@inproceedings{c4a82dbd8d854bc3a107556936835b31,
title = "Knowledge Graph Consolidation by Unifying Synonymous Relationships",
abstract = "{\textcopyright} 2019, Springer Nature Switzerland AG.Entity-centric information resources in the form of huge RDF knowledge graphs have become an important part of today{\textquoteright}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.",
author = "J.-C. Kalo and P. Ehler and W.-T. Balke",
year = "2019",
doi = "10.1007/978-3-030-30793-6_16",
language = "English",
isbn = "9783030307929",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "276--292",
editor = "C. Ghidini and O. Hartig and M. Maleshkova and V. Sv{\'a}tek and I. Cruz and A. Hogan and J. Song and M. Lefran{\c c}ois and F. Gandon",
booktitle = "The Semantic Web – ISWC 2019 - 18th International Semantic Web Conference, Proceedings",
note = "18th International Semantic Web Conference, ISWC 2019 ; Conference date: 26-10-2019 Through 30-10-2019",
}