Biological relation extraction and query answering from MEDLINE abstracts using ontology-based text mining

  • Authors:

  • Muhammad Abulaish;Lipika Dey

  • Affiliations:

  • Department of Mathematics, Jamia Millia Islamia (A Central University), Jamia Nagar, New Delhi 110 025, India;Department of Mathematics, Indian Institute of Technology, Delhi, Hauz Khas, New Delhi 110 016, India

  • Venue:
  • Data & Knowledge Engineering
  • Year:
  • 2007

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Abstract

The rapid growth of the biological text data repository makes it difficult for human beings to access required information in a convenient and effective manner. The problem arises due to the fact that most of the information is embedded within unstructured or semi-structured text that computers cannot interpret very easily. In this paper we have presented an ontology-based Biological Information Extraction and Query Answering (BIEQA) System, which initiates text mining with a set of concepts stored in a biological ontology, and thereafter mines possible biological relations among those concepts using NLP techniques and co-occurrence-based analysis. The system extracts all frequently occurring biological relations among a pair of biological concepts through text mining. A mined relation is associated to a fuzzy membership value, which is proportional to its frequency of occurrence in the corpus and is termed a fuzzy biological relation. The fuzzy biological relations extracted from a text corpus along with other relevant information components like biological entities occurring within a relation, are stored in a database. The database is integrated with a query-processing module. The query-processing module has an interface, which guides users to formulate biological queries at different levels of specificity.