MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning
Multi-hop reasoning approaches over knowledge graphs infer a missing relationship between entities with a multi-hop rule, which corresponds to a chain of relationships. We extend existing works to consider a generalized form of multi-hop rules, where each rule is a set of relation chains. To learn such generalized rules efficiently, we propose a two-step approach that first selects a small set of relation chains as a rule and then evaluates the confidence of the target relationship by jointly scoring the selected chains. A game-theoretical framework is proposed to this end to simultaneously optimize the rule selection and prediction steps. Empirical results show that our multi-chain multi-hop (MCMH) rules result in superior results compared to the standard single-chain approaches, justifying both our formulation of generalized rules and the effectiveness of the proposed learning framework.
Code (0)
등록된 구현이 없습니다.
Tasks
Knowledge GraphsRelationSimilar Papers 제목 키워드 기반
From Chain to Tree: Refining Chain-like Rules into Tree-like Rules on Knowledge Graphs
With good explanatory power and controllability, rule-based methods play an important role in many tasks such as knowledge reasoning and decision support. However, existing studies primarily focused on learning chain-lik…
Knowledge GraphsLink PredictionSemantic Interoperability on Blockchain by Generating Smart Contracts Based on Knowledge Graphs
Background: Health 3.0 allows decision making to be based on longitudinal data from multiple institutions, from across the patient's healthcare journey. In such a distributed setting, blockchain smart contracts can act a…
Code GenerationDecision MakingKnowledge GraphsEfficient Tool Use with Chain-of-Abstraction Reasoning
To achieve faithful reasoning that aligns with human expectations, large language models (LLMs) need to ground their reasoning to real-world knowledge (e.g., web facts, math and physical rules). Tools help LLMs access th…
MathMathematical ReasoningWorld KnowledgeGenerating Logically Consistent Synthetic Supply Chain Data with LLM-Driven Knowledge Graph Reasoning
Synthetic data offers a promising solution to two persistent barriers in supply chain analytics: data scarcity and data privacy. However, for synthetic data to support operational simulation and decision-making, it must …
Tabular Data GenerationSCoRE: Benchmarking Long-Chain Reasoning in Commonsense Scenarios
Currently, long-chain reasoning remains a key challenge for large language models (LLMs) because natural texts lack sufficient explicit reasoning data. However, existing benchmarks suffer from limitations such as narrow …
BenchmarkingDiagnosticLogical ReasoningMultiple-choice