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

Mapping Chinese Medical Entities to the Unified Medical Language System

Background

Chinese medical entities have not been organized comprehensively due to the lack of well-developed terminology systems, which poses a challenge to processing Chinese medical texts for fine-grained medical knowledge representation. To unify Chinese medical terminologies, mapping Chinese medical entities to their English counterparts in the Unified Medical Language System (UMLS) is an efficient solution. However, their mappings have not been investigated sufficiently in former research. In this study, we explore strategies for mapping Chinese medical entities to the UMLS and systematically evaluate the mapping performance.

Methods

First, Chinese medical entities are translated to English using multiple web-based translation engines. Then, 3 mapping strategies are investigated: (a) string-based, (b) semantic-based, and (c) string and semantic similarity combined. In addition, cross-lingual pretrained language models are applied to map Chinese medical entities to UMLS concepts without translation. All of these strategies are evaluated on the ICD10-CN, Chinese Human Phenotype Ontology (CHPO), and RealWorld datasets.

Results

The linear combination method based on the SapBERT and term frequency-inverse document frequency bag-of-words models perform the best on all evaluation datasets, with 91.85%, 82.44%, and 78.43% of the top 5 accuracies on the ICD10-CN, CHPO, and RealWorld datasets, respectively.

Conclusions

In our study, we explore strategies for mapping Chinese medical entities to the UMLS and identify a satisfactory linear combination method. Our investigation will facilitate Chinese medical entity normalization and inspire research that focuses on Chinese medical ontology development.

Multi-domain semantic similarity in biomedical research

BACKGROUND

Given the increasing amount of biomedical resources that are being annotated with concepts from more than one ontology and covering multiple domains of knowledge, it is important to devise mechanisms to compare these resources that take into account the various domains of annotation. For example, metabolic pathways are annotated with their enzymes and their metabolites, and thus similarity measures should compare them with respect to both of those domains simultaneously.

RESULTS

In this paper, we propose two approaches to lift existing single-ontology semantic similarity measures into multi-domain measures. The aggregative approach compares domains independently and averages the various similarity values into a final score. The integrative approach integrates all the relevant ontologies into a single one, calculating similarity in the resulting multi-domain ontology using the single-ontology measure.

CONCLUSIONS

We evaluated the two approaches in a multidisciplinary epidemiology dataset by evaluating the capacity of the similarity measures to predict new annotations based on the existing ones. The results show a promising increase in performance of the multi-domain measures over the single-ontology ones in the vast majority of the cases. These results show that multi-domain measures outperform single-domain ones, and should be considered by the community as a starting point to study more efficient multi-domain semantic similarity measures.

A Semantic Information Content Based Method for Evaluating FCA Concept Similarity

Probability information content-based FCA concepts similarity computation method relies on the frequency of concepts in corpus, it takes only the occurrence probability as information content metric to compute FCA concept similarity, which leads to lower accuracy. This article introduces a semantic information content-based method for FCA concept similarity evaluation, in addition to the occurrence probability, it takes the superordinate and subordinate semantic relationship of concepts to measure information content, which makes the generic and specific degree of concepts more accurate. Then the semantic information content similarity can be calculated with the help of an ISA hierarchy which is derived from the domain ontology. The difference between this method and probability information content is that the evaluation of semantic information content is independent of corpus. Furthermore, semantic information content can be used for FCA concept similarity evaluation, and the weighted bipartite graph is also utilized to help improve the efficiency of the similarity evaluation. The experimental results show that this semantic information content based FCA concept similarity computation method improves the accuracy of probabilistic information content based method effectively without loss of time performance.