.png){kind=icon}
Return
Query-Aware Temporal Knowledge Graph Reasoning with Multi-source Knowledge Based Generation
.png){kind=icon}
PRE
AI
DOI：10.1007/978-3-032-09321-9_6.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Temporal Knowledge Graphs (TKGs) represent dynamic knowledge structures that encode time-sensitive relationships between entities, enabling systems to understand how facts evolve over time. Recent approaches to Temporal Knowledge Graph Reasoning (TKGR) have leveraged Large Language Models (LLMs), but face significant limitations: they often rely solely on first-order historical information, struggle with heavy information loads, and have yet to fully utilize LLMs potential for reasoning with semantically similar information. Additionally, current methods either lack interpretability or struggle with effective temporal rule learning. We present MSKGen (Multi-Source Knowledge-Based Generation), a novel query-aware approach for TKGR that integrates multiple knowledge sources to generate accurate predictions. By integrating rule-based facts with semantically retrieved facts, MSKGen maintains interpretability while maximizing LLMs semantic capabilities, addressing the information load challenges faced by current LLM implementations and offering significant advancements in combining structured temporal reasoning with semantic understanding for knowledge graph reasoning tasks. Experimental results across several common datasets demonstrate MSKGens superior performance, achieving significant improvements over state-of-the-art methods, confirming the effectiveness of our multi-source knowledge integration approach for temporal knowledge graph reasoning tasks.
Keywords:
Temporal Knowledge Graph
Large Language Models
Retrieval-Augmented Generation
Logical Rules
Journal
C
IF:
0
Papers:
34
Citations:
0
Organization
Cited Papers
.png){kind=icon}
Deja vu: Contrastive Historical Modeling with Prefix-tuning for Temporal Knowledge Graph Reasoning
OAAI
OAAIShareSaveShareSave no more
no more