Building Knowledge Graphs: A Practitioner's Guide

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How to apply knowledge graphs in real-world scenarios and explore practical applications in various industries to drive innovation and success. In this section, we will introduce KG by asking some simple but intuitive questions about KG. In fact, we will cover the what, why, and how of the knowledge graph. We will also go through some real-world examples. What is a Knowledge graph? Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014) This is the very first and a valid question anyone will ask when introduced to KG. We will try to go through some points wherein we compare KG with normal graphs and even other ways of storing information. The aim is to highlight the major advantages of using KG. H. Paulheim, Knowledge graph refinement: a survey of approaches and evaluation methods. Semant. Web J. 8(3), 489–508 (2017)

In this blog post, I’ll give you a no-nonsense definition of knowledge graphs, how they work, what they might mean to different people, and why you should care.

Dr. Jim Webber

Author’s note: This article is also present as a chapter in my book on data science (WIP) — A Lazy Guide to Data Science . It is a weak transcription of the first part of guest lectures I gave at the University of Tartu for the Computational Social Science Group. Reference There are two types of databases that can be used to store graphical information. The first is “property graphs” like Neo4j and OrientDB that does not support RDF file (out of the box) and have their own custom query language. On the other hand, we have “RDF triplet stores”, that support RDF files and support query language like SPARQL that is universally used to query KG. Some of the most famous ones are (with open source version), W. Reisig, Understanding Petri Nets—Modeling Techniques, Analysis Methods, Case Studies (Springer, Cham, 2013)

Here is how I built a graph that discovers knowledge about cancer growth discovery, which you can use as a guide to build your own knowledge graph from scratch. In spite of having several open-source KGs, we may have a requirement to create domain-specific KG for our use case. There, our base data (from which we want to create the KG), could be of multiple types — tabular, graphical, or text blob. We will cover some steps on how to create KG from unstructured data like text, as it’s relatively easier to convert structured data into KG using minimal domain knowledge and scripting. The complete process can be divided into two steps, This world of possibilities was enabled by just organizing information as a graph. A graph is formed of nodes and relationships. Any person, object, location, or event can be a node. The relationships describe any kind of interaction between nodes; for example, an event takes place at a location, a person knows another person, etc. Nodes and relationships will be annotated with their types and described by a collection of attributes that characterize them. For instance, a node representing a city will typically have a property indicating its current population or its geographical location, a person would have a date of birth, a name, and so on. Once facts are created as RDF and hosted on an RDF triplet store like Virtuoso, we can query them to extract relevant information. SPARQL is an RDF query language that is able to retrieve and manipulate data stored in RDF format. An interesting read for more detail is Walkthrough DBpedia And Triplestore. In their new book Barrasa and Webber explain that knowledge graphs can underpin everything from consumer-facing systems like navigation and social networks to critical infrastructure like supply chains and power grids.How to use machine learning to enrich your knowledge graph and mine features from a knowledge graph to create accurate predictive models. T.M. Mitchell, W.W. Cohen, E.R. Hruschka Jr., P.P. Talukdar, B. Yang, J. Betteridge, A. Carlson, B.D. Mishra, M. Gardner, B. Kisiel, J. Krishnamurthy, N. Lao, K. Mazaitis, T. Mohamed, N. Nakashole, E.A. Platanios, A. Ritter, M. Samadi, B. Settles, R.C. Wang, D. Wijaya, A. Gupta, X. Chen, A. Saparov, M. Greaves, J. Welling, Never-ending learning. Commun. ACM 61(5), 103–115 (2018) Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, Jr. E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: AAAI 2010, vol. 5, p. 3, July 11 2010

Ehrlinger, L., Wöß, W.: Towards a definition of knowledge graphs. In: SEMANTiCS (Posters, Demos, SuCCESS) (2016) They go on to point out that knowledge graphs are valuable because they can provide contextualised understanding of data. Context derives from the layer of metadata (graph topology and other features) that provides rules for structure and interpretation. The book shows how when connected, that context enables data pros to extract greater value from existing data, drive automation and process optimisation, improve predictions, and support an agile response to changing business environments.

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Avoiding drowning in this ever-increasing data deluge is a serious challenge, but not an insurmountable one, according to a new book, Building Knowledge Graphs: A Practitioner’s Guide, published by our good friends O’Reilly. The tome’s authors, Jesús Barrasa and Dr. Jim Webber, argue that “all is not lost” because a new category of technology, based on graphs, can help extract real value from what would otherwise be an unmanageable data tsunami. M. Van Erp, S. Hellmann, J.P. McCrae, C. Chiarcos, K. Choi, J. Gracia, Y. Hayashi, S. Koide, P.N. Mendes, H. Paulheim, H. Takeda (eds.), Knowledge graphs and language technology, in Proceedings of the 15th International Semantic Web Conference (ISWC2016): International Workshops: KEKI and NLP&DBpedia, Kobe, Japan, 17–21 October 2016. Revised selected papers. Springer LNCS, vol. 10579 (2017) So you’ve learned what a knowledge graph is and how it can be searched and explored. That’s great. But they become even more powerful when they can be queried for richer patterns. And even more, if they can be analyzed at scale for hidden insights. OpenCyc: is a gateway to the full power of Cyc, one of the world’s most complete general knowledge base and commonsense reasoning engines. A Survey on Knowledge Graphs: Representation, Acquisition, and Applications, Shaoxiong Ji et.al 2021