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The Virtual Knowledge Graph System Ontop

Xiao, Guohui, Lanti, Davide, Kontchakov, Roman, Komla-Ebri, Sarah, Kalayci, Elem G\"zel, Ding, Linfang, Corman, Julien, Cogrel, Benjamin, Calvanese, Diego, Botoeva, Elena and others. (2020) The Virtual Knowledge Graph System Ontop. In: The Semantic Web – ISWC 2020 - 19th International Semantic Web Conference. The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference Part II. Lecture Notes in Computer Science , 12507. pp. 259-277. Springer ISBN 978-3-030-62418-7. E-ISBN 978-3-030-62419-4. (doi:10.1007/978-3-030-62466-8_17) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90817)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1007/978-3-030-62466-8_17

Abstract

Ontop is a popular open-source virtual knowledge graph system that can expose heterogeneous data sources as a unified knowledge graph. Ontop has been widely used in a variety of research and industrial projects. In this paper, we describe the challenges, design choices, new features of the latest release of Ontop v4, summarizing the development efforts of the last 4 years.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1007/978-3-030-62466-8_17
Uncontrolled keywords: artificial intelligence; databases; graph theory; Human-Computer Interaction (HCI); knowledge graphs; knowledge representation; knowledge-based system; ontologies; query processing; search engines; semantic web; semantic Web; semantics; SPARQL; Web ontology language
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 12 Oct 2021 14:46 UTC
Last Modified: 12 Oct 2021 14:46 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90817 (The current URI for this page, for reference purposes)
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