Semantic integration of information through relation mining - Application to bio-medical text processing

Abstract

Semantic frameworks can be used to improve the accuracy and expressiveness of natural language processing for the purpose of extracting meaning from text documents. Such a framework represents knowledge using semantic networks and can be generated using information mined from text documents. The key issue however is to identify relevant concepts and their inter-relationships. In this paper, we have presented a scheme for semantic integration of information extracted from text documents. The extraction principle is based on linguistic and semantic analysis of text. Entities and relations are extracted using Natural Language Processing techniques. A method for collating information extracted from multiple sources to generate the semantic net is also presented. The efficacy of the proposed semantic framework is established through experiments carried out for visualizing information embedded in biomedical texts extracted from PubMed database. © Springer-Verlag Berlin Heidelberg 2007.