Data integration with uncertainty

Abstract

This paper reports our first set of results on managing uncertainty in data integration. We posit that data-integration systems need to handle uncertainty at three levels, and do so in a principled fashion. First, the semantic mappings between the data sources and the mediated schema may be approximate because there may be too many of them to be created and maintained or because in some domains (e.g., bioinformatics) it is not clear what the mappings should be. Second, queries to the system may be posed with keywords rather than in a structured form. Third, the data from the sources may be extracted using information extraction techniques and so may yield erroneous data. As a first step to building such a system, we introduce the concept of probabilistic schema mappings and analyze their formal foundations. We show that there are two possible semantics for such mappings: by-table semantics assumes that there exists a correct mapping but we don't know what it is; by-tuple semantics assumes that the correct mapping may depend on the particular tuple in the source data. We present the query complexity and algorithms for answering queries in the presence of approximate schema mappings, and we describe an algorithm for efficiently computing the top-k answers to queries in such a setting.

A Functional Model for Dataspace Management Systems

“…PayGo supports keyword searches but reformulates them into queries that attempt to identify relevant sources using the clusters previously built. UDI [17,23,24,60] is a dataspace proposal for integration of a large number of domain independent data sources automatically. In contrast to the proposals introduced so far, which either start with a manually defined integration schema or use the union of all source schemas as integration schema, UDI aims to derive a merged integration schema automatically, consolidating schema and instance references.…”

Ten Challenges for Ontology Matching

“…The issue of dealing with uncertainty in ontology matching has been addressed in [8,16,28,29,53,63]. A way of modeling ontology matching as an uncertain process is by using similarity matrices as a measure of certainty.…”

“…In [28], uncertainty is refined by a comparison of K alignments, each with its own uncertainty measure (modeled as a fuzzy relation over the two ontologies) in order to improve precision of the matching results. Finally, the work in [16] introduced the notion of probabilistic schema mappings (correspondences), namely a set of mappings with a probability attached to each mapping; and, used it to answer queries with uncertainty about semi-automatically created mappings. Imprecise mappings can be further improved over time as deemed necessary, for example, within the settings of approximate data integration, see, e.g., [72].…”

An Uncertain Data Integration System

“…For building such a system, we take advantage of the background knowledge which is implied in the functional dependencies (FDs) defined on the schemas. Since probabilistic data models have shown to be promising [3][4][5], we build our approach on a probabilistic data model to capture the uncertainty which arises during the schema matching process. Therefore, we generate a set of Probabilistic Mediated Schemas (PMSs).…”