TY - GEN
T1 - Detecting Synonymous Properties by Shared Data-Driven Definitions
AU - Kalo, J.-C.
AU - Mennicke, S.
AU - Ehler, P.
AU - Balke, W.-T.
PY - 2020
Y1 - 2020
N2 - © Springer Nature Switzerland AG 2020.Knowledge graphs have become an essential source of entity-centric information for modern applications. Today’s KGs have reached a size of billions of RDF triples extracted from a variety of sources, including structured sources and text. While this definitely improves completeness, the inherent variety of sources leads to severe heterogeneity, negatively affecting data quality by introducing duplicate information. We present a novel technique for detecting synonymous properties in large knowledge graphs by mining interpretable definitions of properties using association rule mining. Relying on such shared definitions, our technique is able to mine even synonym rules that have only little support in the data. In particular, our extensive experiments on DBpedia and Wikidata show that our rule-based approach can outperform state-of-the-art knowledge graph embedding techniques, while offering good interpretability through shared logical rules.
AB - © Springer Nature Switzerland AG 2020.Knowledge graphs have become an essential source of entity-centric information for modern applications. Today’s KGs have reached a size of billions of RDF triples extracted from a variety of sources, including structured sources and text. While this definitely improves completeness, the inherent variety of sources leads to severe heterogeneity, negatively affecting data quality by introducing duplicate information. We present a novel technique for detecting synonymous properties in large knowledge graphs by mining interpretable definitions of properties using association rule mining. Relying on such shared definitions, our technique is able to mine even synonym rules that have only little support in the data. In particular, our extensive experiments on DBpedia and Wikidata show that our rule-based approach can outperform state-of-the-art knowledge graph embedding techniques, while offering good interpretability through shared logical rules.
UR - https://www.scopus.com/pages/publications/85086145204
UR - https://www.scopus.com/inward/citedby.url?scp=85086145204&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-49461-2_21
DO - 10.1007/978-3-030-49461-2_21
M3 - Conference contribution
SN - 9783030494605
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 360
EP - 375
BT - The Semantic Web - 17th International Conference, ESWC 2020, Proceedings
A2 - Harth, A.
A2 - Kirrane, S.
A2 - Ngonga Ngomo, A.-C.
A2 - Paulheim, H.
A2 - Rula, A.
A2 - Gentile, A.L.
A2 - Haase, P.
A2 - Cochez, M.
PB - Springer
T2 - 17th Extended Semantic Web Conference, ESWC 2020
Y2 - 31 May 2020 through 4 June 2020
ER -