Biological data integration using Semantic Web technologies

TL;DR

This paper explores how Semantic Web technologies can be applied to integrate biological data, highlighting current capabilities and challenges in developing large-scale biological data applications.

Contribution

It demonstrates the feasibility of using Semantic Web technologies for biological data integration and discusses necessary improvements in tools and knowledge modeling.

Findings

Semantic Web technologies are increasingly mature for biological data integration

Current tools need performance improvements for large-scale applications

Effective knowledge modeling is crucial for successful biological data management

Abstract

Current research in biology heavily depends on the availability and efficient use of information. In order to build new knowledge, various sources of biological data must often be combined. Semantic Web technologies, which provide a common framework allowing data to be shared and reused between applications, can be applied to the management of disseminated biological data. However, due to some specificities of biological data, the application of these technologies to life science constitutes a real challenge. Through a use case of biological data integration, we show in this paper that current Semantic Web technologies start to become mature and can be applied for the development of large applications. However, in order to get the best from these technologies, improvements are needed both at the level of tool performance and knowledge modeling.