Building Knowledge Graphs: A Practitioner's Guide

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R. Angles, C. Gutiérrez, Querying RDF data from a graph database perspective, in Proceedings of the 2nd European Semantic Web Conference (ESWC2005), Heraklion, Greece, 29 May–1 June 2005. Springer LNCS, vol. 3532 R.J. Brachman, The future of knowledge representation, in Proceedings of the 8th National Conference on Artificial Intelligence (AAAI1990), 29 July–3 August 1990 (AAAI Press, Boston) F.M. Suchanek, G. Kasneci, G. Weikum, Yago: a core of semantic knowledge, in Proceedings of the 16th International World Wide Web Conference (WWW2007), 8–12 May 2007 (ACM, Banff, Canada)

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. X.L. Dong, E. Gabrilovich, G. Heitz, W. Horn, N. Lao, K. Murphy, T. Strohmann, S. Sun, W. Zhang, Knowledge vault: a web-scale approach to probabilistic knowledge fusion, in Proceedings of the 20th ACM Conference on Knowledge Discovery and Data Mining (KDD2014), 24–27 August 2014a (ACM, New York) S. Malyshev, M. Krötzsch, L. González, J. Gonsior, A. Bielefeldt, Getting the most out of Wikidata: semantic technology usage in Wikipedia’s knowledge graph, in Proceedings of 17th International Semantic Web Conference (ISWC 2018), Monterey, CA, 8–12 October 2018. Springer LNCS, vol. 11137

Knowledge graphs can be used for a large number of tasks — be it for logical reasoning, explainable recommendations, complex analysis or just being a better way to store information. There are two very interesting examples which we will discuss briefly.

Finally, once we have prepared the script (with ttl extension — for scripts in Turtle language), that script contains the complete schema and definition of our KG. In itself, this may not be interesting, hence the file can be imported into any KG database for beautiful visualization and efficient querying. Business logic normally lives in the applications that make use of the data, doesn’t it? Correct, but that’s not necessarily a good thing for a number of reasons: the logic needs to be replicated across applications with significant duplication of effort and with a risk of inconsistency. Also, know that you can keep adding knowledge into this semantic layer by including richer descriptions like taxonomies (hierarchical classifications) that provide additional meaning and offer additional ways to exploit the data in the knowledge graph.Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014) This wide-ranging guide covers everything from the basics to advanced approaches, equipping you with the knowledge and technical skills to employ this groundbreaking technology in your projects.