Designing and refining schema mappings via data examples

Abstract

A schema mapping is a specification of the relationship between a source schema and a target schema. Schema mappings are fundamental building blocks in data integration and data exchange and, as such, obtaining the right schema mapping constitutes a major step towards the integration or exchange of data. Up to now, schema mappings have typically been specified manually or have been derived using mapping-design systems that automatically generate a schema mapping from a visual specification of the relationship between two schemas.We present a novel paradigm and develop a system for the interactive design of schema mappings via data examples. Each data example represents a partial specification of the semantics of the desired schema mapping. At the core of our system lies a sound and complete algorithm that, given a finite set of data examples, decides whether or not there exists a GLAV schema mapping (i.e., a schema mapping specified by Global-and-Local-As-View constraints) that "fits" these data examples. If such a fitting GLAV schema mapping exists, then our system constructs the "most general" one.We give a rigorous computational complexity analysis of the underlying decision problem concerning the existence of a fitting GLAV schema mapping, given a set of data examples. Specifically, we prove that this problem is complete for the second level of the polynomial hierarchy, hence, in a precise sense, harder than NP-complete. This worst-case complexity analysis notwithstanding, we conduct an experimental evaluation of our prototype implementation that demonstrates the feasibility of interactively designing schema mappings using data examples. In particular, our experiments show that our system achieves very good performance in real-life scenarios.

Incrementally improving dataspaces based on user feedback

“…AttV may also be 2 This form of feedback is called negative relevance feedback in the information retrieval literature [47]. retrieved from the sources in which case provenance = Map, where Map is the set of mappings that can be used to retrieve AttV from the sources.…”

“…We then select the mappings that maximize the recall with a given precision threshold λ p , i.e., the mappings that maximize the evaluation function eval recall in equation (2). To do this, we used NOMADm 10 , an implementation of the Mesh Adaptive Direct Search algorithm [1].…”

“…As well as the above proposals, a number of researchers investigated the issue of designing and refining schema mappings using data examples [2,6,22]. For example, Alexe et al [2,6] developed a system for generating mapping specifications based on data examples that the user provides.…”

A Graph-Based Approach to Learn Semantic Descriptions of Data Sources

“…There has been much research in schema mapping, from the seminal work on Clio [10], which provided a practical system and furthered the theoretical foundations of data exchange [11] to more recent systems that support additional schema constraints [17]. Alexe et al [1] generate schema mappings from examples of source data tuples and the corresponding tuples over the target schema. Karma [13] and An et al [2] generate mappings into ontologies, suggested by exploring low-cost Steiner trees that connect matching semantic types within a graph derived from the target ontology.…”

“…Although desirable, generating these descriptions automatically is a challenging problem [1,7,10,17,20]. Most of the proposed approaches on the Semantic Web focus on the first step of the modeling process.…”