Discovering Subsumption Hierarchies of Ontology Concepts from Text Corpora

• Published in: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence pp. 402 - 408
• Main Authors: Zavitsanos, Elias, Paliouras, Georgios, Vouros, George A., Petridis, Sergios
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 02.11.2007
• Series: ACM Conferences
• Subjects: Applied computing > Document management and text processing; Applied computing > Document management and text processing > Document capture > Document analysis; Computing methodologies > Machine learning; Information systems > Information retrieval; Information systems > Information systems applications > Data mining; Theory of computation > Models of computation > Probabilistic computation
• ISBN: 0769530265; 9780769530260
• DOI: 10.1109/WI.2007.47

This paper proposes a method for learning ontologies given a corpus of text documents. The method identifies concepts in documents and organizes them into a subsumption hierarchy, without presupposing the existence of a seed ontology. The method uncovers latent topics in terms of which document text is being generated. These topics form the concepts of the new ontology. This is done in a language neutral way, using probabilistic space reduction techniques over the original term space of the corpus. Given multiple sets of concepts (latent topics) being discovered, the proposed method constructs a subsumption hierarchy by performing conditional independence tests among pairs of latent topics, given a third one. The paper provides experimental results over the GENIA corpus from the domain of biomedicine.