| Semantic similarity techniques are used to compute the semantic similarity (common shared information) between two concepts according to certain language or domain resources like ontologies, taxonomies, corpora, etc. Semantic similarity techniques constitute important components in most Information Retrieval and knowledge-based systems. This thesis presents new techniques for measuring the semantic similarity between concepts based on ontology. The proposed measures are based on three features: (1) (cross-modified) path length, (2) common specificity of concepts in ontology, (3) local granularity of clusters. The new features of common specificity and granularity are applied in computing semantic similarity of concepts in single or across multiple ontologies. The key contribution is the novel cross-ontology approach for measuring similarity of concepts dispersed in multiple ontologies in a unified framework. The proposed techniques were evaluated extensively in the biomedical domain and general English domain. The experimental results proved the effectiveness and superiority of our similarity measures compared with the existing similar techniques. |
