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FQAS '02 Proceedings of the 5th International Conference on Flexible Query Answering Systems
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OTM'07 Proceedings of the 2007 OTM Confederated international conference on On the move to meaningful internet systems: CoopIS, DOA, ODBASE, GADA, and IS - Volume Part I
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ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
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NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
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ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
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ISWC '09 Proceedings of the 8th International Semantic Web Conference
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RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
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Natural Computing: an international journal
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NLDB'11 Proceedings of the 16th international conference on Natural language processing and information systems
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Most ontology learning tools concentrate on extracting concepts and instances from text corpora. There are some recent tools that employ linguistics or data mining to uncover concept relationships, but the results are mixed. Since relationships are semantically complex notions, it seems interesting to combine approaches that address different aspects of concept relationships. In this paper we present a hybrid approach that combines the co-occurrence principle from association rules with contextual similarities from linguistics. The technique has been tested in an ontology engineering project, and the results show significant improvements over traditional techniques.