Exploring (Bio)Medical Knowledge using Linked Data

Developing an effective model for predicting risks of a disease requires exploration of a vast body of (bio)medical knowledge. Furthermore, the continuous growth of this body of knowledge poses extra challenges. Numerous research has attempted to address these issues through developing a variety of approaches and support tools. Most of these tools however, do not sufficiently address the needed dynamism, lack intuitiveness in their use, and present a rather scarce amount of information usually obtained from a single source. This research aims to address the aforementioned gaps through the development of a dynamic model for (bio)medical knowledge, represented as a network of interrelated (bio)medical concepts, and integrating disperse sources. To this end, this paper introduces BioMed Xplorer, presenting a model and a tool that enables researchers to explore biomedical knowledge, organized in an information graph, through a user friendly and intuitive interface. Furthermore, BioMed Xplorer provides concept related information from a multitude of sources, while also preserving and presenting their provenance data. For this purpose a RDF knowledge base has been created based on a core ontology which we have introduced. Results are further experimented with and validated by some domain experts and are contrasted against the state of the art.