Information extraction as a basis for high-precision text classification
ACM Transactions on Information Systems (TOIS)
Natural language understanding (2nd ed.)
Natural language understanding (2nd ed.)
Communications of the ACM
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Information Retrieval Meets Gene Analysis
IEEE Intelligent Systems
Circle Graphs: New Visualization Tools for Text-Mining
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
A text-mining system for knowledge discovery from biomedical documents
IBM Systems Journal
RelEx---Relation extraction using dependency parse trees
Bioinformatics
Data & Knowledge Engineering
Promoting Insight-Based Evaluation of Visualizations: From Contest to Benchmark Repository
IEEE Transactions on Visualization and Computer Graphics
Kernel-based learning for biomedical relation extraction
Journal of the American Society for Information Science and Technology
Exploiting Gene Ontology to Conceptualize Biomedical Document Collections
ASWC '08 Proceedings of the 3rd Asian Semantic Web Conference on The Semantic Web
A Concept-Driven Automatic Ontology Generation Approach for Conceptualization of Document Corpora
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
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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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.