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

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]

Blogs by Alexi Buckingham