Inclusion dependencies, or INDs (which can say, for example, that every manager is an employee) are studied, including their interaction with functional dependencies, or FDs. A simple complete axiomatization for INDs is presented, and the decision problem for INDs is shown to be PSPACE-complete.(The decision problem for INDs is the problem of determining whether or not Z logically implies c, given a set Z of INDs and a single IND (T). It is shown that finite implication (implication over databases with a finite number of tuples) is the same as unrestricted implications for INDs, although finite implication and unrestricted implication are distinct for FDs and 1NDs taken together. It is shown that, although there are simple complete axiomatizations for FDs alone and for INDs alone, there is no complete axiomatization for FDs and INDs taken together, in which every rule is k-ary for some fixed k (and in particular, there is no finite complete axiomatization.) This is true whether we consider finite implication or unrestricted implication, and is true even if no relation scheme has more than three attributes. The nonexistence of a k-ary complete axiomatization for FDs and INDs taken together is proven by giving a condition which is necessary and sufficient in general for the existence of a k-ary complete axiomatization.

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Schema matching is a fundamental issue to many database applications, such as query mediation and data warehousing. It becomes a challenge when different vocabularies are used to refer to the same real-world concepts. In this context, a convenient approach, sometimes called extensional, instance-based or semantic, is to detect how the same real world objects are represented in different databases and to use the information thus obtained to match the schemas. Additionally, we argue that automatic approaches of schema matching should store provenance data about matchings. This paper describes an instance-based schema matching technique for an OWL dialect and proposes a data model for storing provenance data. The matching technique is based on similarity functions and is backed up by experimental results with real data downloaded from data sources found on the Web.