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PLINI: A Probabilistic Logic Program Framework for Inconsistent News Information

  • Massimiliano Albanese
  • Matthias Broecheler
  • John Grant
  • Maria Vanina Martinez
  • V. S. Subrahmanian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6565)

Abstract

News sources are reliably unreliable. Different news sources may provide significantly differing reports about the same event. Often times, even the same news source may provide widely varying data over a period of time about the same event. Past work on inconsistency management and paraconsistent logics assume that we have “clean” definitions of inconsistency. However, when reasoning about events reported in the news, we need to deal with two unique problems: (i) are two events being reported on the same or are they different? and (ii) what does it mean for two event descriptions to be mutually inconsistent, given that these events are often described using linguistic terms that do not always have a uniquely accepted formal semantics? The answers to these two questions turn out to be closely interlinked. In this paper, we propose a probabilistic logic programming language called PLINI (Probabilistic Logic for Inconsistent News Information) within which users can write rules specifying what they mean by inconsistency in situation (ii) above. We show that PLINI rules can be learned automatically from training data using standard machine learning algorithms. PLINI is a variant of the well known generalized annotated program framework that accounts for similarity of numeric, temporal, and spatial terms occurring in news. We develop a syntax, model theoretic semantics, and fixpoint semantics for PLINI rules, and show how PLINI rules can be used to detect inconsistent news reports.

Keywords

Logic Programming Hausdorff Distance News Report Predicate Symbol Equivalence Atom 
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

  • Massimiliano Albanese
    • 1
  • Matthias Broecheler
    • 1
  • John Grant
    • 1
    • 2
  • Maria Vanina Martinez
    • 1
  • V. S. Subrahmanian
    • 1
  1. 1.University of MarylandCollege ParkUSA
  2. 2.Towson UniversityTowsonUSA

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