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Instance Discovery and Schema Matching with Applications to Biological Deep Web Data Integration

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Part of the Lecture Notes in Computer Science book series (LNBI,volume 6254)

Abstract

This paper presents data mining-based techniques for enabling data integration across deep web data sources. We target query processing across inter-dependent data sources. Thus, besides input-input and output-output matching of attributes, we also need to consider input-output matching. We develop data mining techniques for discovering the instances for querying deep web data sources from the information provided by the query interfaces themselves, as well as from the obtained output pages of the related data sources, by query probing using dynamically identified input instances. Then, using a hierarchical representation of schemas and by applying clustering techniques, we are able to generate schema matches. We show the effectiveness of our technique while integrating 24 query interfaces.

Keywords

  • Schema Match
  • Output Attribute
  • Input Attribute
  • Query Interface
  • Input Instance

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-642-15120-0_12
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Liu, T., Wang, F., Agrawal, G. (2010). Instance Discovery and Schema Matching with Applications to Biological Deep Web Data Integration. In: Lambrix, P., Kemp, G. (eds) Data Integration in the Life Sciences. DILS 2010. Lecture Notes in Computer Science(), vol 6254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15120-0_12

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  • DOI: https://doi.org/10.1007/978-3-642-15120-0_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15119-4

  • Online ISBN: 978-3-642-15120-0

  • eBook Packages: Computer ScienceComputer Science (R0)