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Type Inference on Noisy RDF Data

  • Heiko Paulheim
  • Christian Bizer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8218)

Abstract

Type information is very valuable in knowledge bases. However, most large open knowledge bases are incomplete with respect to type information, and, at the same time, contain noisy and incorrect data. That makes classic type inference by reasoning difficult. In this paper, we propose the heuristic link-based type inference mechanism SDType, which can handle noisy and incorrect data. Instead of leveraging T-box information from the schema, SDType takes the actual use of a schema into account and thus is also robust to misused schema elements.

Keywords

Type Inference Noisy Data Link-based Classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Heiko Paulheim
    • 1
  • Christian Bizer
    • 1
  1. 1.Research Group Data and Web ScienceUniversity of MannheimGermany

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