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Assigning Semantic Labels to Data Sources

  • S.K. RamnandanEmail author
  • Amol Mittal
  • Craig A. Knoblock
  • Pedro Szekely
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9088)

Abstract

There is a huge demand to be able to find and integrate heterogeneous data sources, which requires mapping the attributes of a source to the concepts and relationships defined in a domain ontology. In this paper, we present a new approach to find these mappings, which we call semantic labeling. Previous approaches map each data value individually, typically by learning a model based on features extracted from the data using supervised machine-learning techniques. Our approach differs from existing approaches in that we take a holistic view of the data values corresponding to a semantic label and use techniques that treat this data collectively, which makes it possible to capture characteristic properties of the values associated with a semantic label as a whole. Our approach supports both textual and numeric data and proposes the top \(k\) semantic labels along with their associated confidence scores. Our experiments show that the approach has higher label prediction accuracy, has lower time complexity, and is more scalable than existing systems.

Keywords

Semantic labeling Source modeling 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • S.K. Ramnandan
    • 1
    Email author
  • Amol Mittal
    • 2
  • Craig A. Knoblock
    • 3
  • Pedro Szekely
    • 3
  1. 1.Indian Institute of Technology - MadrasChennaiIndia
  2. 2.Indian Institute of Technology - DelhiNew DelhiIndia
  3. 3.University of Southern CaliforniaLos AngelesUSA

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