Automated Ontology Evaluation: Evaluating Coverage and Correctness using a Domain Corpus

Antonio Zaitoun, Tomer Sagi, Katja Hose

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

2 Citations (Scopus)
2 Downloads (Pure)


Ontologies conceptualize domains and are a crucial part of web semantics and information systems. However, re-using an existing ontology for a new task requires a detailed evaluation of the candidate ontology as it may cover only a subset of the domain concepts, contain information that is redundant or misleading, and have inaccurate relations and hierarchies between concepts. Manual evaluation of large and complex ontologies is a tedious task. Thus, a few approaches have been proposed for automated evaluation, ranging from concept coverage to ontology generation from a corpus. Existing approaches, however, are limited by their dependence on external structured knowledge sources, such as a thesaurus, as well as by their inability to evaluate semantic relationships. In this paper, we propose a novel framework to automatically evaluate the domain coverage and semantic correctness of existing ontologies based on domain information derived from text. The approach uses a domain-tuned named-entity-recognition model to extract phrasal concepts. The extracted concepts are then used as a representation of the domain against which we evaluate the candidate ontology’s concepts. We further employ a domain-tuned language model to determine the semantic correctness of the candidate ontology’s relations. We demonstrate our automated approach on several large ontologies from the oceanographic domain and show its agreement with a manual evaluation by domain experts and its superiority over the state-of-the-art.
Original languageEnglish
Title of host publicationACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023
Number of pages11
PublisherAssociation for Computing Machinery
Publication date30 Apr 2023
ISBN (Electronic)9781450394161
Publication statusPublished - 30 Apr 2023
EventThe ACM Web Conference 2023 - Austin, United States
Duration: 30 Apr 20234 May 2023


ConferenceThe ACM Web Conference 2023
Country/TerritoryUnited States


  • BERT
  • knowledge engineering
  • natural language processing
  • ontology


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