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PIRE: An Extensible IR Engine Based on Probabilistic Datalog

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Advances in Information Retrieval (ECIR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3408))

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Abstract

This paper introduces PIRE, a probabilistic IR engine. For both document indexing and retrieval, PIRE makes heavy use of probabilistic Datalog, a probabilistic extension of predicate Horn logics. Using such a logical framework together with probability theory allows for defining and using data types (e.g. text, names, numbers), different weighting schemes (e.g. normalised tf, tf.idf or BM25) and retrieval functions (e.g. uncertain inference, language models). Extending the system thus is reduced to adding new rules. Furthermore, this logical framework provide a powerful tool for including additional background knowledge into the retrieval process.

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Author information

Authors and Affiliations

  1. Institute of Informatics and Interactive Systems, University of Duisburg-Essen, 47048, Duisburg, Germany

    Henrik Nottelmann

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  1. Henrik Nottelmann
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Editors and Affiliations

  1. Departamento de Electrónica y Computación, Universidad de Santiago de Compostela, Spain

    David E. Losada

  2. Departamento de Ciencias de la Computación e Inteligencia Artificial E.T.S.I. Informática y de Telecomunicación, Universidad de Granada, 18071, Granada, Spain

    Juan M. Fernández-Luna

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Nottelmann, H. (2005). PIRE: An Extensible IR Engine Based on Probabilistic Datalog. In: Losada, D.E., Fernández-Luna, J.M. (eds) Advances in Information Retrieval. ECIR 2005. Lecture Notes in Computer Science, vol 3408. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31865-1_19

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  • DOI: https://doi.org/10.1007/978-3-540-31865-1_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25295-5

  • Online ISBN: 978-3-540-31865-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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