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Mining Cardinality Restrictions in OWL

100%

Potoniec J.

Foundations of Computing and Decision Sciences

2020

tom Vol. 45, No. 3

195--216

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.

ReqTagger: A Rule-Based Tagger for Automatic Glossary of Terms Extraction from Ontology Requirements

51%

Wiśniewski D. , Potoniec J. , Ławrynowicz A.

Foundations of Computing and Decision Sciences

2022

tom Vol. 47, No. 1

65--86

Glossary of Terms extraction from textual requirements is an impor- tant step in ontology engineering methodologies. Although initially it was intended to be performed manually, last years have shown that some degree of automatization is possible. Based on these promising approaches, we introduce a novel, human inter- pretable, rule-based method named ReqTagger, which can extract candidates for ontology entities (classes or instances) and relations (data or object properties) from textual requirements automatically. We compare ReqTagger to existing automatic methods on an evaluation benchmark consisting of over 550 requirements and tagged with over 1700 entities and relations expected to be extracted. We discuss the quality of ReqTagger and provide details showing why it outperforms other methods. We also publish both the evaluation dataset and the implementation of ReqTagger.