Semantic Data Integration

The growing volume, variety and complexity of data being collected for scientific purposes presents challenges for data integration. For data to be truly useful, scientists need not only to be able to access it, but also be able to interpret and use it. Doing this requires semantic context. Semantic Data Integration is an active field of research, and this chapter describes the current challenges and how existing approaches are addressing them. The chapter then provides an overview of several active research areas within the semantic data integration field, including interactive and collaborative schema matching, integration of geospatial and biomedical data, and visualization of the data integration process. Finally, the need to move beyond the discovery of simple 1-to-1 equivalence matches to the identification of more complex relationships across datasets is presented and possible first steps in this direction are discussed. 1 An Important Challenge The world around us is an incredibly complex and interconnected system – one filled with phenomena that cannot be understood in isolation. At the same time, the volume and complexity of the data, theory, and models established to explain these phenomena have led scientists to specialize further and further, to the point where many researchers now spend their entire careers on extremely narrow topics, such as the characteristics of one particular class of star, or the habits of a single species of fish. While such specialization is important to increase humanity’s depth of knowledge about many subjects, some of the greatest leaps forward in our understanding come at the intersection of traditional scientific disciplines. These advances require Michelle Cheatham DaSe Laboratory, Wright State University, Ohio, USA, e-mail: [email protected] Catia Pesquita Universidade de Lisboa, Lisbon, Portugal e-mail: [email protected]