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Learning directed probabilistic logical models: ordering-search versus structure-search

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Abstract

We discuss how to learn non-recursive directed probabilistic logical models from relational data. This problem has been tackled before by upgrading the structure-search algorithm initially proposed for Bayesian networks. In this paper we show how to upgrade another algorithm for learning Bayesian networks, namely ordering-search. For Bayesian networks, ordering-search was found to work better than structure-search. It is non-obvious that these results carry over to the relational case, however, since there ordering-search needs to be implemented quite differently. Hence, we perform an experimental comparison of these upgraded algorithms on four relational domains. We conclude that also in the relational case ordering-search is competitive with structure-search in terms of quality of the learned models, while ordering-search is significantly faster.

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Authors and Affiliations

  1. Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, 3001, Leuven, Belgium

    Daan Fierens, Jan Ramon, Maurice Bruynooghe & Hendrik Blockeel

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  1. Daan Fierens
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  2. Jan Ramon
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  3. Maurice Bruynooghe
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  4. Hendrik Blockeel
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Correspondence to Daan Fierens.

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Fierens, D., Ramon, J., Bruynooghe, M. et al. Learning directed probabilistic logical models: ordering-search versus structure-search. Ann Math Artif Intell 54, 99–133 (2008). https://doi.org/10.1007/s10472-009-9134-9

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  • DOI: https://doi.org/10.1007/s10472-009-9134-9

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