Structure Learning via Parameter Learning
Abstract
A key challenge in information and knowledge management is to automatically discover the underlying structures and patterns from large collections of extracted information. This paper presents a novel structure-learning method for a new, scalable probabilistic logic called ProPPR. Our approach builds on the recent success of meta-interpretive learning methods in Inductive Logic Programming (ILP), and we further extends it to a framework that enables robust and efficient structure learning of logic programs on graphs: using an abductive second-order probabilistic logic, we show how first-order theories can be automatically generated via parameter learning. To learn better theories, we then propose an iterated structural gradient approach that incrementally refines the hypothesized space of learned first-order structures. In experiments, we show that the proposed method further improves the results, outperforming competitive baselines such as Markov Logic Networks (MLNs) and FOIL on multiple datasets with various settings; and that the proposed approach can learn structures in a large knowledge base in a tractable fashion.
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Metarules are another popular syntactic bias used by many ILP approaches, including Metagol and, to an extent, ∂ ILP.
“Metarules (Cropper and Tourret 2020) are another popular syntactic bias used by many ILP approaches (De Raedt and Bruynooghe 1992; Wang et al. 2014; Albarghouthi et al. 2017; Kaminski et al. 2018), including Metagol (Muggleton et al. 2015; Cropper et al. 2020; Cropper and Muggleton 2016) and, to an extent 5 , ∂ ILP (Evans and Grefenstette 2018).”
From Learning programs by learning from failures (2021) · cites this paper as Wang et al. 2014
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- Authors
- Wang, W. Y., Mazaitis, K., & Cohen, W. W.
- Affiliations
- Carnegie Mellon University (US)
- ORCID
- Wang, William Yang
- Venue
- Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management
- Publisher
- ACM
- Published
- 2014-11-03
- Pages
- 1199-1208
- ISSN
- 2155-0751 (ISSN-L)
- OpenAlex
- W2089334447
- Cited by
- 47
- References
- 46
- Citations by year
- 2026: 1 · 2025: 1 · 2023: 4 · 2022: 2 · 2021: 7 · 2020: 7 · 2019: 7 · 2018: 4 · 2017: 3 · 2016: 6 · 2015: 3 · 2014: 1
- FWCI
- 2.55
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Information and Knowledge Management (CIKM) (publishedVersion)
- Funders
- Defense Advanced Research Projects Agency (FA8750-12-2-0342)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Keywords
- structure learning, probabilistic logic, inductive logic programming, parameter learning, knowledge base learning, Markov logic networks
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