For other uses, see Knowledge graph (disambiguation).
Example conceptual diagram
In knowledge representation and reasoning, a knowledge graph is a knowledge base that uses a graph-structured data model or topology to represent and operate on data. Knowledge graphs are often used to store interlinked descriptions of entities – objects, events, situations or abstract concepts – while also encoding the semantics or relationships underlying these entities.[1]
Since the development of the Semantic Web, knowledge graphs have often been associated with linked open data projects, focusing on the connections between concepts and entities.[2][3] They are also historically associated with and used by search engines such as Google, Bing, Yext and Yahoo; knowledge-engines and question-answering services such as WolframAlpha, Apple's Siri, and Amazon Alexa; and social networks such as LinkedIn and Facebook.
Recent developments in data science and machine learning, particularly in graph neural networks and representation learning, have broadened the scope of knowledge graphs beyond their traditional use in search engines and recommender systems. They are increasingly used in scientific research, with notable applications in fields such as genomics, proteomics, and systems biology.[4]
^"What is a Knowledge Graph?". 2018.
^Ehrlinger, Lisa; Wöß, Wolfram (2016). Towards a Definition of Knowledge Graphs(PDF). SEMANTiCS2016. Leipzig: Joint Proceedings of the Posters and Demos Track of 12th International Conference on Semantic Systems – SEMANTiCS2016 and 1st International Workshop on Semantic Change & Evolving Semantics (SuCCESS16). pp. 13–16.
^Soylu, Ahmet (2020). "Enhancing Public Procurement in the European Union Through Constructing and Exploiting an Integrated Knowledge Graph". The Semantic Web – ISWC 2020. Lecture Notes in Computer Science. Vol. 12507. pp. 430–446. doi:10.1007/978-3-030-62466-8_27. ISBN 978-3-030-62465-1. S2CID 226229398.
^Mohamed, Sameh K.; Nounu, Aayah; Nováček, Vít (2021). "Biological applications of knowledge graph embedding models". Briefings in Bioinformatics. 22 (2): 1679–1693. doi:10.1093/bib/bbaa012. hdl:1983/919db5c6-6e10-4277-9ff9-f86bbcedcee8. PMID 32065227 – via Oxford Academic.
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