Knowledge Graph Rule Induction With LP-Guided Rule Selection
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Solution Overview
Problem
Existing knowledge graph (KG) completion methods face limitations in scalability and interpretability, with rule-based approaches lacking accuracy and embedding methods being non-interpretable and limited to transductive reasoning, while both struggle with large datasets.
Innovation Solution
A linear programming (LP) model is applied to iteratively generate and evaluate candidate rules, using column generation techniques to expand the rule set, ensuring interpretability and generalizability by constraining rule complexity and tuning hyperparameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If rule-based approaches are used for KG completion, then interpretability is improved, but accuracy deteriorates
Solution Approach 1:
The patent merges rule-based approaches with linear programming optimization. Rules provide interpretability while the LP model optimizes rule selection and weighting to achieve high accuracy. The combination allows the system to maintain human-understandable reasoning paths while achieving state-of-the-art performance on KG completion tasks.
Solution Approach 2:
The patent transforms discrete rule selection into a continuous optimization problem by introducing weights for different rules in the linear programming model. This allows fine-grained control over rule application and enables the system to balance between different rule qualities, thereby improving accuracy while maintaining interpretability through the weighted combination of interpretable rules.
2Measurement precision
If embedding methods are used for KG completion, then accuracy is improved, but interpretability deteriorates
Solution Approach 1:
The patent segments the KG completion task into rule-based reasoning steps that can be individually interpreted. Instead of using monolithic embedding models that produce black-box predictions, the system breaks down the reasoning into discrete, explainable rules that can be traced and understood, while still achieving high accuracy through optimized rule combinations.
3Loss of information
If traditional rule-based methods are used, then interpretability is maintained, but scalability deteriorates
Solution Approach 1:
The patent replaces manual rule engineering and exhaustive rule search with automated linear programming optimization. The LP model automatically selects and weights rules based on data-driven objectives, eliminating the need for manual rule curation and enabling the system to scale to large knowledge graphs without proportionally increasing manual effort.
Solution Approach 2:
The patent changes the problem from discrete rule enumeration to continuous optimization, allowing efficient computation of optimal rule combinations even with large numbers of candidate rules. This transformation enables scalability by using mathematical optimization algorithms that can handle large-scale rule sets without exhaustive search.
4Loss of information
If comprehensive rule sets are generated, then KG completeness is improved, but rule complexity increases
Solution Approach 1:
The patent generates a comprehensive set of candidate rules beyond what is strictly necessary, then uses linear programming to select the optimal subset. This approach ensures KG completeness by considering all possible reasoning paths, while the optimization process prunes redundant or low-value rules, maintaining manageable complexity in the final rule set.
Solution Approach 2:
The patent performs preliminary generation of all candidate rules before optimization, ensuring that no potentially useful rules are missed. The LP model then evaluates and selects from this comprehensive preliminary set, guaranteeing KG completeness while controlling final complexity through optimized selection rather than restrictive generation constraints.
Data Source
AI summary
Mechanisms are provided for automated rule set generation for identifying relations in knowledge graph data structures. An input knowledge graph is processed to extract tuples representing relations between entities present in the input knowledge graph. A set of rules is generated based on one or more heuristics applied to tuples, and candidate rule(s) are identified that are candidates for adding to the set of rules. A linear programming computer model is evaluated for a modified set of rules comprising the set of rules and the candidate rule(s) to determine whether or not adding the candidate rule(s) improves an objective function of the linear programming model. The set of rules is expanded to include the candidate rule(s) in response to the evaluation of the linear programming computer model indicating that the addition of the candidate rule(s) improves the objective function of the linear programming computer model.