Learning SKOS relations for terminological ontologies from text

Wei Wang, Payam M. Barnaghi, Andrzej Bargiela

Research output: Chapter in Book or Report/Conference proceedingChapterpeer-review

4 Citations (Scopus)


The problem of learning concept hierarchies and terminological ontologies can be divided into two subtasks: concept extraction and relation learning. The authors of this chapter describe a novel approach to learn relations automatically from unstructured text corpus based on probabilistic topic models. The authors provide definition (Information Theory Principle for Concept Relationship) and quantitative measure for establishing "broader" (or "narrower") and "related" relations between concepts. They present a relation learning algorithm to automatically interconnect concepts into concept hierarchies and terminological ontologies with the probabilistic topic models learned. In this experiment, around 7,000 ontology statements expressed in terms of "broader" and "related" relations are generated using different combination of model parameters. The ontology statements are evaluated by domain experts and the results show that the highest precision of the learned ontologies is around 86.6% and structures of learned ontologies remain stable when values of the parameters are changed in the ontology learning algorithm.

Original languageEnglish
Title of host publicationOntology Learning and Knowledge Discovery Using the Web
Subtitle of host publicationChallenges and Recent Advances
PublisherIGI Global
Number of pages24
ISBN (Print)9781609606251
Publication statusPublished - 2011
Externally publishedYes

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