<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Mining Cardinality Restrictions in OWL

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Mining Cardinality Restrictions in OWL"}

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...

Mining Cardinality Restrictions in OWL

Online veröffentlicht: 18. Sept. 2020

Seitenbereich: 195 - 216

Eingereicht: 28. Feb. 2020

Akzeptiert: 27. Aug. 2020

DOI: https://doi.org/10.2478/fcds-2020-0011

Schlüsselwörter
Semantic Web, ontology learning, frequency estimation, kernel density estimation, cardinality restrictions

© 2020 Jedrzej Potoniec, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mining Cardinality Restrictions in OWL

Online veröffentlicht: 18. Sept. 2020

Seitenbereich: 195 - 216

Eingereicht: 28. Feb. 2020

Akzeptiert: 27. Aug. 2020

DOI: https://doi.org/10.2478/fcds-2020-0011

SchlüsselwörterSemantic Web, ontology learning, frequency estimation, kernel density estimation, cardinality restrictions

© 2020 Jedrzej Potoniec, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Semantic Web, ontology learning, frequency estimation, kernel density estimation, cardinality restrictions