Alexi Buckingham

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 the data that you put in it, and that is effectively -
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 popilated 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 a solid information
preparation - data that was already clearly structured and well organised.
Pre-defining your domain model is an important step to establish 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]

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.

Blogs by Alexi Buckingham