Abstract
Probabilistic Inductive Logic Programming (PILP) systems extend ILP by allowing the world to be represented using probabilistic facts and rules, and by learning probabilistic theories that can be used to make predictions. However, such systems can be inefficient both due to the large search space inherited from the ILP algorithm and to the probabilistic evaluation needed whenever a new candidate theory is generated. To address the latter issue, this work introduces probability estimators aimed at improving the efficiency of PILP systems. An estimator can avoid the computational cost of probabilistic theory evaluation by providing an estimate of the value of the combination of two subtheories. Experiments are performed on three real-world datasets of different areas (biology, medical and web-based) and show that, by reducing the number of theories to be evaluated, the estimators can significantly shorten the execution time without losing probabilistic accuracy.
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Notes
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The unification of variables between the literals is obtained from the specified language bias.
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Theories are indexed only for clarity’s sake. They correspond to the same concept to be learned.
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Acknowledgments
Joana Côrte-Real is funded by the FCT grant SFRH/BD/52235/2013. This work is partially funded by the ERDF through the COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, by National Funds through the FCT as part of project UID/EEA/50014/2013, and by the North Portugal Regional Operational Programme, under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund as part of project NanoSTIMA (NORTE-01-0145-FEDER-000016).
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Côrte-Real, J., Dutra, I., Rocha, R. (2017). Estimation-Based Search Space Traversal in PILP Environments. In: Cussens, J., Russo, A. (eds) Inductive Logic Programming. ILP 2016. Lecture Notes in Computer Science(), vol 10326. Springer, Cham. https://doi.org/10.1007/978-3-319-63342-8_1
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