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

Alexi Buckingham

Our Text AI Services are easy-to-integrate SaaS tools that use AI and machine learning to make document classification as efficient as possible. 

Document Classification Text AI Service by Data Language

AI Document Classification
Data Language has been tackling the problem of document classification for many
years and has used their wealth of experience in NLP, data science, and text
analytics to create a product that is world leading. 

Our Text AI Services are easy-to-integrate SaaS tools that use AI and machine
learning to make content categorisation and tagging as easy and efficient as
possible. Our Text AI Services dramatically reduce the cost and effort
associated with tagging and organising written content, speeding up
behind-the-scenes processes and ensuring accuracy.

With our Document Classification Text AI Service, the difficult engineering and
scaling is done for you, dramatically reducing the total cost of ownership of
developing AI based classification in-house.

Document Classification Text AI Service main features
 * Fully managed Software as a Service, trivial to integrate into your systems
 * Language agnostic
 * Classifies content using your own vocabularies and dictionaries
 * Self optimizes and learns on-the-fly independently to your workflow
 * Dramatically reduces the time, cost and complexity of tagging content
 * Gives you more time to do what you do best

Benchmarking
We have benchmarked our Document Classification Text AI Service against Reuters 
RCV1 and Arvix Academic Paper Dataset AAPD and achieved impressive results.
 Read more about the benchmarks here
[https://datalanguage.com/blog/benchmarking-tagmatic].

Our Document Classification Text AI Service is perfect for almost any text
classification problem, and in any language, some of its primary use-cases are : 

Publishing
Our Document Classification Text AI Service was originally built to tackle the 
challenges that publishers [https://datalanguage.com/tagmatic-for-publishers] 
and media organisations have with automatic and human-assisted content tagging:
reacting to new terms (tags) fast as they break in news, automatic on-the-fly
training, consistency, disambiguation, editorial tagging style  - all this is
handled out of the box by our service. 

Law and Legal Tech
Our Document Classification Text AI Service is perfect for classifying legal
texts and documents, or part-of-documents such as contract clauses. Once
trained, it can be used to automate the tagging of incoming legal documents, as
well as archives of existing documents. We have demonstrated Document
Classification Text AI Service on Hansard of some 200,000 parliamentary
interventions for the 2005/6 period , classified with >7200 categories from the
parliamentary library classification vocabulary, and achieved extremely accurate
results.

Healthcare and Life Sciences
Through our work with Cochrane, we have extensive experience with bioinformatics
vocabularies such as SNOMED-CT, MESH, and MedDRA. Our Document Classification
Text AI Service is ideally suited for the automated classification and tagging
of content against these vocabularies, whether for classifying clinical trials,
 patient records, or general medical documents.

Free Trial
If you are a publisher or work with high volumes, or large content archives and
are working on or thinking of building your own automated tagging platform, then
our Document Classification Text AI Service is an ideal solution - you can be up
and running in hours, and reduce your total cost of ownership of building,
scaling, and hosting your own AI / NLP based classification solution
dramatically. 

Please get in touch for a free trial.

News and product updates about our Text AI Services. Read about how our Text AI products are being used in innovation projects, and by our clients.

Read the latest on our Text AI Services. Projects, Innovation initiatives, and new developments for AI features in specialized use cases.

News and Product Updates about our Text AI Services

Text AI Services

Data Language AI Products

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

Data Moments enhances videos with structured data that creates new brand activation, new brand engagement, and new shoppable moments opportunities.

Data Moments - The video moment product that activates your video

Since our inception in 2014 we have been working on innovation around video
metadata technology, and Data Moments
[https://datalanguage.com/products/datamoments] is the productized upshot of all
this work. Data Moments [https://datalanguage.com/products/datamoments] 
massively boosts video reach, engagement, and monetisation opportunities.

Data Moments [https://datalanguage.com/products/datamoments] enhances video with
machine-friendly structured metadata that enables all players in the video value
chain to create new brand experiences, and to harness this new core business
data.

Data Moments on Jamie Oliver Video: HowTo Moments and Shoppable Moments
We have been working with Jamie Oliver to make the moments in his video more
"Cook along"-able, and to add products to key video moments to create "Shoppable
moments".

Screen grab showing how Data Moments
[https://datalanguage.com/products/datamoments] promotes a "shoppable moment"
when consumer intent is at its height. These Jamie Oliver Shoppable Moments
created CTR of 5.4% and ecommerce conversion of 9.6%Data Moments
[https://datalanguage.com/products/datamoments] on the Rugby World Cup: Sport
Event Moments
We worked with the Japanese broadcaster J SPORTS to enhance their Rugby World
Cup video [https://video.jsports.co.jp/] with activated video moments, promoting
their e-commerce products, their SVOD service, and highlighting each important
sporting event.

Rugby World Cup moments with new SEO and Social optimisation from Data Moments
[https://datalanguage.com/products/datamoments]. Each sporting event was
labelled with optimised metadata.

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 Moments: Video Moment Technology

Tolga Ergin

It’s what we do
Building innovative data driven platforms and products is what we do. We are
experts in innovative data driven products and platforms. 

We are world class data and software engineers, and build practical machine
learning and AI solutions that deliver real business value rapidly.

But we don’t just stop at the platform, our talented developers also build
beautiful, accessible data visualisations and user experiences.

How do we do it
Our approach is multi-fold. Tried and tested, and focussed on delivering early
value.

Domain-Driven Design
To build a great product it is critical to understand the domain. We use 
domain-driven design [https://datalanguage.com/domain-driven-design] at the
outset to gain a broad-enough and deep-enough understanding of the problem space
and value-proposition not just to deliver the right solution, but to deliver the
right value too.

Minimum Useful Product
Delivering early value is key. Working with stakeholders and the outputs of
domain-modelling we identify a Minimum Useful Product (MUP), that is both
desirable and viable.

Technical Strategy
We take a practical and pragmatic approach to technical strategy
[https://datalanguage.com/technical-strategy], tailored on business vision,
rigorous risk management, lean processes for agile governance, ultimately
designing technical solutions to support market opportunities.

Iterative Full-Stack Implementation
We build a feature-driven vertical slice through our platform as early as
possible. We automate from the outset using contemporary tooling for continuous
delivery, including infrastructure-as-code, and
test/behavioural-driven-development patterns.
Our data models are maintained and evolved iteratively too, modelling
just-enough to deliver the detail of the evolving-features, while keeping in
mind the big picture domain model. 
By delivering an early vertical-slice, we identify risks and challenges early.
Our vertical-slice is iteratively evolved feature-by-feature to deliver the MUP,
with user and stakeholder feedback at each iteration.

Data Driven Product Innovation

You are missing huge opportunities available in your "video moments", when your user is thinking "I want to buy / go / know / do / watch"! Use Data Moments.

Video Micro-Moments: Meet user needs: Buy, Do, Go, Know, Watch

If you are in the business of creating high production-value video, you spend a
lot of valuable expertise, time and resources creating the video.

With conventional video publishing, the only part of this that is accessible and
usable for your viewers is "the whole video" – you are missing countless
opportunities available in your "video moments".

How can you unleash the value that you have locked into your amazing video
moments?

Use Video Micro-Moments
Meet user needs at scale, across your video and brand portfolio, using our SaaS
product Data Moments [https://datalanguage.com/products/datamoments] to create
your Video Micro-Moments.

With Video Micro-Moments you can offer your user relevant information ("Making
thyme-flavoured oil, shown below) or the very products that are being used (the
pestle and mortar, shown below), right in the very moment when they are needed:

Use our activated Data Moments [https://datalanguage.com/products/datamoments] 
video moments to meet user needs, exactly when they are thinking "I want to do"
or "I want to buy" or "I want to know".These video moments also surface in
search engines, and can be shared on social media, fully unlocking the potential
of your video content.

Get in touch for a demo.

Video Micro-Moments: Meet user needs, in the moment

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.

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

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

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

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

David Wesby

Charlie Furniss

Data Graphs [https://datagraphs.com/] has a brand new Data Visualisation
feature. We have expanded on the 'Domain Model' feature within Data Graphs to
now allow you to navigate through your knowledge graph visually with the 'Graph
Explorer.

Data Graphs visualises your domain model automatically, allowing you to explore
and interact with your knowledge graph in a new light. Presenting your
information visually helps provide context, meaning and interconnection in your
data. 

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

New Data Graphs Feature: Knowledge Graph Explorer

Financial Times

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

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

Janes

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 Moments [https://datalanguage.com/products/datamoments] is a video product
that uses structured data to activate video moments. It makes video more
discoverable to customers, and more attractive to advertisers. Created by the
team at Data Language [https://datalanguage.com/], it is built from years of
experience in metadata and digital video publishing.

What is Data Moments?

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

Datagraphs Logo on Dark

Data Moments Logo on Dark

Data Language AI Logo on Dark

Data Moments activates video by adding structured data moments, used across multiple channels including social media to increase high-intent audience engagement and reach.

Data Moments - Activate your Video

The last decade has seen an explosion in video content creation. 

But with video now everywhere, much of the potential value of that video is
lost. 

Companies are increasingly looking for solutions to make their video content
more discoverable to their customers and more attractive to their advertisers.

Data Moments is the next evolution in video marketing
Using Data Moments, we maximise the potential of key moments in your video and
in turn creates 2 key advantages:

 1. Your video now has many completely new SEO opportunities - one for every
    moment meaning your users can find each video moment via a Google search.
 2. While conventional video advertising can be both disruptive and irrelevant,
    Data Moments allows brand activation at a logical time, creating
    opportunities for sponsors or advertisers provide    users in a video with
    what they want, when they want it.

And of course, measuring your ROI is essential. All these results can be viewed
by you when you attach Data Moments to your video content. 

The long and short of it, Data Moments can turn your passive video viewers into
valuable high-intent users.

Data Language AI Strapline

Data Language AI Logo

Data Moments Strapline

Data Moments Logo

Data Graphs Strapline

Our products work together to accelerate digital transformation, and to lower the cost of innovation: Data Graphs, Data Language AI and Data Moments.

Products by Data Language - Data-led Digital Transformation

Our products work together to accelerate digital transformation, and to lower
the cost of innovation.

Our Products

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

Scopa Logo

Luminery Logo

Luminery website

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

Shoppable moments are when users are given the utility they need to buy a
product that they see in the video, in the actual moment when they see it. This
can be achieved using technology that allows users to click directly to an
e-commerce product offer page from that video moment, or to "Add to cart" in
that video moment.

What are Shoppable Moments in Video?

Video moments connect the user to what they want when they actually want it. For
example, we show the user a product that is featuring in a video when they are
likely to actually want to buy it. This is in total contrast to the outdated
video Ad paradigm of randomly interrupting user experiences with unrelated
products when they do not want to buy them (and while tracking them around the
internet).

Why do Video Moments work better than Video Ads?

Video Moments can be used to meet user needs when they are at their strongest.
In the case of Products, you can provide a product moment to show the e-commerce
destination that offers a specific product at the exact moment that it is being
used or demonstrated in the video. This harnesses the strongest user intent, and
typically saves the users many clicks and search tasks.

How can Marketers use Video Moments to sell products?

Video Moment Technology enables Publishers, Brands and Product Retailers to add
new utility and commercial opportunity at a video moment level. Structured video
moment metadata is used to connect new highly contextual user experiences, brand
promotion opportunities, and to remove the reliance on cookie-based user
tracking and conventional video Ads.

What is Video Moment Technology?

of Video Micro-Moments generated by Data Moments
[https://datalanguage.com/products/datamoments] for the Rugby World Cup were
listed in the first page / top 10 of Google Search Results (SERPs). Statistic
source: Data Moments video key moments on Rugby World Cup 2019, for J SPORTS
[https://datalanguage.com/news/luminery-on-rugby-world-cup].

new interactions with Video Moments for every 100 video views on Jamie Oliver.
These are brand new interactions meeting user needs in Micro-Moments, that video
publishers did not have at their disposal previously. Statistic source: Data
Moments on Jamie Oliver videos
[https://datalanguage.com/news/luminery-video-moments-on-jamie-oliver-results].

CTR on Video Plays for Product "I want to buy" Micro-Moments on Jamie Oliver
Videos. This is 10 x the Video Pre-roll Ad standard of around 0.5% CTR.
Statistic source: Data Moments on Jamie Oliver videos
[https://datalanguage.com/news/luminery-video-moments-on-jamie-oliver-results].

5.4%

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.

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

Nicola Donadoni

Simplicity as competitive advantage. Simpler is better. As simple as that.

Techniques for hypothesis validation and minimisation of negative outcomes are
utilised throughout the design and execution of the technical strategy.

Effective evolutionary technical strategies that are continuously aligned to
business propositions.

Data Language Text AI Services can be applied to almost any text classification
problem, and are ideally suited to domains such as News, Sport, and
Lifestyle/Entertainment Publishing. Importantly, our Text AI Services also work
in Law and Legal tech for classifying legal texts, contracts and clauses, and in
Healthcare and Life Sciences for classifying documents against bioinformatics
vocabularies such as SNOMED-CT, MeSH, and MedDRA (as a Computer Aided Coding
System).

What sectors do Data Language Text AI Services work for ?

Data Language Text AI Services are incredibly easy to integrate. They have
powerful APIs for training and prediction. Documents are sent to the training
endpoint for on-the-fly training, and sent to the predict endpoint for
sub-second rapid prediction. We also have a visual playground that can be used
for experimenting, and trialling the services. All the infrastructure is managed
by us, you can be up and running in just hours.

How do I integrate Data Language Text AI Services with my publishing system ?

Data Language Text AI Services are language and character set agnostic. They
work out-of-the-box on all latin character set languages, as well as Cyrillic,
greek, Arabic, and any other alphabet based language. They have not yet been
deployed on Chinese and Japanese Kanji, but if you have a project for
classification of Kanji text, we would be happy to work with you.

What languages do Data Language Text AI Services support ?

Data Language Text AI Services are commercial SaaS tools for sophisticated,
language agnostic text classification and recommendation using AI and machine
learning. They are fully managed cloud services, that use your own dictionaries,
taxonomies, vocabularies or knowledge graph entities. They are built on pure
machine-learning algorithms and thus avoids problems such as disambiguation.
They are self-optimizing, and evolve as your content evolves.

What are Data Language Text AI Services?

The outcome of domain modelling is a set of related conceptual models and a
ubiquitous language. A domain model is one of potentially many models that
describe your organisation's core business space. If a project team designs,
writes code or acts on a strategy with a shared understanding of the domain, it
reduces project risk and can improve the quality of the software being
developed.

What is the outcome of domain modelling?

Domain models are supposed to grow and change as the problem space evolves. We
often start an engagement with a big picture view of an organisation's domain.
This helps everyone understand the process and its potential value, as well as
setting more focused sessions in context. As a product moves into a discovery
phase, a key part of discovery becomes understanding the domain and developing
the ubiquitous language of the team. It is key here that we attempt to keep the
models as small and loosely coupled as possible. In DDD we will talk about
bounded context and avoiding designing all encompassing enterprise data models.
Enterprise scale data models tend to work against the goals of making the domain
easy to communicate to others and retaining model flexibility over time.

How does domain driven design fit into the product life-cycle?

For products that deal with complex domains, the team's collective ability to
learn becomes the most significant constraint. It also presents the largest risk
as ambiguity or lack of domain knowledge compound errors in the system. These
types of errors tend to be expensive to fix if caught late. By refocusing the
project on its primary domain design decisions can be made which are informed by
a shared team understanding.

Why is domain driven design important to product development?

Domain-driven design is the process of optimising a team's ability to learn about the domain it is working in. Find out more here.

How do I do Domain Driven Design? by Data Language

Domain-driven design is the process of optimising a team's ability to learn
about the domain it is working in. Documents are often places where we can hide
our ignorance about a domain and DDD discourages the over reliance on
documentation. Domain modelling sessions will most likely take place in front of
a whiteboard where the team and subject matter experts will draw back at each
other using illustrative examples from the problem space. Through this
exploration of the domain team members reach a consensus in their understanding.
This consensus (or ubiquitous language) is the foundation that development work
can then build on. In a complex domain you will need to conduct many sessions to
capture different perspectives and establish how meaning changes between
contexts and edge cases.

How do I do domain driven design?

We take you through a quick explanation of Domain Driven Design, the process of optimising your team's understanding of the domain they work in.

What is Domain Driven Design/Domain Modelling/a Domain Model?

A domain model is a conceptual model which results from the activity of
domain-driven design. The conceptual model will often be represented as an
entity relationship diagram. It is important to note that domain-driven design
puts less emphasis on the documentation but rather on the collective learning
that the team gains from the process. This helps reduce the risk of expensive
mistakes being made late-on in a project where they have become encoded in lines
of code or data models.

What is domain driven design? (domain modelling/domain model)

GraphQL is a contemporary API technology developed by Facebook. Typically it is
used for querying, manipulating and serializing JSON data structures. Until
recently (as of Jan 2020) it was not normally associated with the querying of
graph databases, but a number of graph database vendors are now providing
GraphQL API interfaces alongside the native query languages like SPARQL and
Gremlin. While it is a very high utility, and extremely developer-friendly API
technology, it is not as expressive or as fully featured as the native graph
query languages.

What is GraphQL, how does it relate to Graph Databases ?

The method of querying a knowledge graph depends on its implementation, they are
typically queried using one or more dedicated query languages. A knowledge graph
built upon a labelled-property architecture (e.g. Neo4J) is typically queried
with either Cypher or Gremlin. A RDF based graph (e.g. GraphDB or StarDog) is
queried using SPARQL. Graph database vendors are also starting to provide
GraphQL interfaces as a query mechanism, but this does not provide the
expressiveness and feature-capability of the native query languages. These query
languages are typically aimed at developers and data specialists, for business
users, the ability to navigate and ask questions of the graph is typically
abstracted into microservices, search interfaces, visual graph explorers, or
tailored user-experiences to meet specific use-cases.

How do you query a knowledge graph ?

Graph Databases come in two main archetypes: Labelled-Property Graphs (LPGs),
and Resource Description Framework (RDF) based graphs.

With a labelled-property graph, nodes and edges both can have metadata
(properties) assigned to them in the form of key-value pairs. They are tuned for
querying and analyzing Paths through the graph, and do not typically support
semantic reasoning.

RDF based graph databases are based upon the W3C RDF standard, and store data in
the form of triple-statements (subject-predicate-object). The predicates
(relationships) that join nodes together confer semantic meaning upon the data.
RDF graph databases can be reasoned upon, and can infer new assertions
(statements) from the semantic data model.

What types of graph databases are there ?

Last updated: 25th May 2018

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the Data Language team. Information on how Google keeps personal data secure
can
be found here [https://privacy.google.com/intl/en-GB/your-security.html?], and 
Google's privacy policy can be found here
[https://www.google.com/policies/privacy/]. Information on how Hubspot keeps
personal data secure can be found here [https://www.hubspot.com/security], and 
Hubspot's privacy policy can be found here
[https://legal.hubspot.com/privacy-policy].

We will not share your personal data with any other organisation unless you
provide us with your consent.

Security
The security of your Personal Information is important to us, but it is
important to note that no method of transmission over the Internet, or method of
electronic storage, is 100% secure. While we strive to use commercially
acceptable means to protect your Personal Information, we cannot guarantee its
absolute security.

Access to your Personal Data
If you have submitted your name and/or email address to us previously, you are
able to request and view any and all personal information that we hold on you.
Simply use our contact form here, [https://datalanguage.com/contact-us]and
provide us with a message requesting your data. It is important that you use the
same email address that you first sent to us.

Your right to be forgotten
If you have submitted your name and/or email address to us previously, you can
request that we remove any and all personal information of yours that we hold.
Simply use our contact form here, [https://datalanguage.com/contact-us]and
provide us with a message requesting that we delete your data. It is important
that you use the same email address that you first sent to us.

Log Data
Like many site operators, we collect information that your browser sends
whenever you visit our Site ("Log Data"). This Log Data may include information
such as your computer's Internet Protocol ("IP") address, browser type, browser
version, the pages of our Site that you visit, the time and date of your visit,
the time spent on those pages and other statistics. We use a third party service
called Google Analytics that collects, monitors and analyzes this "Log data".
You can find the Google Privacy Policy here
[https://www.google.com/policies/privacy/]. We also use a third party service
called Hubspot Analytics that collects, monitors and analyzes this "Log data".
You can find the Hubspot Privacy Policy here
[https://legal.hubspot.com/privacy-policy].

Communications
If you submit your information to us via our Contact Us page
[https://datalanguage.com/contact-us], we may use your Personal Information to
contact you with Data Language -related marketing or promotional materials and
other information that is relevant to you.

Cookies
Cookies are files with small amount of data, which may include an anonymous
unique identifier. Cookies are sent to your browser from a web site and stored
on your computer's hard drive. Like many sites, we use "cookies" to collect
information about website usage using Google Analytics. We also use Hubspot
Analytics to collect similar website usage information. You can instruct your
browser to refuse all cookies or to indicate when a cookie is being sent.

Changes To This Privacy Policy
This Privacy Policy is effective as of 25th May 2018 and will remain in effect
except with respect to any changes in its provisions in the future, which will
be in effect immediately after being posted on this page. We reserve the right
to update or change our Privacy Policy at any time and you should check this
Privacy Policy periodically. Your continued use of the Service after we post any
modifications to the Privacy Policy on this page will constitute your
acknowledgment of the modifications and your consent to abide and be bound by
the modified Privacy Policy. If we make any material changes to this Privacy
Policy, we will notify you either through the email address you have provided
us, or by placing a prominent notice on our website.

Contact Us
If you have any questions about this Privacy Policy, please contact us
[https://datalanguage.com/contact-us].

Privacy Policy

A graph database is a NOSQL database based on graph-theory that stores data in
indexed collections of nodes and edges, where the nodes represent domain
entities, and the edges represent the semantic relationships between the
entities. Node-Edge-Node relationships (statements) can be chained to create
navigable arbitrary-length paths through the data. Graph databases can be
queried using one of a number of query languages depending on the database
implementation type. They can typically store many billions of assertion
statements, and are often used as an implementation technology for knowledge
graphs. Graph databases typically form the foundation of a knowledge graph - but
not a necessary requirement.

What is a Graph Database ?

Knowledge graphs are becoming an important and integral part of an
organisation's data landscape. They provide a human and machine readable
database of all the things of interest to the enterprise in their domain. The
knowledge graph typically describes the domain entities and the semantic
relationships between them. It provides interfaces to query the graph as
structured data to drive products and user-experiences both internal and
external to the organisation. It is evolvable, and the knowledge stored can grow
and mutate as the business itself grows and changes. Its value lies in its
ability to allow an organisation to ask questions of the things it cares most
about.

What is a Knowledge Graph ?

We benchmarked our Tagmatic service against Hansard data: a corpus of ~200,000
parliamentary interventions for the 2005/6 period , classified with ~7200
categories, F1 scoring a whopping 0.83

0.83

Data Language have been fantastic working with us to develop a set of domain
models. Through this process we were able to design a coherent set of ontologies
to enable new innovative data driven products.

Parliament

Data Language have a very high level of knowledge in data modelling and overall
skills in technical architecture and semantic workflows.

Data Language have helped Cochrane transform from a document centric
organisation to a data centric one. Their knowledge of linked data and data
strategy is world class. They deliver.

Cochrane

The Google knowledge graph contains over 1 billion entities, and 70 billion
facts (assertions) about those entities. The majority of Graph Databases today
will comfortable scale to many billions of statements.

The Linked Open Data Cloud [https://lod-cloud.net/] contains some 1,239 datasets
such as Wikidata, DBpedia, Geonames, IMDB, BBC Music, data.gov.uk, across
domains including media, life-sciences, government, social and geography.

1,239

We helped Cochrane develop a knowledge graph for evidence-based health care with
some 350k concepts covering populations, interventions and outcomes, linking to
industry biomedical vocabularies such as MeSH and SNOMED-CT.

350k

 * Make more of your video content for use across multiple channels
 * Track the impact of your campaigns more accurately and show ROI
 * Increase web traffic and SEO
 * Allow viewers to navigate quickly to individual moments for an optimised
 * viewing experience

Marketers & Content Creators

 * Capture the video moments that best showcase your brand
 * Integrate contextual commerce product ads for instant ROI
 * Use video content across multiple channels for maximum reach

Social Media Managers

 * Integrate product and brand ads without impacting viewing experience and
   reduce risk of ad blockers
 * Control the products you advertise - integrate with many online shops
 * Achieve up to 20% conversion on Amazon

Advertisers

We have built success on strong working ethics and constant pursue of
excellence. We have a dynamic, lean, organisational structure the ideal
environment for driven talent to develop, raise and thrive.

We take our professional mission very seriously, but also believe that an easy
going, relaxed environment makes the best setting for people to reach their full
productivity potential in their own style. We foster continuous learning,
incentivise initiative and creativity, and reward out-of-the-box thinking. We
are tremendously focused, and equally affable.

Join Us

Modelling and experimentation are only part of the fun. Taking an idea to
production is a journey through a wide range of software engineering challenges.
Take your passion for AI to the next level.

ML engineer

Before making tea you think of a test for it, and make it fail first? We want to
hear from you.

Jamie Oliver uses Data Moments

Related work

Data Moments contextual commerce "Product Moments"

Data Moments "How To" video moments.

Our Products

Related work