Mediated Data Integration Systems Using Functional Dependencies Embedded in Ontologies

Abstract

The exponential growth of data sources over the Internet or in enterprise intranets requires the development of data integration methodologies and solutions to facilitate data access by offering uniform interface to end users. Data integration is facing two challenges: (1) management of source heterogeneity and (2) consolidation of the query results. To deal with the problem of heterogeneity, several research efforts proposed the use of ontologies to explicit semantic of sources. This explicitation of source semantic facilitates the resolution of different conflicts identified during the integration process. Once an integration system is built (using mediator architecture) it shall support user queries, by first identifying the relevant sources for a given query and then conciliating the result. To accomplish this task, two trends emerge in the current work: (1) the supposition that different entities of sources representing the same concept have the same key. This hypothesis is not always true in real applications due to the autonomy of sources. (2) The use of statistical methods to identify similar instances. For some applications like banking and engineering, precise integration solutions are needed. In this chapter, we propose an integration methodology for sources referencing shared domain ontology (called ontology-based database sources) with mediation architecture. Our ontology is enriched by functional dependencies defined in each ontology class. The presence of these functional dependencies allows the generation of the lists of candidate keys for each class. Therefore, each source can choose its keys from these lists. This gives more autonomy of sources and allows consolidation of the results in the absence of a common identifier. Our approach is validated using a set of ontology based database sources in Postgres DBMS, where all mediator components are formally described.