Enabling semantic similarity estimation across multiple ontologies: An evaluation in the biomedical domain

• Published in: Journal of biomedical informatics Vol. 45; no. 1; pp. 141 - 155
• Main Authors: Sánchez, David, Solé-Ribalta, Albert, Batet, Montserrat, Serratosa, Francesc
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.02.2012
• Subjects: Algorithms; Medical Informatics - methods; Medical Subject Headings; MeSH; Multiple ontologies; Semantic similarity; Semantics; Unified Medical Language System; WordNet; MeSH; Semantic similarity; Multiple ontologies; WordNet
• ISSN: 1532-0464; 1532-0480; 1532-0480
• DOI: 10.1016/j.jbi.2011.10.005

[Display omitted] ► Methods to enable the estimation of semantic similarity between different ontologies are presented. ► They rely on the assessment of the semantic overlapping and structural similarity between ontological concepts. ► Evaluated with several edge-counting similarity measures, two biomedical benchmarks and MeSH and WordNet as ontologies. ► Results show a noticeable improvement in similarity accuracy compared to approaches based on terminological matchings. The estimation of the semantic similarity between terms provides a valuable tool to enable the understanding of textual resources. Many semantic similarity computation paradigms have been proposed both as general-purpose solutions or framed in concrete fields such as biomedicine. In particular, ontology-based approaches have been very successful due to their efficiency, scalability, lack of constraints and thanks to the availability of large and consensus ontologies (like WordNet or those in the UMLS). These measures, however, are hampered by the fact that only one ontology is exploited and, hence, their recall depends on the ontological detail and coverage. In recent years, some authors have extended some of the existing methodologies to support multiple ontologies. The problem of integrating heterogeneous knowledge sources is tackled by means of simple terminological matchings between ontological concepts. In this paper, we aim to improve these methods by analysing the similarity between the modelled taxonomical knowledge and the structure of different ontologies. As a result, we are able to better discover the commonalities between different ontologies and hence, improve the accuracy of the similarity estimation. Two methods are proposed to tackle this task. They have been evaluated and compared with related works by means of several widely-used benchmarks of biomedical terms using two standard ontologies (WordNet and MeSH). Results show that our methods correlate better, compared to related works, with the similarity assessments provided by experts in biomedicine.