Building Knowledge Graphs: A Practitioner's Guide

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Example of knowledge graph-based knowledge panel used by Google. [Right] the actual panel shown by google when you search for Einstein. [left] recreation of how we might store similar information in a KG. Source: by Author + Google. 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. M.K. Bergman, A Knowledge Representation Practionary—Guidelines Based on Charles Sanders Peirce (Springer, Cham, 2018) J. Hoffart, F.M. Suchanek, K. Berberich, G. Weikum, YAGO2: a spatially and temporally enhanced knowledge base from Wikipedia. Artif. Intell. 194, 28–61 (2013) Classical algorithms considered user-item interactions to generate recommendations. Over time, newly created algorithms started considering additional information about the user as well as items to improve the recommendations.

M. Croitoru, P. Marquis, S. Rudolph, G. Stapleton (eds.), Proceedings of the 5th International Workshop on Graph Structures for Knowledge Representation and Reasoning (GKR2017): Revised Selected Papers, Melbourne, 21 August 2017. Springer LNCS, vol. 10775 (2018) J.F. Sowa, Semantic networks, in Encyclopedia of Artificial Intelligence, ed. by S. C. Shapiro, 2nd edn., (Wiley, New York, 1992). http://www.jfsowa.com/pubs/semnet.pdf

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The fundamental principles of knowledge graphs, their components, and their growing importance in today’s data-driven world. How to apply knowledge graphs in real-world scenarios and explore practical applications in various industries to drive innovation and success. 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 H. Paulheim, Machine learning with and for Semantic Web knowledge graphs, ed. by C. d’Amato, M. Theobald, in Proceedings of the 14th International Summer School 2018: Reasoning Web. Learning, Uncertainty, Streaming, and Scalability: Tutorial Lectures, Esch-sur-Alzette, Luxembourg, 22–26 September 2018a. Springer LNCS, vol. 11078

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) You might be wondering: What does that mean exactly? How do we capture business logic in a graph? Why would I want to do that?Ehrlinger, L., Wöß, W.: Towards a definition of knowledge graphs. In: SEMANTiCS (Posters, Demos, SuCCESS) (2016)

This is the most general definition I could think of, and because of its generality, it will probably leave you unsatisfied, so here are some more refined ones depending on who you are: I.J. Goodfellow, Y. Bengio, A.C. Courville, Deep Learning. Adaptive Computation and Machine Learning (MIT Press, Cambridge, 2016) 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.Heterogenous data: supports different types of entities (person, date, painting, etc) and relations (likes, born on, etc).