an
intro call with the Text AI Team
[https://calendly.com/datalanguage/data-language-ai-intro-call]

Launching Text AI Services

We had some fantastic news this week. Innovate UK
[https://www.gov.uk/government/organisations/innovate-uk], part of the UK
Government Technology Strategy Board has awarded Data Language an innovation
grant to fund the development of a platform to deliver crowd and machine sourced
contextual commerce and structerd data on video moments.

Our aim is to structurally enhance videos at a moment level, which will then
improve the revenue publishers can make from video. The approach will also
provide a fair reward mechanism for crowd workers.

Watch this space!

The 13-minute knowledge graph, with Netflix, Disney+, AmazonPrimeVideo, and Hulu data

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