Probabilistic-Logical Web Data Integration

  • Mathias Niepert
  • Jan Noessner
  • Christian Meilicke
  • Heiner Stuckenschmidt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6848)


The integration of both distributed schemas and data repositories is a major challenge in data and knowledge management applications. Instances of this problem range from mapping database schemas to object reconciliation in the linked open data cloud. We present a novel approach to several important data integration problems that combines logical and probabilistic reasoning. We first provide a brief overview of some of the basic formalisms such as description logics and Markov logic that are used in the framework. We then describe the representation of the different integration problems in the probabilistic-logical framework and discuss efficient inference algorithms. For each of the applications, we conducted extensive experiments on standard data integration and matching benchmarks to evaluate the efficiency and performance of the approach. The positive results of the evaluation are quite promising and the flexibility of the framework makes it easily adaptable to other real-world data integration problems.


Description Logic Stability Constraint Ground Atom Ontology Match Markov Logic Network 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mathias Niepert
    • 1
  • Jan Noessner
    • 1
  • Christian Meilicke
    • 1
  • Heiner Stuckenschmidt
    • 1
  1. 1.KR & KM Research GroupUniversity of MannheimMannheimGermany

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