ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916099594911744 |
|---|---|
| author | Luo, Linhao Ju, Jiaxin Xiong, Bo Li, Yuan-Fang Haffari, Gholamreza Pan, Shirui |
| author_facet | Luo, Linhao Ju, Jiaxin Xiong, Bo Li, Yuan-Fang Haffari, Gholamreza Pan, Shirui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_01538 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning Luo, Linhao Ju, Jiaxin Xiong, Bo Li, Yuan-Fang Haffari, Gholamreza Pan, Shirui Artificial Intelligence Computation and Language 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. |
| title | ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2309.01538 |