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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

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Published on: February 23, 2019

Evaluating semantic similarity between Chinese biomedical terms through multiple ontologies with score normalization:

Wenxin Ning¹, Ming Yu¹, Dehua Kong¹

¹Health Care Services Research Center, Department of Industrial Engineering, Tsinghua University, Beijing 100084, China.

Journal of Biomedical Informatics

|November 5, 2016

Summary

This study introduces a novel method for estimating semantic similarity between Chinese biomedical terms. Score normalization enhances accuracy by addressing heterogeneity across multiple ontologies.

Keywords:

Chinese biomedicine Multiple ontologies Natural language processing Score normalization Semantic similarity

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Area of Science:

Computational linguistics
Medical informatics
Ontology engineering

Background:

Semantic similarity estimation is crucial for understanding biomedical resources and aiding medical decisions.
Existing research primarily focuses on English biomedical terms, leaving a gap in Chinese language resources.
China lacks a comprehensive public biomedical ontology, necessitating methods for utilizing multiple smaller, non-overlapping ones.

Purpose of the Study:

To explore semantic similarity estimation between Chinese biomedical terms using multiple non-overlapping ontologies.
To address the heterogeneity and comparability issues arising from using diverse ontologies.
To develop and evaluate a novel method for improving semantic similarity estimation accuracy in the Chinese biomedical domain.

Main Methods:

Applied path-based and information content (IC)-based similarity measures to multiple Chinese biomedical ontologies.
Identified heterogeneity in statistical distributions of similarity scores across different ontologies.
Developed a novel, language-independent method combining semantic similarity estimation and score normalization to mitigate heterogeneity.

Main Results:

The developed score normalization method demonstrated superior performance compared to existing task-independent methods, especially for IC-based measures.
Score normalization significantly enhanced the accuracy of semantic similarity estimation.
Mitigation of heterogeneity in similarity scores derived from multiple ontologies was achieved, improving overall reliability.

Conclusions:

Score normalization is essential for accurate ontology-based semantic similarity estimation, particularly when using multiple, non-overlapping resources.
The proposed method offers a viable solution for semantic similarity estimation in the Chinese biomedical domain.
Findings can be extended to other language systems for broader applications in biomedical natural language processing and text mining.