Abduction for Extending Incomplete Information Sources

  • Carlo Meghini
  • Yannis Tzitzikas
  • Nicolas Spyratos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3955)


The extraction of information from a source containing term-classified objects is plagued with uncertainty, due, among other things, to the possible incompleteness of the source index. To overcome this incompleteness, the study proposes to expand the index of the source, in a way that is as reasonable as possible with respect to the original classification of objects. By equating reasonableness with logical implication, the sought expansion turns out to be an explanation of the index, captured by abduction. We study the general problem of query evaluation on the extended information source, providing a polynomial time algorithm which tackles the general case, in which no hypothesis is made on the structure of the taxonomy. We then specialize the algorithm for two well-know structures: DAGs and trees, showing that each specialization results in a more efficient query evaluation.


Information Source Query Term Propositional Variable Truth Assignment Query Evaluation 
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 2006

Authors and Affiliations

  • Carlo Meghini
    • 1
  • Yannis Tzitzikas
    • 2
  • Nicolas Spyratos
    • 3
  1. 1.Istituto della Scienza e delle Tecnologie della InformazioneConsiglio Nazionale delle RicerchePisaItaly
  2. 2.Department of Computer ScienceUniversity of CreteHeraklion, CreteGreece
  3. 3.Laboratoire de Recherche en InformatiqueUniversité Paris-SudOrsay CedexFrance

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