A semantic similarity method based on information content exploiting multiple ontologies

The quantification of the semantic similarity between terms is an important research area that configures a valuable tool for text understanding. Among the different paradigms used by related works to compute semantic similarity, in recent years, information theoretic approaches have shown promising results by computing the information content (IC) of concepts from the knowledge provided by ontologies. These approaches, however, are hampered by the coverage offered by the single input ontology. In this paper, we propose extending IC-based similarity measures by considering multiple ontologies in an integrated way. Several strategies are proposed according to which ontology the evaluated terms belong. Our proposal has been evaluated by means of a widely used benchmark of medical terms and MeSH and SNOMED CT as ontologies. Results show an improvement in the similarity assessment accuracy when multiple ontologies are considered.

► A method to compute IC-based semantic similarity from multiple ontologies is presented.
► Strategies to integrate both overlapping and disjoint ontologies are proposed.
► Evaluation has been performed with a standard benchmark and widely used medical ontologies.
► Results show an improvement in accuracy when multiple ontologies are considered.