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Improving Candidate Quality of Probabilistic Logic Models

Authors Joana Côrte-Real , Anton Dries , Inês Dutra , Ricardo Rocha

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Author Details

Joana Côrte-Real
  • CRACS & INESC TEC and Faculty of Sciences, University of Porto, Rua do Campo Alegre, 1021, 4169-007 Porto, Portugal
Anton Dries
  • KU Leuven, Department of Computer Science, Celestijnenlaan 200A bus 2402, 3001 Leuven, Belgium
Inês Dutra
  • CINTESIS and Faculty of Sciences, University of Porto, Rua do Campo Alegre, 1021, 4169-007 Porto, Portugal
Ricardo Rocha
  • CRACS & INESC TEC and Faculty of Sciences, University of Porto, Rua do Campo Alegre, 1021, 4169-007 Porto, Portugal

Funding

Work partially funded by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF) as part of project NanoSTIMA (NORTE-01-0145-FEDER-000016) and through the operation POCI-01-0145-FEDER-007746 funded by COMPETE2020 and by national funds from FCT within CINTESIS, R&D Unit (reference UID/IC/4255/2013).
  • Côrte-Real, Joana: Funded by the FCT grant SFRH/BD/52235/2013.

Cite AsGet BibTex

Joana Côrte-Real, Anton Dries, Inês Dutra, and Ricardo Rocha. Improving Candidate Quality of Probabilistic Logic Models. In Technical Communications of the 34th International Conference on Logic Programming (ICLP 2018). Open Access Series in Informatics (OASIcs), Volume 64, pp. 6:1-6:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/OASIcs.ICLP.2018.6

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 adequate 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 evaluation 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) maintaining 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.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Probabilistic reasoning
Keywords
  • Relational Machine Learning
  • Probabilistic Inductive Logic Programming
  • Search Space Pruning
  • Model Quality
  • Experiments

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