Dynamic iterative ontology learning

Christopher Brewster, José Iria, Ziqi Zhang, Fabio Ciravegna, Louise Guthrie, Yorick Wilks

Research output: Unpublished contribution to conferencePoster


The fundamental failure of current approaches to ontology learning is to view it as single pipeline with one or more specific inputs and a single static output. In this paper, we present a novel approach to ontology learning which takes an iterative view of knowledge acquisition for ontologies. Our approach is founded on three open-ended resources: a set of texts, a set of learning patterns and a set of ontological triples, and the system seeks to maintain these in equilibrium. As events occur which disturb this equilibrium, actions are triggered to re-establish a balance between the resources. We present a gold standard based evaluation of the final output of the system, the intermediate output showing the iterative process and a comparison of performance using different seed input. The results are comparable to existing performance in the literature.
Original languageEnglish
Publication statusPublished - 2007
Event6th International Conference on Recent Advances in Natural Language Processing - Borovets, Bulgaria
Duration: 27 Sept 200729 Sept 2007


Conference6th International Conference on Recent Advances in Natural Language Processing
Abbreviated titleRANLP-2007


  • failure
  • ontology learning
  • knowledge acquisition


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