Ontology-based vector space model and fuzzy query expansion to retrieve knowledge on medical computational problem solutions.
Medical Computational Problem (MCP) solving is related to medical problems and their computerized algorithmic solutions. In this paper, an extension of an ontology-based model to fuzzy logic is presented, as a means to enhance the information retrieval (IR) procedure in semantic management of MCPs. We present herein the methodology followed for the fuzzy expansion of the ontology model, the fuzzy query expansion procedure, as well as an appropriate ontology-based Vector Space Model (VSM) that was constructed for efficient mapping of user-defined MCP search criteria and MCP acquired knowledge. The relevant fuzzy thesaurus is constructed by calculating the simultaneous occurrences of terms and the term-to-term similarities derived from the ontology that utilizes UMLS (Unified Medical Language System) concepts by using Concept Unique Identifiers (CUI), synonyms, semantic types, and broader-narrower relationships for fuzzy query expansion. The current approach constitutes a sophisticated advance for effective, semantics-based MCP-related IR.
Bratsas
Charalampos
C
Lab. of Medical Informatics, Medical School, Aristotle University of Thessaloniki, 54124, P.O. Box 323, Thessaloniki, Greece. mpampis@med.auth.gr
Koutkias
Vassilis
V
Kaimakamis
Evangelos
E
Bamidis
Panagiotis
P
Maglaveras
Nicos
N
D001185:Artificial Intelligence; D003628:Database Management Systems; D016208:Databases, Factual; D020000:Decision Support Systems, Clinical; D017143:Fuzzy Logic; D007722:Health Knowledge, Attitudes, Practice; D016247:Information Storage and Retrieval / Q000379:methods*; D008954:Models, Biological; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods