Jesus Barrasa - Knowledge Graphs

by Jess Barrasa , Amy E. Hodler , and Jim Webber

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• July 2021: First Edition

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978-1-098-10485-6

[LSI]

The winners in the data industry know where the puck is going: making data smarter. This can be accomplished by integrating data with knowledge at scale, and this is where knowledge graphs come in. This book is a practical guide to understand what knowledge graphs are and why you should care. Importantly, it strikes the right balance between technical aspects and corresponding business value for an organization. If you need to make the business case for knowledge graphs, this is the book for you!

Lets first talk about the elephant in the room: RDF versus property graphs. Over the years, Ive enjoyed my conversations with Jess Barrasa on this topic. We have always been strong believers that these technologies will converge because at the end of the day, its all just a graph! This book is evidence of this convergence: enriching the property graph model with taxonomies, ontologies, and semantics in order to create knowledge graphs. And dont forget that the conversation should focus on the business value and not just the technology.

How do you get started on your knowledge graph journey?

First, one of my mantras is dont boil the ocean. This means that your knowledge graph journey should start simple, be practical, and focus on the business return coming from the right amount of semantics. Second, I always say that you need to crawl, walk, and run. Crawling means to start by creating a knowledge graph of your metadata that catalogs the data within your organization. Im thrilled to see that we are fully aligned: effective data integration solutions rely on understanding the relationships between data assets, which is at the heart of knowledge graphs. Furthermore, in the AI and ML era that we live in, understanding the quality and governance is key for effective decision-making.

Speaking of AI, knowledge graphs are changing AI by providing context. This leads to explainability, diversification, and improved processing. If AI is changing the future and knowledge graphs are changing AI, then by transitivity, knowledge graphs are also changing the future.

If you are still asking yourself why knowledge graphs?, guess what...your competitors arent! Dont be that person! Jess, Amy, and Jim have written this book just for you.

Juan Sequeda, PhD

Principal Scientist, data.world

July 2021

Chapter 1. Introduction

Graph data has become ubiquitous in the last decade.Graphs underpin everything from consumer-facing systems like navigation and social networks, to critical infrastructure like supply chains and policing.A consistent theme has emerged that applying knowledge in context is the single most powerful tool that most businesses have.Through research and experience, a set of patterns and practices called knowledge graphs has been developed to support extracting knowledge from data.

This report is for information technology professionals who are interested in managing and exploiting data for value.For the CIO or CDO, the report is brief yet thorough enough to provide an overview of the techniques and how they are delivered.For the data professional, data scientist, or software professional, this report provides an easy on-ramp to the world of knowledge graphs, and a language for discussing their implementation with peers and management .

Our fundamental tenet is that knowledge graphs are useful because they provide contextualized understanding of data.They achieve this by adding a layer of metadata that imposes rules for structure and interpretation.Well illustrate how using knowledge graphs can help extract greater value from existing data, drive automation and process optimization, improve predictions, and enable an agile response to changing business environments.

This chapter explains the background and motivation behind knowledge graphs.To do so, well discuss graphs and graph data and show how we can build systems with smarter data using knowledge graph techniques.

What Are Graphs?

Knowledge graphs are a type of graph, so its important to have a basic understanding of graphs before we go much further.Graphs are simple structures where we use nodes (or vertices) connected by relationships (or edges) to create high-fidelity models of a domain.To avoid any confusion, the graphs we talk about in this book have nothing to do with visualizing data as histograms or plotting a function, which we consider to be charts, as shown in .

Figure 1-1. Graphs versus charts

The graphs we talk about in this book are sometimes referred to as networks.They are a simple but powerful way of describing how things connect.

Graphs are not new.In fact, graph theory was invented by the Swiss mathematician Leonhard Euler in the 18th century.It was created to help compute the minimum distance that the emperor of Prussia had to walk to see the town of Knigsberg (modern-day Kaliningrad) by ensuring that each of its seven bridges was crossed only once, as shown in .

Figure 1-2. A graphical representation of Knigsberg and its seven bridges crossing the River Pregel