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YAM: A Step Forward for Generating a Dedicated Schema Matcher

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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXV

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 9620))

Abstract

Discovering correspondences between schema elements is a crucial task for data integration. Most schema matching tools are semi-automatic, e.g., an expert must tune certain parameters (thresholds, weights, etc.). They mainly use aggregation methods to combine similarity measures. The tuning of a matcher, especially for its aggregation function, has a strong impact on the matching quality of the resulting correspondences, and makes it difficult to integrate a new similarity measure or to match specific domain schemas. In this paper, we present YAM (Yet Another Matcher), a matcher factory which enables the generation of a dedicated schema matcher for a given schema matching scenario. For this purpose we have formulated the schema matching task as a classification problem. Based on this machine learning framework, YAM automatically selects and tunes the best method to combine similarity measures (e.g., a decision tree, an aggregation function). In addition, we describe how user inputs, such as a preference between recall or precision, can be closely integrated during the generation of the dedicated matcher. Many experiments run against matchers generated by YAM and traditional matching tools confirm the benefits of a matcher factory and the significant impact of user preferences.

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Notes

  1. 1.

    The name of the tool refers to a discussion during a panel session at XSym 2007.

  2. 2.

    Extensible Markup Language (XML) (November 2015).

  3. 3.

    JavaScript Object Notation (JSON) (November 2015).

  4. 4.

    Second String (November 2015).

  5. 5.

    We focus on supervised classification, i.e., all training data are labelled with a class.

  6. 6.

    The two schemas of a pair may be necessary to compute similarity values, for instance with structural or contextual measures.

  7. 7.

    Other stop conditions may be used, for instance “all training data have been correctly classified”.

  8. 8.

    If the user has not provided a sufficient number of correspondences, YAM will extract others from the repository.

  9. 9.

    Second String (November 2015).

  10. 10.

    Free Web Services (November 2015).

  11. 11.

    Only the F-measure plot is provided since the plots for precision and recall follow the same trend as the F-measure.

  12. 12.

    This classifier is named instance-based since the correspondences (included in the training scenarios) are considered as instances during learning. Our approach does not currently use schema instances.

  13. 13.

    Some GUIs already exist to facilitate this task by suggesting the most probable correspondences.

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Author information

Authors and Affiliations

  1. Université Lyon 1, LIRIS UMR 5205, Lyon, France

    Fabien Duchateau

  2. Université Montpellier, LIRMM, Montpellier, France

    Zohra Bellahsene

Authors
  1. Fabien Duchateau
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  2. Zohra Bellahsene
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Corresponding author

Correspondence to Fabien Duchateau .

Editor information

Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. FAW, University of Linz, Linz, Austria

    Josef Küng

  3. FAW, University of Linz, Linz, Austria

    Roland Wagner

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Duchateau, F., Bellahsene, Z. (2016). YAM: A Step Forward for Generating a Dedicated Schema Matcher. In: Hameurlain, A., Küng, J., Wagner, R. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXV. Lecture Notes in Computer Science(), vol 9620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49534-6_5

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  • DOI: https://doi.org/10.1007/978-3-662-49534-6_5

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