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A knowledge acquisition methodology to ontology construction for information retrieval from medical documents
Author(s) -
ValenciaGarcía Rafael,
FernándezBreis Jesualdo Tomás,
RuizMartínez Juana María,
GarcíaSánchez Francisco,
MartínezBéjar Rodrigo
Publication year - 2008
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2008.00464.x
Subject(s) - computer science , ontology , information retrieval , bottleneck , domain (mathematical analysis) , idef5 , natural language , knowledge acquisition , unified medical language system , natural language processing , upper ontology , semantic web , artificial intelligence , mathematical analysis , philosophy , mathematics , epistemology , embedded system
Abstract: Vast amounts of medical information reside within text documents, so that the automatic retrieval of such information would certainly be beneficial for clinical activities. The need for overcoming the bottleneck provoked by the manual construction of ontologies has generated several studies and research on obtaining semi‐automatic methods to build ontologies. Most techniques for learning domain ontologies from free text have important limitations. Thus, they can extract concepts so that only taxonomies are generally produced although there are other types of semantic relations relevant in knowledge modelling. This paper presents a language‐independent approach for extracting knowledge from medical natural language documents. The knowledge is represented by means of ontologies that can have multiple semantic relationships among concepts.