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Concept Name Similarity Measure on SNOMED CT

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Knowledge and Systems Sciences (KSS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 780))

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Abstract

The semantic similarity measure between biomedical terms or concepts is a crucial task in biomedical information extraction and knowledge discovery. Most of the existing similarity approaches measure the similarity degree based on the path length between concept nodes as well as the depth of the ontology tree or hierarchy. These measures do not work well in case of the “primitive concepts” which are partially defined and have only few relations in the ontology structure. Namely, they cannot give the desired similarity results against human expert judge on the similarity among primitive concepts. In this paper, the existing two ontology-based measures are introduced and analyzed in order to determine their limitations with respect to the considered knowledge base. After that, a new similarity measure based on concept name analysis is proposed to solve the weakness of the existing similarity measures for primitive concepts. Using SNOMED CT as the input ontology, the accuracy of our proposal is evaluated and compared against other approaches with the human expert results based on different types of ontology concepts. Based on the correlation between the results of the evaluated measures and the human expert ratings, this paper analyzes the strength and weakness of each similarity measure for all ontology concepts.

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Acknowledgments

The first author is supported by Sirindhorn International Institute of Technology (SIIT) under an Excellent Foreign Student (EFS) scholarship. We are thankful to all those anonymous experts who spent their valuable time to evaluate the similarity between diseases.

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Authors and Affiliations

  1. School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Bangkok, Thailand

    Htet Htet Htun & Virach Sornlertlamvanich

Authors
  1. Htet Htet Htun
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  2. Virach Sornlertlamvanich
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Corresponding author

Correspondence to Htet Htet Htun .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Jian Chen

  2. Thammasat University, Bangkadi Muang Pathumthani, Thailand

    Thanaruk Theeramunkong

  3. National Electronics and Computer Technology Center, Pathumthani, Thailand

    Thepachai Supnithi

  4. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

    Xijin Tang

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Htun, H.H., Sornlertlamvanich, V. (2017). Concept Name Similarity Measure on SNOMED CT. In: Chen, J., Theeramunkong, T., Supnithi, T., Tang, X. (eds) Knowledge and Systems Sciences. KSS 2017. Communications in Computer and Information Science, vol 780. Springer, Singapore. https://doi.org/10.1007/978-981-10-6989-5_7

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  • DOI: https://doi.org/10.1007/978-981-10-6989-5_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6988-8

  • Online ISBN: 978-981-10-6989-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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