Building Knowledge Graphs: A Practitioner's Guide

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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) 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. If you’re a data scientist, you will see a knowledge graph as an augmented feature store for enriched (connected) data, where you will be able to compute and access (and operationalize and govern) structural features for ML. Think of centrality metrics for a given data point, the different data clusters it belongs to, or the distance to a given point in the graph. All these features completely escape table-based datasets and significantly improve the accuracy of your predictive models.

D.B. Lenat, R.V. Guha, Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project, 1st edn. (Addison-Wesley Longman, Reading, MA, 1989)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 Knowledge graph immediately appeared as the best option, which would lead me to additional insights and gain wisdom. The syntax is not important for now. The important bit is that when your data and knowledge are managed in the form of a graph, pretty sophisticated logic can be expressed in a concise way. And equally important, evaluated efficiently at scale over large volumes of data. But we will talk about this in more detail in future posts. For now, just keep this intuition of inference as navigating up and down the data and semantic layers in a knowledge graph. 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)

Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76298-0_52 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. With the increasing interest in knowledge graph over the years, several approaches have been proposed for building knowledge graphs. Most of the recent approaches involve using semi-structured sources such as Wikipedia or information crawled from the web using a combination of extraction methods and Natural Language Processing (NLP) techniques. In most cases, these approaches tend to make a compromise between accuracy and completeness. In our ongoing work, we examine a technique for building a knowledge graph over the increasing volume of open data published on the web. The rationale for this is two-fold. First, we intend to provide a foundation for making existing open datasets searchable through keywords similar to how information is sought on the web. The second reason is to generate logically consistent facts from usually inaccurate and inconsistent open datasets. Our approach to knowledge graph development will compute the confidence score of every relationship elicited from underpinning open data in the knowledge graph. Our method will also provide a scheme for extending coverage of a knowledge graph by predicting new relationships that are not in the knowledge graph. In our opinion, our work has major implications for truly opening up access to the hitherto untapped value in open datasets not directly accessible on the World Wide Web today. Keywords Nurdiati, S., Hoede, C.: 25 years development of knowledge graph theory: the results and the challenge (2008) 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.

Dr. Jim Webber

We sometimes refer to this as making explicit the semantics of data and is a distinctive feature of knowledge graphs. How Do Knowledge Graphs Work: Semantics & Ontologies