Ontology Learning for the Semantic Web
IEEE Intelligent Systems
Kernel methods for relation extraction
The Journal of Machine Learning Research
Unsupervised discovery of scenario-level patterns for Information Extraction
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
A novel use of statistical parsing to extract information from text
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
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ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
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COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
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ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
Unsupervised learning of semantic relations between concepts of a molecular biology ontology
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
RelExt: a tool for relation extraction from text in ontology extension
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
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ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
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WM'05 Proceedings of the Third Biennial conference on Professional Knowledge Management
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ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
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ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
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EKAW '08 Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns
Market Blended Insight: Modeling Propensity to Buy with the Semantic Web
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Ontology learning from text: A look back and into the future
ACM Computing Surveys (CSUR)
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We present an approach for relation extraction from texts aimed to enrich the semantic annotations produced by a semantic web portal. The approach exploits linguistic and empirical strategies, by means of a pipeline method involving processes such as a parser, part-of-speech tagger, named entity recognition system, pattern-based classification and word sense disambiguation models, and resources such as an ontology, knowledge base and lexical databases. With the use of knowledge intensive strategies to process the input data and corpus-based techniques to deal both with unpredicted cases and ambiguity problems, we expect to accurately discover most of the relevant relations for known and new entities, in an automated way.