We describe eTuner, an approach to automatically tune schema matching systems.
Tuning is skill and time intensive, but (as we show) without it the matching accuracy is significantly inferior. The domain user mustthen tune the system: select the right component to be executed and correctly adjust their numerous “knobs” (e.g., thresholds, formula coefficients). Most recent schema matching systems assemble multiple components, each employing a particular matching technique. Finally, COMA++ cannot only be used to solve match problems but also to comparatively evaluate the effectiveness of different match algorithms and strategies. Furthermore, different match strategies can be applied including various forms of reusing previously determined match results and a so-called fragment-based match approach which decomposes a large match problem into smaller problems.
COMA++ includes new approaches for ontology matching, in particular the utilization of shared taxonomies. the powerful standard languages W3C XML Schema and OWL. Using a generic data representation, COMA++ uniformly supports schemas and ontologies, e.g. It comes with a graphical interface enabling a variety of user interactions. COMA++ implements significant improvements and offers a comprehensive infrastructure to solve large real-world match problems. It extends our previous prototype COMA utilizing a composite approach to combine different match algorithms. We demonstrate the schema and ontology matching tool COMA++. More precisely, it appears that Approxivect, when its parameters are tuned in optimum configurations, discovers most of the relevant couples in the top ranking while COMA++ only finds half of the mappings. Finally, we have performed experiments showing that our tool provides good results regarding those provided by COMA++. Another important feature of our tool is that our method does not use any dictionary or language-based knowledge and works in specialized domain areas. Furthermore, a tool has been implemented, Approxivect, based on the approximation of terminological methods and on the cosine measure between context vectors. In this article we present an automatic method to calculate the similarity measure between two schema elements.
Etuner pdf manual#
Unfortunately, most of the tools used nowadays to discover those mappings are either manual or semi-automatic. The possibility to query heterogeneous and semantically linked data sources depends on the ability to find correspondences between their structure and/or their content. The second advantage is the improvement of the quality of matches. For this purpose, for a given domain, only the most suitable match algorithms are used from a large library of match algorithms. As a first consequence of using the decision tree, the performance of the system is improved since the complexity is bounded by the height of the decision tree. Thus, the matching engine makes use of a decision tree to combine most appropriate match algorithms. Unlike other composite matchers, it is able to learn the most appropriate match algorithms for a given schema matching scenario. In this paper, we present a novel method for combining schema matching algorithms, which enables to avoid these drawbacks. However, this aggregation entails several drawbacks on the performance, quality and tuning aspects. The quality of matches depends on the adequacy and of the number of match algorithms used, and their combination and aggregation strategy. To improve the matching accuracy, most of the schema matching tools aggregate the results obtained by several matching algorithms.
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