MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning

• URL: http://arxiv.org/abs/2010.01735v1
• Date: Mon, 5 Oct 2020 01:32:20 GMT
• Title: MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning
• Authors: Lu Zhang, Mo Yu, Tian Gao, Yue Yu
• Abstract summary: We consider a generalized form of multi-hop rules, where each rule is a set of relation chains. 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. Empirical results show that our multi-chain multi-hop (MCMH) rules result in superior results compared to the standard single-chain approaches.
• Score: 46.68583750992613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Top-K Pairwise Ranking: Bridging the Gap Among Ranking-Based Measures for Multi-Label Classification [120.37051160567277]
  This paper proposes a novel measure named Top-K Pairwise Ranking (TKPR) A series of analyses show that TKPR is compatible with existing ranking-based measures. On the other hand, we establish a sharp generalization bound for the proposed framework based on a novel technique named data-dependent contraction.
  arXiv  Detail & Related papers  (2024-07-09T09:36:37Z)
• Probabilistic Truly Unordered Rule Sets [4.169915659794567]
  We propose TURS, for Truly Unordered Rule Sets. We exploit the probabilistic properties of our rule sets, with the intuition of only allowing rules to overlap if they have similar probabilistic outputs. We benchmark against a wide range of rule-based methods and demonstrate that our method learns rule sets that have lower model complexity and highly competitive predictive performance.
  arXiv  Detail & Related papers  (2024-01-18T12:03:19Z)
• ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning [107.61997887260056]
  We propose a novel framework, ChatRule, unleashing the power of large language models for mining logical rules over knowledge graphs. Specifically, the framework is initiated with an LLM-based rule generator, leveraging both the semantic and structural information of KGs. To refine the generated rules, a rule ranking module estimates the rule quality by incorporating facts from existing KGs.
  arXiv  Detail & Related papers  (2023-09-04T11:38:02Z)
• On the Aggregation of Rules for Knowledge Graph Completion [9.628032156001069]
  Rule learning approaches for knowledge graph completion are efficient, interpretable and competitive to purely neural models. We show that existing aggregation approaches can be expressed as marginal inference operations over the predicting rules. We propose an efficient and overlooked baseline which combines the previous strategies and is competitive to computationally more expensive approaches.
  arXiv  Detail & Related papers  (2023-09-01T07:32:11Z)
• RulE: Knowledge Graph Reasoning with Rule Embedding [69.31451649090661]
  We propose a principled framework called textbfRulE (stands for Rule Embedding) to leverage logical rules to enhance KG reasoning. RulE learns rule embeddings from existing triplets and first-order rules by jointly representing textbfentities, textbfrelations and textbflogical rules in a unified embedding space. Results on multiple benchmarks reveal that our model outperforms the majority of existing embedding-based and rule-based approaches.
  arXiv  Detail & Related papers  (2022-10-24T06:47:13Z)
• Concise and interpretable multi-label rule sets [13.416159628299779]
  We develop a multi-label classifier that can be represented as a concise set of simple "if-then" rules. Our method is able to find a small set of relevant patterns that lead to accurate multi-label classification.
  arXiv  Detail & Related papers  (2022-10-04T11:23:50Z)
• Combining Rules and Embeddings via Neuro-Symbolic AI for Knowledge Base Completion [59.093293389123424]
  We show that not all rule-based Knowledge Base Completion models are the same. We propose two distinct approaches that learn in one case: 1) a mixture of relations and the other 2) a mixture of paths. When implemented on top of neuro-symbolic AI, which learns rules by extending Boolean logic to real-valued logic, the latter model leads to superior KBC accuracy outperforming state-of-the-art rule-based KBC by 2-10% in terms of mean reciprocal rank.
  arXiv  Detail & Related papers  (2021-09-16T17:54:56Z)
• Theoretical Rule-based Knowledge Graph Reasoning by Connectivity Dependency Discovery [2.945948598480997]
  We present a theory for rule-based knowledge graph reasoning, based on which the connectivity dependencies in the graph are captured via multiple rule types. Results show that our RuleDict model not only provides precise rules to interpret new triples, but also achieves state-of-the-art performances on one benchmark knowledge graph completion task.
  arXiv  Detail & Related papers  (2020-11-12T03:00:20Z)
• On Connections between Regularizations for Improving DNN Robustness [67.28077776415724]
  This paper analyzes regularization terms proposed recently for improving the adversarial robustness of deep neural networks (DNNs) We study possible connections between several effective methods, including input-gradient regularization, Jacobian regularization, curvature regularization, and a cross-Lipschitz functional.
  arXiv  Detail & Related papers  (2020-07-04T23:43:32Z)
• Towards Learning Instantiated Logical Rules from Knowledge Graphs [20.251630903853016]
  We present GPFL, a probabilistic learner rule optimized to mine instantiated first-order logic rules from knowledge graphs. GPFL utilizes a novel two-stage rule generation mechanism that first generalizes extracted paths into templates that are acyclic abstract rules. We reveal the presence of overfitting rules, their impact on the predictive performance, and the effectiveness of a simple validation method filtering out overfitting rules.
  arXiv  Detail & Related papers  (2020-03-13T00:32:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.