Improving Candidate Quality of Probabilistic Logic Models
Joana Côrte-Real, Anton Dries, Inês Dutra and Ricardo Rocha
July 2018
Abstract
Many real-world phenomena exhibit both relational structure and
uncertainty. Probabilistic Inductive Logic Programming (PILP) uses
Inductive Logic Programming (ILP) extended with probabilistic facts to
produce meaningful and interpretable models for real-world
phenomena. This merge between First Order Logic (FOL) theories and
uncertainty makes PILP a very ade- quate tool for knowledge
representation and extraction. However, this flexibility is coupled
with a problem (inherited from ILP) of exponential search space growth
and so, often, only a subset of all possible models is explored due to
limited resources. Furthermore, the probabilistic eval- uation of FOL
theories, coming from the underlying probabilistic logic language and
its solver, is also computationally demanding. This work introduces a
prediction-based pruning strategy, which can reduce the search space
based on the probabilistic evaluation of models, and a safe pruning
criterion, which guarantees that the optimal model is not pruned away,
as well as two alternative more aggressive criteria that do not
provide this guarantee. Experiments performed using three benchmarks
from different areas show that prediction pruning is effective in (i)
main- taining predictive accuracy for all criteria and experimental
settings; (ii) reducing the execution time when using some of the more
aggressive criteria, compared to using no pruning; and (iii) selecting
better candidate models in limited resource settings, also when
compared to using no pruning.
Bibtex
@InProceedings{corte-real-iclp18,
author = {J. Côrte-Real and A. Dries and I. Dutra and R. Rocha},
title = {{Improving Candidate Quality of Probabilistic Logic Models}},
booktitle = {Technical Communications of the 34th International Conference on Logic Programming (ICLP 2018)},
editor = {A. dal Palù and P. Tarau},
month = {July},
year = {2018},
address = {Oxford, UK},
}
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