Automatic detection of relation assertion errors and induction of relation constraints

Article type: Research Article

Authors: Melo, Andre | Paulheim, Heiko^{; *}

Affiliations: Data and Web Science Group, University of Mannheim, Germany. E-mail: [email protected]

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: Although the link prediction problem, where missing relation assertions are predicted, has been widely researched, error detection did not receive as much attention. In this paper, we investigate the problem of error detection in relation assertions of knowledge graphs, and we propose an error detection method which relies on path and type features used by a classifier for every relation in the graph exploiting local feature selection. Furthermore, we propose an approach for automatically correcting detected errors originated from confusions between entities. Moreover, we present an approach that translates decision trees trained for relation assertion error detection into SHACL-SPARQL relation constraints. We perform an extensive evaluation on a variety of datasets comparing our error detection approach with state-of-the-art error detection and knowledge completion methods, backed by a manual evaluation on DBpedia and NELL. We evaluate our error correction approach results on DBpedia and NELL and show that the relation constraint induction approach benefits from the higher expressiveness of SHACL and can detect errors which could not be found by automatically learned OWL constraints.

Keywords: SHACL, SPARQL, ontology learning, error detection, knowledge graph

DOI: 10.3233/SW-200369

Journal: Semantic Web, vol. 11, no. 5, pp. 801-830, 2020