Conventional information retrieval models, such as Boolean model, vector model, fuzzy set model, and extended Boolean model, have not exploited knowledge from hierarchical thesaurus directly for the query evaluation process. Hierarchical thesaurus represents hierarchical relationships between index terms. With an inference mechanism utilizing inter-term relationships in hierarchical thesaurus, we can improve the retrieval effectiveness of information retrieval system.
This thesis discusses a knowledge based information retrieval model with hierarchical thesaurus. The model computes the conceptual distance between a query and an object, both are indexed with weighted terms from a hierarchical thesaurus. The hierarchical thesaurus is represented by a Hierarchical-Concept Graph (HCG) in which nodes represent concepts and directed edges represent "generalization" relationships.
Rada et al. have developed a similar model. However, their model considered only a binary indexing scheme and revealed some counter-intuitive results. The proposed model extends theirs to allow the index term and the edge of HCG to be weighted. A new concept mapping method is devised to overcome Rada``s counter-intuitive results. In addition, a scheme for allowing Boolean operators in user queries is provided with a formula for computing conceptual distance from negated index terms.
Experimental results have shown that the model simulates human performance more closely than Rada``s model. The application to the Common LISP library system reveals that the proposed model is attractive for practical applications.