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Combining Semantic and Lexical Measures to Evaluate Medical Terms Similarity

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Data Integration in the Life Sciences (DILS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 11371))

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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.

This work is supported by FNR Luxembourg and DFG Germany through the ELISA project.

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Notes

  1. 1.

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Author information

Authors and Affiliations

  1. LIST, Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

    Silvio Domingos Cardoso, Marcos Da Silveira & Cédric Pruski

  2. LRI, University of Paris-Sud XI, Gif-sur-Yvette, France

    Silvio Domingos Cardoso & Chantal Reynaud-Delaître

  3. Department of Computer Science, Universität Leipzig, Leipzig, Germany

    Ying-Chi Lin, Victor Christen & Erhard Rahm

Authors
  1. Silvio Domingos Cardoso
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  2. Marcos Da Silveira
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  3. Ying-Chi Lin
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  4. Victor Christen
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  5. Erhard Rahm
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  6. Chantal Reynaud-Delaître
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  7. Cédric Pruski
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Correspondence to Silvio Domingos Cardoso .

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Editors and Affiliations

  1. TIB and Leibniz University, Hannover, Germany

    Sören Auer

  2. TIB and Leibniz University, Hannover, Germany

    Maria-Esther Vidal

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Cardoso, S.D. et al. (2019). Combining Semantic and Lexical Measures to Evaluate Medical Terms Similarity. In: Auer, S., Vidal, ME. (eds) Data Integration in the Life Sciences. DILS 2018. Lecture Notes in Computer Science(), vol 11371. Springer, Cham. https://doi.org/10.1007/978-3-030-06016-9_2

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  • DOI: https://doi.org/10.1007/978-3-030-06016-9_2

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