Toward semantic understanding: an approach based on information extraction ontologies
ADC '04 Proceedings of the 15th Australasian database conference - Volume 27
Towards Ontology Generation from Tables
World Wide Web
Large scale colour ontology generation with XO
AOW '05 Proceedings of the 2005 Australasian Ontology Workshop - Volume 58
Transforming arbitrary tables into logical form with TARTAR
Data & Knowledge Engineering
AUTOMATIC DOMAIN ONTOLOGY GENERATION FROM WEB SITES
Journal of Integrated Design & Process Science
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ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
Web Semantics: Science, Services and Agents on the World Wide Web
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SOCASE'08 Proceedings of the 2008 AAMAS international conference on Service-oriented computing: agents, semantics, and engineering
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ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Survey on ontology learning from Web and open issues
ISIICT'09 Proceedings of the Third international conference on Innovation and Information and Communication Technology
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Proceedings of the sixth workshop on Ph.D. students in information and knowledge management
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We often need to access and reorganize information available in multiple tables in diverse Web pages. To understand tables, we rely on acquired expertise, background information, and practice. Current computerized tools seldom consider the structure and content in the context of other tables with related information. This paper addresses the table processing issue by developing a new framework to table understanding that applies an ontology-based conceptual modeling extraction approach to: (i) understand a table's structure and conceptual content to the extent possible; (ii) discover the constraints that hold between concepts extracted from the table; (iii) match the recognized concepts with ones from a more general specification of related concepts; and (iv) merge the resulting structure with other similar knowledge representations for use in future situations. The result will be a formalized method of processing the format and content of tables while incrementally building a relevant reusable conceptual ontology.