Knowledge Graph Consolidation by Unifying Synonymous Relationships

J.-C. Kalo, P. Ehler, W.-T. Balke

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


© 2019, Springer Nature Switzerland AG.Entity-centric information resources in the form of huge RDF knowledge graphs have become an important part of today’s information systems. But while the integration of independent sources promises rich information, their inherent heterogeneity also poses threats to the overall usefulness. To some degree challenges of heterogeneity have been addressed by creating underlying ontological structures. Yet, our analysis shows that synonymous relationships are still prevalent in current knowledge graphs. In this paper we compare state-of-the-art relational learning techniques to analyze the semantics of relationships for unifying synonymous relationships. By embedding relationships into latent feature models, we are able to identify relationships showing the same semantics in a data-driven fashion. The resulting relationship synonyms can be used for knowledge graph consolidation. We evaluate our technique on Wikidata, Freebase and DBpedia: we identify hundreds of existing relationship duplicates with very high precision, outperforming the current state-of-the-art method.
Original languageEnglish
Title of host publicationThe Semantic Web – ISWC 2019 - 18th International Semantic Web Conference, Proceedings
EditorsC. Ghidini, O. Hartig, M. Maleshkova, V. Svátek, I. Cruz, A. Hogan, J. Song, M. Lefrançois, F. Gandon
ISBN (Print)9783030307929
Publication statusPublished - 2019
Externally publishedYes
Event18th International Semantic Web Conference, ISWC 2019 - Auckland, New Zealand
Duration: 26 Oct 201930 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference18th International Semantic Web Conference, ISWC 2019
Country/TerritoryNew Zealand


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