Invited Talk on "Matching Biomedical Ontologies for Semantic Data Integration."

Scientific research addressing complex life sciences phenomena must balance depth and breadth. Both specialization and interdisciplinarity are needed to support our understanding, which requires the integration of data from many different domains, while preserving detail, uncertainty, and above all the context of the data involved.

Semantic web technologies have been adopted by the life sciences community as an answer to this data heterogeneity problem, and notable efforts on semantic data modelling have become standards. After years of community engagement, ontologies now cover all domains of the life sciences and increasingly more data is exposed as Linked Open Data. This change in the life sciences data landscape presents opportunities to address more complex subjects, by allowing data to be integrated and analysed given its context. However, no single ontology can describe the multiple perspectives that are needed to understand complex biological phenomena. Tailored semantic networks that combine relevant ontologies are being used to support scientific data management and analysis in complex contexts.  The creation of such networks inevitably relies on ontology matching approaches that establish meaningful relations between ontology entities.

I will discuss the recent advances in matching biomedical ontologies, how these are being put into practice to support biomedical data integration and analysis, and chart future directions to address the challenges that will be faced by the semantic data integration community.