R. Blanco, B.B. Cambazoglu, P. Mika, N. Torzec, Entity recommendations in web search, in Proceedings of the 12th International Semantic Web Conference (ISWC2013), Sydney, Australia, 21–25 October 2013. Springer LNCS, vol. 8219 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? If you’re a developer, you will normally build applications that consume the information in the knowledge graph. For you, a knowledge graph is a database with which you’ll interact through some form of API offering you structural primitives, like “For a given item A, retrieve all other items related to it,” “Is there a direct or indirect connection between items A and B? If so, how many different ones exist?” or “What is the most significant path connecting items A and B?” Richer knowledge graphs will offer pattern-based query languages like Cypher, GQL, or SPARQL, but simpler ones may offer more basic interfaces, for example, a method returning all related items for a given one. 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

Knowledge graph immediately appeared as the best option, which would lead me to additional insights and gain wisdom. 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) Knowledge graphs are connected to a long history of research in an area of artificial intelligence called knowledge representation. The idea behind it is to combine data and business logic together in a common representation that enables the automation of complex tasks. Wikipedia defines a “knowledge graph” as a knowledge base that uses a graph-structured data model or topology to integrate data. Knowledge graphs can be very handy for the storage and representation of interconnected descriptions of a wide variety of things, including objects, events, situations, or abstract concepts.

I had designed this in FileMaker Pro initially by setting up two tables, one for subjects, one for objects, and then another table that connects everything together. I didn’t know Neo4j or property graph model at the time when this started but it allowed me to collect some data over time and then I came across Neo4j, which is exactly what I need.

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: A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E.R. Hruschka, T.M. Mitchell, Toward an architecture for never-ending language learning, in Proceedings of the 24th Conference on Artificial Intelligence (AAAI2010), 11–15 July 2010 (AAAI Press, Atlanta) Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. In: Semantic Web Preprint, pp. 1–20 (2016) 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.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 Building Knowledge Graphs: A Practitioner’s Guide is a crucial resource for developers and data scientists who aspire to excel in building, managing, and leveraging knowledge graphs, brought to you by Neo4j and O’Reilly – one of the trusted names in technology and business knowledge. 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. D. Vrandečić, M. Krötzsch, Wikidata: a free collaborative knowledge base. Commun. ACM 57(10), 78–85 (2014) Rospocher, M., et al.: Building event-centric knowledge graphs from news. Web Semant.: Sci. Serv. Agents World Wide Web 37, 132–151 (2016)

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) This graph model (see graph on the bottom right on the image above) shows a basic network, where a company designs a molecule that acts on a molecular target, and other companies work on a different molecule but act on the same molecular target. It’s the start of a network, but it’s not the end of it. How to query, analyze, and visualize your knowledge graph using languages like Cypher and uncover valuable insights through data analytics and visualization. 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 E.F. Codd, A relational model of data for large shared data banks. Commun. ACM 13(6), 377–387 (1970)Schultz, A., et al.: LDIF-linked data integration framework. In: Proceedings of the Second International Conference on Consuming Linked Data, vol. 782. CEUR-WS.org (2011) Model real-world information: closer to our brain’s mental model of the world (represents information as a normal human does) Head or tail: these are entities that are real-world objects or abstract concepts which are represented as nodes

Remember, the above representations are just for nomenclature sake, hence you may come across people referring to the fact either way. Let’s follow the HRT representation for this article. So either way, facts contain 3 elements (hence facts are also called triplets) that can help with the intuitive representation of KG as a graph, Nakashole, N., Theobald, M., Weikum, G.: Scalable knowledge harvesting with high precision and high recall. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining. ACM (2011)Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web 2007, pp. 697–706. ACM, 8 May 2007