Paper 151 (Research track)

Learning SHACL Constraints for Validation of Relation Assertions in Knowledge Graphs

Author(s): Andre de Oliveira Melo, Heiko Paulheim

Abstract: Constraints are an important part of ontologies and are responsible for the detection of wrong statements. The automatic induction of constraints from data can assist the creation and maintenance of knowledge graphs. Current state-of-the-art knowledge graph constraint learning approaches are part of ontology learning methods and are restricted to the generation of simple RDFS or OWL axioms. In this paper we propose a method for automatically learning complex SHACL relation constraints from data in order to extend existing ontologies. Our approach translates decision trees trained for relation assertion error detection into SPARQL validation queries. We show that our approach benefits from the higher expressiveness of SHACL and can detect errors which could not be found by current automatic ontology learning methods.

Keywords: ontology learning; error detection; machine learning

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