Enabling knowledge representation on the Web by extending RDF schema
Proceedings of the 10th international conference on World Wide Web
Constructing Biological Knowledge Bases by Extracting Information from Text Sources
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
A corpus-based approach to automatic compound extraction
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Extracting the names of genes and gene products with a hidden Markov model
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Data & Knowledge Engineering
Enhancing a biological concept ontology to fuzzy relational ontology with relations mined from text
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
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Biological information embedded within the large repository of unstructured or semi-structured text documents can be extracted more efficiently through effective semantic analysis of the texts in collaboration with structured domain knowledge. The GENIA corpus houses tagged MEDLINE abstracts, manually annotated according to the GENIA ontology, for this purpose. However, manual tagging of all texts is impossible and special purpose storage and retrieval mechanisms are required to reduce information overload for users. In this paper we have proposed an ontology-based biological Information Extraction and Query Answering (BIEQA) system that has four components: an ontology-based tag analyzer for analyzing tagged texts to extract Biological and lexical patterns, an ontology-based tagger for tagging new texts, a knowledge base enhancer which enhances the ontology, and incorporates new knowledge in the form of biological entities and relationships into the knowledge base, and a query processor for handling user queries.