This article explores what knowledge graphs are, why they are becoming a favourable data storage format, and discusses their potential to improve artificial intelligence and machine learning analytics.

1. Knowledge Graphs
And Machine Learning
AI Analytics?
- The Future Of

2. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
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IntroductionIntroduction
The unprecedented explosion in the amount of information we are generating and
collecting, thanks to the arrival of the internet and the always-online society, powers all
the incredible advances we see today in the field of artificial intelligence (AI) and Big
Data.
With this in mind, a great deal of thought and research has gone into working out the
best way to store and organise information during the digital age. The relational
database model was developed in the 1970s and organises data into tables consisting of
rows and columns – meaning the relationship between different data points can be
determined at a glance.
Knowledge Graphs And Machine Learning – The
Future of AI Analytics?

3. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
Title
Text
IntroductionIntroduction
This worked very well in the early days of business computing, where information
volumes grew slowly. For more complicated operations, however – such as establishing
a relationship between data points stored in many different tables - the necessary
operations quickly become complex, slow and cumbersome.
Machine learning – the self-teaching algorithms designed to become more accurate at
generating predictions from data as they are fed increasingly large volumes of
information – often need to draw data from vast and disparate datasets. It quickly
became apparent that a new approach was necessary.
Knowledge Graphs And Machine Learning – The
Future of AI Analytics?

4. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
There have been many attempts to improve on the functionality of the relational database
since the model was first developed. One which is quickly growing in popularity due to its
flexibility and potential for dealing with complex, interrelated data, is the knowledge graph
(sometimes known as a graph database.)
The meaning of the term is not precisely set-in-stone – for example, Google has a specific
feature which it calls the Knowledge Graph, which powers the section of its search results
page that displays factual information, drawn from recognised sources of authority.
While this is built with several of the same ideas that feed into the broader concept of the
knowledge graph, it's not the be-all and end-all of the technology.
In basic terms, a knowledge graph is a database which stores information in a graphical
format – and, importantly, can be used to generate a graphical representation of the
relationships between any of its data points.
The Knowledge Graph

5. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
This means that the apparent advantage over the older, relational style database is that the
relationships between any data points can be calculated far more quickly and with less
compute power overheads, regardless of whether the data points fit neatly together into a
table.
Oracle – which actually released the first commercially available relational database
management system in 1978 – is now leading the field in making knowledge graph systems
available to the wider business community. Currently, you're more likely to find them being
used by tech giants and research institutions, but this is set to change in the very near future.
Hassan Chaffi, senior director of research and advanced development at Oracle Labs,
describes the difference between relational and graph databases to me in this way: "With a
relational database … it just deals with tables … it allows you to find a row in a table, or take
two tables and combine them … and with every join, you're traversing one hop in these
graphs, but you're reasoning about it in these tabular ways.
The Knowledge Graph

6. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
"So now what we're saying is, what if you were to rearrange that same information as a
graph? Now it's visual, and instead of having these tables representing connections, you have
vertices which represent people, or accounts, and you have 'edges' which represent
relationships. Now I can more quickly say, 'ok, are Bob and Charlie related? And I can see
easily that they are."
The Knowledge Graph

7. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
At the moment, knowledge graphs are widely used by the tech giants that have made
gathering and analysing huge volumes of messy, complex data their core business. They
power Google’s search engine, as the original page rank algorithm is based on a form of
knowledge graph, as well as later additions to its search technology such as the Knowledge
Graph.
Facebook also relies on this form of information organisation, to keep track of networks of
people and the connections between them, as well as every other data point they use to build
a picture of their users, such as favourite artists and movies, events attended and
geographical locations. One of its most significant breakthroughs is considered to be the
realisation that the relationships between data points are as valuable as the data points
themselves when it comes to building social networks.
Netflix uses knowledge graph technology to organise information on its vast catalogue of
content, drawing connections between movies and TV shows and the actors, directors or
producers who put them together. This helps them to predict what customers might like to
watch next, and foster the "binge-watching" model of consumption it has built its business
around.
Who Uses Knowledge Graphs?

8. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
Electronics and manufacturing giant Siemens uses knowledge graphs to build accessible
models of all of the data it generates and stores, and use it for risk management, process
monitoring and building “digital twins” – simulated versions of real-world systems which can
be used for design, prototyping and training.
In supply chain logistics, knowledge graphs can be used to keep track of inventories of
different components and parts, allowing manufacturers to understand the crossover between
materials that are used in different products.
They are also being quickly adopted by the financial services industry, where they are useful
for assessing whether or not transactions are fraudulent, as well as many other functions such
as marketing and investment analytics.
Chaffi tells me "In particular for crime and compliance … for money laundering, one can think
of money moving around as a graph, and you need to think about whether those movements
are risky or not … If you were to follow the trail that starts from one place, does it come back
to the same place after an indefinite number of hops?"
Who Uses Knowledge Graphs?

9. © 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
With industries increasingly adopting machine learning, it seems likely that knowledge graph
technology will also evolve hand-in-hand. As well as being a useful format for feeding training
data to algorithms, machine learning can quickly build and structure graph databases,
drawing connections between data points that would otherwise go unnoticed.
Machine learning is great for answering questions, and knowledge graphs are a step towards
enabling machines to more deeply understand data such as video, audio and text that don’t
fit neatly into the rows and columns of a relational database.
This could potentially revolutionise fields where the technology undoubtedly has applications
that have not yet been fully explored, including healthcare and law.
As with machine learning itself, what started as an academic exercise before being adopted by
the most cutting-edge tech companies will no doubt “trickle down”, as tools and frameworks
designed to make it accessible become more widely available.
Machine Learning

10. © 2017 Bernard Marr , Bernard Marr & Co. All rights reserved
© 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a
strategic business & technology advisor to governments and companies. He helps
organisations improve their business performance, use data more intelligently, and
understand the implications of new technologies such as artificial intelligence, big data,
blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent
contributor to the World Economic Forum and writes a regular column for Forbes. Every day
Bernard actively engages his 1.5 million social media followers and shares content that
reaches millions of readers.
Visit The
Website
© 2017 Bernard Marr , Bernard Marr & Co. All rights reserved
© 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a
strategic business & technology advisor to governments and companies. He helps
organisations improve their business performance, use data more intelligently, and
understand the implications of new technologies such as artificial intelligence, big data,
blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent
contributor to the World Economic Forum and writes a regular column for Forbes. Every day
Bernard actively engages his 1.5 million social media followers and shares content that
reaches millions of readers.
Visit The
Website

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