Research of Semantic Similarity Algorithm Based on the Knowledge of Medical Domain

DOI

10.2991/cnct-16.2017.87How to use a DOI?

Keywords

Semantic Similarity, Semantic Correlation, SNOMED CT, The Knowledge Of Medical Domain.

Abstract

With the development of medical information technology, the research and application of medical big data has become an important direction of data research. The semantic similarity evaluation between medical domain knowledge is an important part of the understanding of medical large data, which can effectively promote the processing, classification and structured processing of medical resources. In medical domain knowledge, the similarity calculation can improve the performance of information retrieval of medical resources and effectively promote the integration of heterogeneous clinical data. Based on the analysis of semantic similarity and semantic correlation algorithm and combined with the characteristics of medical psychology knowledge, the paper introduces the concept of weight value to simulate the characteristics of human psychological quantity, and gives the medical domain knowledge semantic similarity calculation method. Finally adding the semantic structure model with Oxford Centre for Tropical Forests(OCTF) similarity, constitute OCTF similarity calculation model based on the semantic, and formulas are given. By using Systematized Nomenclature of Medicine -- Clinical Terms(SNOMED CT) as the input ontology, the accuracy and usability of the algorithm are verified by the evaluation standard of medical terminology.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Li-quan HAN
AU  - Zheng-chao XU
AU  - Xiao-bo WANG
PY  - 2016/12
DA  - 2016/12
TI  - Research of Semantic Similarity Algorithm Based on the Knowledge of Medical Domain
BT  - Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)
PB  - Atlantis Press
SP  - 633
EP  - 638
SN  - 2352-538X
UR  - https://doi.org/10.2991/cnct-16.2017.87
DO  - 10.2991/cnct-16.2017.87
ID  - HAN2016/12
ER  -