A-LIOn - Alignment Learning through Inconsistency negatives of the aligned Ontologies

Sarah M. Alghamdi, Fernando Zhapa-Camach, Robert Hoehndorf

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

Ontologies play an important role in sharing and reusing knowledge. Several ontologies have been developed to describe a particular domain but from different perspectives from communities of developers and users. This has led to the existence of multiple ontologies covering the same or a different domain with varying degrees of variability. Ontology Alignment is typically used to identify correspondences between semantically related elements of two or more ontologies in order to address this problem. We propose A-LIOn a system that learns alignments by combining lexical and semantic approaches as well as machine learning. The system utilizes OWL EL reasoning for negative sampling which is iteratively used to inform the correction of the learning of the alignments. We demonstrate that A-LIOn produces alignments that are coherent with respect to OWL EL.

Original languageEnglish (US)
Pages137-144
Number of pages8
StatePublished - 2022
Event17th International Workshop on Ontology Matching, OM 2022 - Virtual, Online, China
Duration: Oct 23 2022 → …

Conference

Conference17th International Workshop on Ontology Matching, OM 2022
Country/TerritoryChina
CityVirtual, Online
Period10/23/22 → …

Keywords

  • Inconsistency negatives
  • Ontology Alignments
  • Ontology matching

ASJC Scopus subject areas

  • General Computer Science

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