A concept-driven biomedical knowledge extraction and visualization framework for conceptualization of text corpora

• Published in: Journal of biomedical informatics Vol. 43; no. 6; pp. 1020 - 1035
• Main Authors: Jahiruddin, Abulaish, Muhammad, Dey, Lipika
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.12.2010
• Subjects: Abstracting and Indexing as Topic - methods; Algorithms; Biological relation extraction; Biological text mining; Biomedical knowledge extraction and visualization; Biomedical query answering; Information Storage and Retrieval - methods; Natural Language Processing; Semantic network; Semantics; Biomedical knowledge extraction and visualization; Biological relation extraction; Biomedical query answering; Biological text mining; Semantic network
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2010.09.008

A number of techniques such as information extraction, document classification, document clustering and information visualization have been developed to ease extraction and understanding of information embedded within text documents. However, knowledge that is embedded in natural language texts is difficult to extract using simple pattern matching techniques and most of these methods do not help users directly understand key concepts and their semantic relationships in document corpora, which are critical for capturing their conceptual structures. The problem arises due to the fact that most of the information is embedded within unstructured or semi-structured texts that computers can not interpret very easily. In this paper, we have presented a novel Biomedical Knowledge Extraction and Visualization framework, BioKEVis to identify key information components from biomedical text documents. The information components are centered on key concepts. BioKEVis applies linguistic analysis and Latent Semantic Analysis (LSA) to identify key concepts. The information component extraction principle is based on natural language processing techniques and semantic-based analysis. The system is also integrated with a biomedical named entity recognizer, ABNER, to tag genes, proteins and other entity names in the text. We have also presented a method for collating information extracted from multiple sources to generate semantic network. The network provides distinct user perspectives and allows navigation over documents with similar information components and is also used to provide a comprehensive view of the collection. The system stores the extracted information components in a structured repository which is integrated with a query-processing module to handle biomedical queries over text documents. We have also proposed a document ranking mechanism to present retrieved documents in order of their relevance to the user query.