Abstract:We demonstrated the potential necessity of score normalization when estimating semantic similarity using ontology-based measures. The results of this study can also be extended to other language systems to implement semantic similarity estimation in biomedicine.
“…As Ning et al discussed in their study [ 39 ], there is a variety of combination methods to integrate string and semantic similarities. Thus, we designed 4 different integration methods—the z-score, min-max, tanh, and linear combination—to integrate string and semantic similarity scores, which consider both string and semantic contributions to the similarity between queries and candidate concepts.…”
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.
“…As Ning et al discussed in their study [ 39 ], there is a variety of combination methods to integrate string and semantic similarities. Thus, we designed 4 different integration methods—the z-score, min-max, tanh, and linear combination—to integrate string and semantic similarity scores, which consider both string and semantic contributions to the similarity between queries and candidate concepts.…”
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.
“…Schliker & Albrecht [29] propose a similar methodology, where semantic similarity in the Gene Ontology (GO) is calculated independently for each of the three branches of this ontology, and then aggregated into a final similarity score. Ning et al [22] also use similar methods to aggregate similarity in each ontology into a final multi-ontology value. Fig.…”
Section: Methodsmentioning
confidence: 99%
“…Two recent papers have been presented that tackle them [22, 23]: Ning et al [23] propose and evaluate semantic similarity of biomedical terms using four Chinese ontologies using path-based measure of similarity, and Cheng et al [23] propose a gene-specific methodology to measure similarity between terms form different ontologies. However neither of those previous approaches is comparable to ours: Ning et al [22] propose a way to aggregate semantic similarity calculated with various ontologies. While the work is in principle very similar to ours, they only use path-based measures of similarity, which are known to suffer from various drawbacks, particularly in the biomedical field (see [24]).…”
Section: Introductionmentioning
confidence: 99%
“…Ning et al [22] propose a way to aggregate semantic similarity calculated with various ontologies. While the work is in principle very similar to ours, they only use path-based measures of similarity, which are known to suffer from various drawbacks, particularly in the biomedical field (see [24]).…”
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.
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