ChatRule: Mining Logical Rules with Large Language Models for Knowledge
Graph Reasoning
- URL: http://arxiv.org/abs/2309.01538v3
- Date: Mon, 22 Jan 2024 02:39:17 GMT
- Title: ChatRule: Mining Logical Rules with Large Language Models for Knowledge
Graph Reasoning
- Authors: Linhao Luo, Jiaxin Ju, Bo Xiong, Yuan-Fang Li, Gholamreza Haffari,
Shirui Pan
- Abstract summary: 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.
- Score: 107.61997887260056
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Logical rules are essential for uncovering the logical connections between
relations, which could improve reasoning performance and provide interpretable
results on knowledge graphs (KGs). Although there have been many efforts to
mine meaningful logical rules over KGs, existing methods suffer from
computationally intensive searches over the rule space and a lack of
scalability for large-scale KGs. Besides, they often ignore the semantics of
relations which is crucial for uncovering logical connections. Recently, large
language models (LLMs) have shown impressive performance in the field of
natural language processing and various applications, owing to their emergent
ability and generalizability. In this paper, 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 prompt LLMs to generate logical rules. To refine the
generated rules, a rule ranking module estimates the rule quality by
incorporating facts from existing KGs. Last, the ranked rules can be used to
conduct reasoning over KGs. ChatRule is evaluated on four large-scale KGs,
w.r.t. different rule quality metrics and downstream tasks, showing the
effectiveness and scalability of our method.
Related papers
- Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning [87.10396098919013]
Large Language Models (LLMs) have demonstrated extensive knowledge and remarkable proficiency in temporal reasoning.
We propose a Large Language Models-guided Dynamic Adaptation (LLM-DA) method for reasoning on Temporal Knowledge Graphs.
LLM-DA harnesses the capabilities of LLMs to analyze historical data and extract temporal logical rules.
arXiv Detail & Related papers (2024-05-23T04:54:37Z) - Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMs [87.34281749422756]
Large language models (LLMs) have achieved impressive human-like performance across various reasoning tasks.
However, their mastery of underlying inferential rules still falls short of human capabilities.
We propose a logic scaffolding inferential rule generation framework, to construct an inferential rule base, ULogic.
arXiv Detail & Related papers (2024-02-18T03:38:51Z) - Reasoning on Graphs: Faithful and Interpretable Large Language Model
Reasoning [104.92384929827776]
Large language models (LLMs) have demonstrated impressive reasoning abilities in complex tasks.
They lack up-to-date knowledge and experience hallucinations during reasoning.
Knowledge graphs (KGs) offer a reliable source of knowledge for reasoning.
arXiv Detail & Related papers (2023-10-02T10:14:43Z) - 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) - EngineKGI: Closed-Loop Knowledge Graph Inference [37.15381932994768]
EngineKGI is a novel closed-loop KG inference framework.
It combines KGE and rule learning to complement each other in a closed-loop pattern.
Our model outperforms other baselines on link prediction tasks.
arXiv Detail & Related papers (2021-12-02T08:02:59Z) - RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs [91.71504177786792]
This paper studies learning logic rules for reasoning on knowledge graphs.
Logic rules provide interpretable explanations when used for prediction as well as being able to generalize to other tasks.
Existing methods either suffer from the problem of searching in a large search space or ineffective optimization due to sparse rewards.
arXiv Detail & Related papers (2020-10-08T14:47:02Z) - Building Rule Hierarchies for Efficient Logical Rule Learning from
Knowledge Graphs [20.251630903853016]
We propose new methods for pruning unpromising rules using rule hierarchies.
We show that the application of HPMs is effective in removing unpromising rules.
arXiv Detail & Related papers (2020-06-29T16:33:30Z) - 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.