Abstract
The use of similarity measures in various domains is cornerstone for different tasks ranging from ontology alignment to information retrieval. To this end, existing metrics can be classified into several categories among which lexical and semantic families of similarity measures predominate but have rarely been combined to complete the aforementioned tasks. In this paper, we propose an original approach combining lexical and ontology-based semantic similarity measures to improve the evaluation of terms relatedness. We validate our approach through a set of experiments based on a corpus of reference constructed by domain experts of the medical field and further evaluate the impact of ontology evolution on the used semantic similarity measures.
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Cardoso, S. D., Da Silveira, M., Lin, Y. C., Christen, V., Rahm, E., Reynaud-Delaître, C., & Pruski, C. (2019). Combining semantic and lexical measures to evaluate medical terms similarity. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11371 LNBI, pp. 17–32). Springer Verlag. https://doi.org/10.1007/978-3-030-06016-9_2
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