Mining Cardinality Restrictions in OWL

Abstract

We present an approach to mine cardinality restriction axioms from an existing knowledge graph, in order to extend an ontology describing the graph. We compare frequency estimation with kernel density estimation as approaches to obtain the cardinalities in restrictions. We also propose numerous strategies for filtering obtained axioms in order to make them more available for the ontology engineer. We report the results of experimental evaluation on DBpedia 2016-10 and show that using kernel density estimation to compute the cardinalities in cardinality restrictions yields more robust results that using frequency estimation. We also show that while filtering is of limited usability for minimum cardinality restrictions, it is much more important for maximum cardinality restrictions. The presented findings can be used to extend existing ontology engineering tools in order to support ontology construction and enable more efficient creation of knowledge-intensive artificial intelligence systems.

Inductive Learning of OWL 2 Property Chains

“…Hellmann et al presented DL-Learner, a tool for learning class descriptions from very large knowledge bases [8]. Recently, a method for mining OWL cardinality restrictions from knowledge graphs has been proposed by Potoniec [9]. Finally, Melo et al reported on a method on adding type assertion to entities in RDF knowledge bases [10].…”