A Flexible Approach for Planning Schema Matching Algorithms

Duchateau, Fabien; Bellahsène, Zohra; Coletta, Rémi

doi:10.1007/978-3-540-88871-0_18

Cited by 39 publications

(31 citation statements)

References 27 publications

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“…On the other hand, non-linear classifiers such as decision trees [15] can indeed represent non-linear decision surfaces from a limited number of training examples, but are not inherently probabilistic, and the binary decisions output by them are not easy to use in the global assignment process that determines the entire mapping between two schemas from the pair-wise matches between their individual elements. Other probabilistic approaches to the automatic schema matching problem include the use of an attribute dictionary in the AU-TOMATCH system, where training examples of matching schemas are used to compile the dictionary, and candidate elements from new schemas are compared probabilistically to the dictionary.…”

Section: Related Workmentioning

confidence: 99%

Matcher Composition Methods for Automatic Schema Matching

Nikovski

Esenther

Xiang

et al. 2013

Enterprise Information Systems

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We address the problem of automating the process of deciding whether two data schema elements match (that is, refer to the same actual object or concept), and propose several methods for combining evidence computed by multiple basic matchers. One class of methods uses Bayesian networks to account for the conditional dependency between the similarity values produced by individual matchers that use the same or similar information, so as to avoid overconfidence in match probability estimates and improve the accuracy of matching. Another class of methods relies on optimization switches that mitigate this dependency in a domain-independent manner. Experimental results under several testing protocols suggest that the matching accuracy of the Bayesian composite matchers can significantly exceed that of the individual component matchers, and the careful selection of optimization switches can improve matching accuracy even further. Springer LinkThis work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Abstract. We address the problem of automating the process of deciding whether two data schema elements match (that is, refer to the same actual object or concept), and propose several methods for combining evidence computed by multiple basic matchers. One class of methods uses Bayesian networks to account for the conditional dependency between the similarity values produced by individual matchers that use the same or similar information, so as to avoid overconfidence in match probability estimates and improve the accuracy of matching. Another class of methods relies on optimization switches that mitigate this dependency in a domain-independent manner. Experimental results under several testing protocols suggest that the matching accuracy of the Bayesian composite matchers can significantly exceed that of the individual component matchers, and the careful selection of optimization switches can improve matching accuracy even further.

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Section: Related Workmentioning

confidence: 99%

Matcher Composition Methods for Automatic Schema Matching

Nikovski

Esenther

Xiang

et al. 2013

Enterprise Information Systems

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“…Also with increasing sizes of decision trees the performance drops significantly. Other learning techniques like MatchPlanner [7], YAM [6], [8] or [9] might not suffer that strongly from overfitting, but they do not consider schema features. MatchPlanner constructed decision trees from a given knowledge-base of correct mappings.…”

Section: Adaptive Selectionmentioning

confidence: 99%

A Self-Configuring Schema Matching System

Peukert

Eberius²,

Rahm

2012

2012 IEEE 28th International Conference on Data Engineering

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Abstract-Mapping complex metadata structures is crucial in a number of domains such as data integration, ontology alignment or model management. To speed up the generation of such mappings, automatic matching systems were developed to compute mapping suggestions that can be corrected by a user. However, constructing and tuning match strategies still requires a high manual effort by matching experts as well as correct mappings to evaluate generated mappings. We therefore propose a self-configuring schema matching system that is able to automatically adapt to the given mapping problem at hand. Our approach is based on analyzing the input schemas as well as intermediate matching results. A variety of matching rules use the analysis results to automatically construct and adapt an underlying matching process for a given match task. We comprehensively evaluate our approach on different mapping problems from the schema, ontology and model management domains. The evaluation shows that our system is able to robustly return good quality mappings across different mapping problems and domains.

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“…• MatchPlanner (University of Montpellier) (Duchateau et al, 2008). It uses a decision tree to combine the most appropriate similarity measures for a given domain.…”

Section: Existing Meta-matching Toolsmentioning

confidence: 99%

An overview of current ontology meta-matching solutions

Martinez‐Gil

Aldana‐Montes

2012

The Knowledge Engineering Review

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Nowadays there are a lot of techniques and tools for addressing the ontology matching problem, however, the complex nature of this problem means that the existing solutions are unsatisfactory. This work intends to shed some light on a more flexible way of matching ontologies using ontology meta-matching. This emerging technique selects appropriate algorithms and their associated weights and thresholds in scenarios where accurate ontology matching is necessary. We think that an overview of the problem and an analysis of the existing state-of-the-art solutions will help researchers and practitioners to identify the most appropriate specific features and global strategies in order to build more accurate and dynamic systems following this paradigm.

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A Flexible Approach for Planning Schema Matching Algorithms

Cited by 39 publications

References 27 publications

Matcher Composition Methods for Automatic Schema Matching

Matcher Composition Methods for Automatic Schema Matching

A Self-Configuring Schema Matching System

An overview of current ontology meta-matching solutions

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