Rule Induction in Knowledge Graphs Using Linear Programming
Authors:
- Sanjeeb DashSDSanjeeb Dash
- Joao GoncalvesJGJoao Goncalves
Preprint
Abstract
We present a simple linear programming (LP) based method to learn compact and interpretable sets of rules encoding the facts in a knowledge graph (KG) and use these rules to solve the KG completion problem. Our LP model chooses a set of rules of bounded complexity from a list of candidate first-order logic rules and assigns weights to them. The complexity bound is enforced via explicit constraints. We combine simple rule generation heuristics with our rule selection LP to obtain predictions with accuracy comparable to state-of-the-art codes, even while generating much more compact rule sets. Furthermore, when we take as input rules generated by other codes, we often improve interpretability by reducing the number of chosen rules, while maintaining accuracy.Keywords
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This publication has 14 references indexed in Scilit:
- Anytime bottom-up rule learning for large-scale knowledge graph completionThe VLDB Journal, 2023
- Interpretable machine learning: Fundamental principles and 10 grand challengesStatistics Surveys, 2022
- A Survey on Bias and Fairness in Machine LearningACM Computing Surveys, 2021
- A Survey on Knowledge Graphs: Representation, Acquisition, and ApplicationsIEEE Transactions on Neural Networks and Learning Systems, 2021
- Survey of Explainable Machine Learning with Visual and Granular Methods Beyond Quasi-Explanations2021
- Knowledge Graph Embedding: A Survey of Approaches and ApplicationsIEEE Transactions on Knowledge and Data Engineering, 2017
- How Does Predicate Invention Affect Human Comprehensibility?2017
- Learning the structure of Markov logic networks2005
- An upper‐level ontology for the biomedical domainComparative and Functional Genomics, 2003
- Linear Programming Boosting via Column GenerationMachine Learning, 2002