.png){kind=icon}
Return
Rules-Guided Retrieval-Augmented Generation for Temporal Knowledge Graph Reasoning
.png){kind=icon}
PRE
AI
DOI：10.1007/978-981-95-5640-3_29.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Temporal knowledge graph (TKG) logical reasoning aims to predict future facts by learning logical correlations from TKGs. However, conventional Retrieval-Augmented Generation (RAG) methods rely on unstructured text as the retrieval space, failing to capture and utilize implicit logical correlations within the retrieved content. Furthermore, the conventional rule confidence calculation methods significantly restrict the generalization of the model when temporal logical rules are incorporated. To address these challenges, We propose a novel TKG reasoning framework Ru1eRAG (integration Rule with RAG). It validates the confidence of temporal logical rules using large language models (LLMs), and applies the rules and the query as input to LLMs. Experimental results demonstrate that Ru1eRAG outperforms state-of-the-art models.
Keywords:
Temporal Knowledge Graph Reasoning
Logical Rules
Large Language Model
Retrieval-Augmented Generation
Journal
W
IF:
0
Papers:
32
Citations:
0
Organization
Cited Papers
.png){kind=icon}
ShareSave
View more