Towards the estimation of feature-based semantic similarity using multiple ontologies
A key application of ontologies is the estimation of the semantic similarity between terms. By means of this assessment, the comprehension and management of textual resources can be improved. However, most ontology-based similarity measures only support a single input ontology. If any of the compared terms do not belong to that ontology, their similarity cannot be assessed. To solve this problem, multiple ontologies can be considered. Even though there are methods that enable the multi-ontology similarity assessment by means of integrating concepts from different ontologies, most of them are based on simple terminological and/or partial matchings. This hampers similarity measures that exploit a broad set of taxonomic evidences of similarity, like feature-based ones. In this paper, we tackle this problem by proposing a method to identify all the suitable matchings between concepts of different ontologies that intervene in the similarity assessment. In addition to the obvious terminological matching, we exploit the ontological structure and the notion of concept subsumption to discover non-trivial equivalences between heterogeneous ontologies. Our final goal is to enable the accurate application of feature-based similarity measures in a multi-ontology setting. Our proposal is evaluated with regard human judgements of similarity for several benchmarks and ontologies. Results shows an improvement against related works, with similarity accuracies that even rival those obtained in an ideal mono-ontology setting.
feature-based semantic similarity multiple ontologies ontologies wordnet mesh