Semantic Data Integration on Biomedical Data Using Semantic Web Technologies

Contemporary life sciences research requires an understanding of systems across wide ranges of scale and distribution. Therefore, there is an urgent need to integrate biomedical knowledge generated by different communities and separate subfields (Shadbolt et al., 2006). Scientific publications and curated databases together hold a vast amount of this useable knowledge. Additionally the number, size, and complexity of life science databases continues to grow (Kei-Hoi et al., 2009). Therefore scientists in the field of genomics, proteomics, metabolomics, clinical medicine and drug discovery need a concept to integrate their data, (Shadbolt et al., 2006) which is a prominent problem (Kei-Hoi et al., 2009). But to generate such a uniform data integration concept there are still some challenges to overcome such as handling the variety and amount of available data, inconsistency with data heterogeneity from the different sources, the autonomy and differing capabilities of the sources and a lack of standards for such an integration concept. Many heterogeneity conflicts remain in data integration due to the lack of semantics (Gagnon, 2007). In order, to efficiently exploit the knowledge from different resources, it will be important to connect the sources in a manner that machine processes can traverse and intelligently identify these links (Neumann et al., 2004). A promising approach to integrate heterogeneous data sources could be the use of Semantic Web technologies. They provide a framework to deal with the afore mentioned problems and fulfil the requirements for machine processing. This book chapter provides an overview of data integration on biomedical data using Semantic Web technologies including existing techniques (standards, specifications and methods), challenges, approaches and projects.