Abstract
A number of ontologies have been recently developed in order to represent common knowledge in a structured manner. This allows users and agents involved in a particular domain to make inquiries and discover the underlying conceptual differences present in the data. However, currently the majority of ontology construction tools are heavily dependent on the human domain experts for selecting concepts and defining their relationships. In this paper, we would like to present a new tool called ACORN, which implements novel techniques for automatically extracting concepts and building concept-to-concept relationships. We first utilize the WordNet lexical database and term co-occurrence frequency for discovering domain specific concepts and introduce ‘cluster mapping’ and ‘generality ordering’ techniques for connecting these concepts. We apply our techniques to a widely available dataset and show that ACORN is able to produce high quality ontologies.
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Bae, E., Vasudevan, B.G., Balakrishnan, R. (2008). ACORN : Towards Automating Domain Specific Ontology Construction Process. In: Zhang, Y., Yu, G., Bertino, E., Xu, G. (eds) Progress in WWW Research and Development. APWeb 2008. Lecture Notes in Computer Science, vol 4976. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78849-2_49
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DOI: https://doi.org/10.1007/978-3-540-78849-2_49
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