Multi-domain semantic similarity in biomedical research

João D Ferreira,Francisco M Couto

doi:10.1186/s12859-019-2810-9

Abstract

BackgroundGiven 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.ResultsIn 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.ConclusionsWe 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.

Highlights

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
With the increase in the amount of biomedical domains being represented in formal ontologies, the desire to use ontologies to annotate biomedical entities increases, which resulted in multiple ontologies being used to that effect: metabolic pathways [2, 3], mathematical models of biological processes [4], functional tissue units [5], epidemiological resources [6], biomedical text and clinical notes [7], chemical toxicity [8] etc
The results obtained with this dataset are meant to achieve two goals: (a) we show that the proposed approaches to the multi-domain similarity problem are effective, at least in comparison with the single-ontology counterpart; and (b) we hope to stimulate the community to think about the problematic of multidisciplinary similarity surrounding the ideas of knowledge representation and ontologies in the biomedical domain

Summary

Introduction

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. With the increase in the amount of biomedical domains being represented in formal ontologies, the desire to use ontologies to annotate biomedical entities increases, which resulted in multiple ontologies being used to that effect: metabolic pathways [2, 3], mathematical models of biological processes [4], functional tissue units [5], epidemiological resources [6], biomedical text and clinical notes [7], chemical toxicity [8] etc These multidisciplinary entities, along with their multi-ontology annotations, can be regarded as biomedical digital resources that describe complex real-world phenomena

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Discussion

Conclusion