Building Knowledge Graphs: A Practitioner's Guide

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Qian, R.: Understand Your World with Bing, 21 March 2013. http://blogs.bing.com/search/2013/03/21/understand-your-world-with-bing/. Accessed 15 Jan 2017 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) 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 The types of entities and relationships in a knowledge graph are not limited, and new ones will be added over their lifetime. Your initial knowledge graph may contain information about locations and restaurants, but then you decide to extend it with details on the types of cuisine and ingredients served at the restaurants or maybe with other types of local businesses like hair salons, bookstores, or dry cleaners.

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) World Travel & Tourism Council, Travel & Tourism Economic Impact 2018 World (2018). https://www.wttc.org/-/media/files/reports/economic-impact-research/regions-2018/world2018.pdf D. Vrandečić, M. Krötzsch, Wikidata: a free collaborative knowledge base. Commun. ACM 57(10), 78–85 (2014) For example, think of two applications or two reports that implement different definitions of what a customer is. Maybe one includes churned customers, and the other one does not. Or maybe one of them considers churned customers as those with a canceled subscription, but the other just count any customer with a given period of inactivity? Even if they’re working on the same data, they could produce different results. It would be obviously beneficial to centralize and standardize the definition of critical business entities and metrics. R.V. Guha, Contexts: A Formalization and Some Applications, Ph.D. thesis, Stanford University, STAN-CS-91-1399-Thesis.guha, 1991This 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. 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. Dong, X., et al.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2014) Ehrlinger, L., Wöß, W.: Towards a definition of knowledge graphs. In: SEMANTiCS (Posters, Demos, SuCCESS) (2016) 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

I started out with a simple set of concepts. It grew over time and is still growing. I started to get into the actual products that companies sell and into the financials that gives me a perspective of how profitable some of these drugs are. Then I also wanted to get into the biology and the chemistry more, and started adding bioprocess or biological structure, in addition to the target, the molecule, and the disease. So the graph is still growing as it is, as more data becomes available and as additional questions may come to the foreground. 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. DBpedia: is a crowd-sourced community-based effort to extract structured content from the information present in various Wikimedia projects. Below, we can see one KG (movie KG) that not only contains user-item connections (here person-movies) but also user-user interactions and item attributes. The idea is that, provided all this additional information, we can make much more accurate and informed suggestions. Without going into the exact algorithm, let's rationalize what recommendations could be generated.G. Qi, J. Tang, J. Du, J.Z. Pan, Y. Yu (eds.), Linked Data and Knowledge Graph—7th Chinese Semantic Web Symposium and 2nd Chinese Web Science Conference (CSWS2013): Revised Selected Papers, Shanghai, China, 12–16 August 2013. Springer CCIS, vol. 406 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, P. Hayes, The Logic of Frames, Readings in Artificial Intelligence (Morgan Kaufmann, Los Altos, CA, 1981) 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)

E.A. Feigenbaum, Knowledge engineering: the applied side of artificial intelligence. Ann. NY Acad. Sci. 426(1), 91–107 (1984). (Originally published 1980) J.M. Giménez-García, M.C. Duarte, A. Zimmermann, C. Gravier, E.R. Hruschka Jr., P. Maret, NELL2RDF: Reading the Web, and Publishing It as Linked Data, Technical Report (2018). https://arxiv.org/abs/1804.05639 How to use machine learning to enrich your knowledge graph and mine features from a knowledge graph to create accurate predictive models. P.F. Patel-Schneider, Analyzing Schema.org, in Proceedings of the 13th International Semantic Web Conference (ISWC2014), Riva del Garda, Italy, 19–23 October 2014. Springer LNCS, vol. 8796 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)F. Erxleben, M. Günther, M. Krötzsch, J. Mendez, D. Vrandečić, Introducing wikidata to the linked data web, in Proceedings of the 13th International Semantic Web Conference (ISWC 2014), Riva del Garda, Italy, 19–23 October 2014. Springer LNCS, vol. 8796 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? How to apply knowledge graphs in real-world scenarios and explore practical applications in various industries to drive innovation and success.