Extraction and Exploitation of Intensional Knowledge from Heterogeneous Information Sources: Semi-Automatic Approaches and Tools

The problem of integrating multiple information sources into a uni?ed data store is currently one of the most important challenges in data management. Within the ?eld of source integration, the problem of automatically gen- ating an integrated description of the data sources is surely one of the most relevant. The signi?cance of the issue can be best understood if one c- siders the huge number of information sources that an organization has to integrate. Indeed, it is even impossible to try to do all the work by hand. Like other important issues in data management, the problem of integrating multiple data sources into a unique global system has several facets, each of which represents, ¿per se¿, an interesting research problem, and comprises, for instance, that of recognizing, at the intensional level, similarities and dissimilarities among scheme objects, that of resolving representation m- matches among schemes, and that of deciding how to obtain an integrated data store out of a set of input sources and of a semantic description of their contents. The research and application relevance of such issues has attracted wide interest in the database community in recent years. And, as a con- quence, several techniques have been presented in the literature attacking one side or another of this complex and multifarious problem.