Related Experiment Video
Updated: Jul 10, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Ontology-based vector space model and fuzzy query expansion to retrieve knowledge on medical computational problem
Charalampos Bratsas1, Vassilis Koutkias, Evangelos Kaimakamis
1Lab. of Medical Informatics, Medical School, Aristotle University of Thessaloniki, 54124, P.O. Box 323, Thessaloniki, Greece. mpampis@med.auth.gr
Abstract:
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.
Related Concept Videos
Vectors in Space: Problem Solving
Mathematical Modeling: Problem Solving
Vector Functions and Motion: Problem Solving
Vector Calculus: Problem Solving
Pharmacokinetic Models: Overview
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal assumptions,...
