Unsolvable problems related to the view integration approach

Convent, Bernhard

doi:10.1007/3-540-17187-8_34

Cited by 26 publications

(12 citation statements)

References 10 publications

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“…Semantic heterogeneity, a well-known obstacle to data source integration [10], is resolved through a process of semantic reconciliation, which matches concepts from heterogeneous data sources. Traditionally, semantic reconciliation was performed by a human observer (a designer, a DBA, or a user) [34,59] due to its complexity [10]. However, manual reconciliation (with or without computer-aided tools) tends to be slow and inefficient in dynamic environments and does not scale for obvious reasons.…”

Section: Introductionmentioning

confidence: 99%

A framework for modeling and evaluating automatic semantic reconciliation

et al. 2005

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Abstract. The introduction of the Semantic Web vision and the shift toward machine understandable Web resources has unearthed the importance of automatic semantic reconciliation. Consequently, new tools for automating the process were proposed. In this work we present a formal model of semantic reconciliation and analyze in a systematic manner the properties of the process outcome, primarily the inherent uncertainty of the matching process and how it reflects on the resulting mappings. An important feature of this research is the identification and analysis of factors that impact the effectiveness of algorithms for automatic semantic reconciliation, leading, it is hoped, to the design of better algorithms by reducing the uncertainty of existing algorithms. Against this background we empirically study the aptitude of two algorithms to correctly match concepts. This research is both timely and practical in light of recent attempts to develop and utilize methods for automatic semantic reconciliation.

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Section: Introductionmentioning

confidence: 99%

A framework for modeling and evaluating automatic semantic reconciliation

et al. 2005

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“…2 can be interpreted as a (weighted) sum (with λ = m) or a (weighted) average (with λ = 1) of the 5 Given σ 1 , . .…”

Section: Formalism Notation and Problem Statementmentioning

confidence: 99%

“…human experts [5], [28]. For obvious reasons, manual concept reconciliation in large scale and/or dynamic environments (with or without computer-aided tools) is inefficient and at times close to impossible.…”

Section: Introductionmentioning

confidence: 99%

Rank Aggregation for Automatic Schema Matching

Domshlak

Gal

Roitman

2007

IEEE Trans. Knowl. Data Eng.

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Abstract-Schema matching is a basic operation of data integration and several tools for automating it have been proposed and evaluated in the database community. Research in this area reveals that there is no single schema matcher that is guaranteed to succeed in finding a good mapping for all possible domains, and thus an ensemble of schema matchers should be considered. In this paper we introduce schema meta-matching, a general framework for composing an arbitrary ensemble of schema matchers, and generating a list of best-ranked schema mappings. Informally, schema meta-matching stands for computing a "consensus" ranking of alternative mappings between two schemata, given the "individual" graded rankings provided by several schema matchers. We introduce several algorithms for this problem, varying from adaptations of some standard techniques for general quantitative rank aggregation to novel techniques specific to the problem of schema matching, and to combinations of both. We provide a formal analysis of the applicability and relative performance of these algorithms, and evaluate them empirically on a set of real-world schemata.

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“…Due to the cognitive complexity of this matching process [18], it has traditionally been performed by human experts, such as web designers, database analysts, and even lay users, depending on the context of the application [79,47]. For obvious reasons, manual concept reconciliation in dynamic environments such as the web (with or without computer-aided tools) is inefficient to the point of being infeasible, and so cannot provide a general solution for semantic reconciliation.…”

Section: Introductionmentioning

confidence: 99%

Advances in Ontology Matching

Gal

Shvaiko

2008

Advances in Web Semantics I

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Matching of concepts describing the meaning of data in heterogeneous distributed information sources, such as database schemas and other metadata models, grouped here under the heading of an ontology, is one of the basic operations of semantic heterogeneity reconciliation. The aim of this chapter is to motivate the need for ontology matching, introduce the basics of ontology matching, and then discuss several promising themes in the area as reflected in recent research works. In particular, we focus on such themes as uncertainty in ontology matching, matching ensembles, and matcher self-tuning. Finally, we outline some important directions for future research.

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Unsolvable problems related to the view integration approach

Cited by 26 publications

References 10 publications

A framework for modeling and evaluating automatic semantic reconciliation

A framework for modeling and evaluating automatic semantic reconciliation

Rank Aggregation for Automatic Schema Matching

Advances in Ontology Matching

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