Semantic-Based Indexing Approaches for Medical Document Clustering Using Cognitive Search

Semantic-Based Indexing Approaches for Medical Document Clustering Using Cognitive Search

Logeswari Shanmugam (Bannari Amman Institute of Technology, India) and Premalatha K. (Bannari Amman Institute of Technology, India)
DOI: 10.4018/978-1-5225-7522-1.ch003

Abstract

Biomedical literature is the primary repository of biomedical knowledge in which PubMed is the most absolute database for collecting, organizing and analyzing textual knowledge. The high dimensionality of the natural language text makes the text data quite noisy and sparse in the vector space. Hence, the data preprocessing and feature selection are important processes for the text processing issues. Ontologies select the meaningful terms semantically associated with the concepts from a document to reduce the dimensionality of the original text. In this chapter, semantic-based indexing approaches are proposed with cognitive search which makes use of domain ontology to extract relevant information from big and diverse data sets for users.
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Literature Review

Medical document analysis is one of the innovative fields with remarkable research potential. It employs with the extraction of novel, significant information from the huge quantity of biomedical associated documents. The substantial amount of biomedical text offers a comfortable source of knowledge for biomedical research.

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