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Constraint-Based Querying for Bayesian Network Exploration

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Advances in Intelligent Data Analysis XIV (IDA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9385))

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Abstract

Understanding the knowledge that resides in a Bayesian network can be hard, certainly when a large network is to be used for the first time, or when the network is complex or has just been updated. Tools to assist users in the analysis of Bayesian networks can help. In this paper, we introduce a novel general framework and tool for answering exploratory queries over Bayesian networks. The framework is inspired by queries from the constraint-based mining literature designed for the exploratory analysis of data. Adapted to Bayesian networks, these queries specify a set of constraints on explanations of interest, where an explanation is an assignment to a subset of variables in a network. Characteristic for the methodology is that it searches over different subsets of the explanations, corresponding to different marginalizations. A general purpose framework, based on principles of constraint programming, data mining and knowledge compilation, is used to answer all possible queries. This CP4BN framework employs a rich set of constraints and is able to emulate a range of existing queries from both the Bayesian network and the constraint-based data mining literature.

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Notes

  1. 1.

    http://reasoning.cs.ucla.edu/ace/.

  2. 2.

    http://www.gecode.org.

  3. 3.

    available at http://www.bnlearn.com/bnrepository/.

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Acknowledgment

This work was supported by the European Commission under the project“Inductive Constraint Programming” contract number FP7-284715 and by the Research Foundation–Flanders by means of two Postdoc grants

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Correspondence to Behrouz Babaki .

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Babaki, B., Guns, T., Nijssen, S., De Raedt, L. (2015). Constraint-Based Querying for Bayesian Network Exploration. In: Fromont, E., De Bie, T., van Leeuwen, M. (eds) Advances in Intelligent Data Analysis XIV. IDA 2015. Lecture Notes in Computer Science(), vol 9385. Springer, Cham. https://doi.org/10.1007/978-3-319-24465-5_2

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  • DOI: https://doi.org/10.1007/978-3-319-24465-5_2

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