Semantic data integration of big biomedical data for supporting personalised medicine

Abstract

Big biomedical data has grown exponentially during the last decades and a similar growth rate is expected in the next years. Likewise, semantic web technologies have also advanced during the last years, and a great variety of tools, e.g., ontologies and query languages, have been developed by different scientific communities and practitioners. Although a rich variety of tools and big data collections are available, many challenges need to be addressed in order to discover insights from which decisions can be taken. For instance, different interoperability conflicts can exist among data collections, data may be incomplete, and entities may be dispersed across different datasets. These issues hinder knowledge exploration and discovery, being thus required data integration in order to unveil meaningful outcomes. In this chapter, we address these challenges and devise a knowledge-driven framework that relies on semantic web technologies to enable knowledge exploration and discovery. The framework receives big data sources and integrates them into a knowledge graph. Semantic data integration methods are utilized for identifying equivalent entities, i.e., entities that correspond to the same real-world elements. Fusion policies enable the merging of equivalent entities inside the knowledge graph, as well as with entities in other knowledge graphs, e.g., DBpedia and Bio2RFD. Knowledge discovery allows for the exploration of knowledge graphs in order to uncover novel patterns and relations. As proof of concept, we report on the results of applying the knowledge-driven framework in the EU funded project iASiS (http://project-iasis.eu/) in order to transform big data into actionable knowledge, paving thus the way for personalised medicine.