Data Integration for Supporting Biomedical Knowledge Graph Creation at Large-Scale

TL;DR

This paper introduces ConMap, a scalable semantic integration method that leverages ontologies to simultaneously perform curation, mapping, and integration, significantly reducing knowledge graph creation time in biomedical data.

Contribution

ConMap is a novel approach that uses ontology-encoded knowledge to perform multiple integration tasks concurrently, improving efficiency over traditional methods.

Findings

Reduces knowledge graph creation time by up to 70%

Effective on various biomedical datasets

Scalable for large-scale data integration

Abstract

In recent years, following FAIR and open data principles, the number of available big data including biomedical data has been increased exponentially. In order to extract knowledge, these data should be curated, integrated, and semantically described. Accordingly, several semantic integration techniques have been developed; albeit effective, they may suffer from scalability in terms of different properties of big data. Even scaled-up approaches may be highly costly because tasks of semantification, curation and integration are performed independently. In order to overcome these issues, we devise ConMap, a semantic integration approach which exploits knowledge encoded in ontology in order to describe mapping rules to perform these tasks at the same time. Experimental results performed on different data sets suggest that ConMap can significantly reduce the time required for knowledge graph creation by up to 70\% of the time that is consumed following a traditional approach.