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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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
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