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

R.J. Brachman, On the epistemological status of semantic networks, in Associative Networks: Representation and Use of Knowledge by Computers, ed. by N. V. Findler, (Academic, New York, 1979)Query complex information: better than SQL for data where relationship matters more than individual data points (for example, in case you have to do lots of JOIN statements in a SQL query, which is inherently slow) Nurdiati, S., Hoede, C.: 25 years development of knowledge graph theory: the results and the challenge (2008) Rospocher, M., et al.: Building event-centric knowledge graphs from news. Web Semant.: Sci. Serv. Agents World Wide Web 37, 132–151 (2016)

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) In their new book Barrasa and Webber explain that knowledge graphs can underpin everything from consumer-facing systems like navigation and social networks to critical infrastructure like supply chains and power grids.

Dr. Jim Webber

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) While there are several small-sized and domain-specific KGs, on the other hand, we also have many huge-sized and domain-agnostic KG that contains facts of all types and forms. Some of the famous open-source knowledge graphs are, Bob: as it belongs to the Sci-fi genre same as Interstellar and Inception (which is already watched by Bob) 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 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.

For high-level strategic folk towards the top of the IT food chain (CIOs, we’re looking at you here), this book may still be useful since it provides an overview of knowledge graphs and how they are delivered. Hoffart, J., Suchanek, F.M., Berberich, K., Weikum, G.: YAGO2: a spatially and temporally enhanced knowledge base from Wikipedia. Artif. Intell. J. (2012)Ehrlinger, L., Wöß, W.: Towards a definition of knowledge graphs. In: SEMANTiCS (Posters, Demos, SuCCESS) (2016)

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, 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) Which molecules are they developing on their own, and which molecules are they developing in collaboration with others?

Table of contents

N. Noy, Y. Gao, A. Jain, A. Narayanan, A. Patterson, J. Taylor, Industry-scale knowledge graphs: lessons and challenges. ACM Queue 17(2), 48–75 (2019) J. Lehmann, R. Isele, M. Jakob, A. Jentzsch, D. Kontokostas, P.N. Mendes, S. Hellmann, M. Morsey, P. van Kleef, S. Auer, C. Bizer, DBpedia—a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web J. 6(2), 167–195 (2015) P. Hayes, The Logic of Frames, Readings in Artificial Intelligence (Morgan Kaufmann, Los Altos, CA, 1981) Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. In: Semantic Web Preprint, pp. 1–20 (2016) 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)