Knowledge-based integration of neuroscience data sources

The need for information integration is paramount in many biological disciplines, because of the large heterogeneity in both the types of data involved and in the diversity of approaches (physiological, anatomical, biochemical, etc.) taken by biologists to study the same or correlated phenomena. However, the very heterogeneity makes the task of information integration very difficult since two approaches studying different aspects of the same phenomena may not even share common attributes in their schema description. This paper develops a wrapper-mediator architecture which extends the conventional data- and view-oriented information mediation approach by incorporating additional knowledge-modules that bridge the gap between the heterogeneous data sources. The semantic integration of the disparate local data sources employs F-logic as a data and knowledge representation and reasoning formalism. We show that the rich object-oriented modeling features of F-logic together with its declarative rule language and the uniform treatment of data and metadata (schema information) make it an ideal candidate for complex integration tasks. We substantiate this claim by elaborating on our integration architecture and illustrating the approach using real world examples from the neuroscience domain. The complete integration framework is currently under development; a first prototype establishing the viability of the approach is operational.