An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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Web Semantics: Science, Services and Agents on the World Wide Web
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Introduction to Information Retrieval
Cross ontology query answering on the semantic web: an initial evaluation
Proceedings of the fifth international conference on Knowledge capture
Text2Onto: a framework for ontology learning and data-driven change discovery
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
A approach to clinical proteomics data quality control and import
ITBAM'11 Proceedings of the Second international conference on Information technology in bio- and medical informatics
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The interpretation of a multiple-domain text corpus as a single ontology leads to misconceptions. This is because some concepts may be syntactically equal; though, they are semantically lopsided in different domains. Also, the occurrences of a domain concept in a large multiple-domain corpus may not gauge correctly the concept significance. This paper tackles the mentioned problems and proposes a novel ontology builder to extract separate domain specific ontologies from such a corpus. The builder contribution is to sustain each domain specific concepts and relations to get precise answers for user questions. We extend a single ontology builder named Text2Onto to apply our thought. We fruitfully enhance it to answer, more precisely, questions on a subset of AQUAINT corpus.