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GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models

A novel retrieval-augmented generation framework for temporal knowledge graph forecasting using large language models.

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GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models

By Ruotong Liao, Jia Xu, Yangzhe Li, Yunpu Ma, Volker Tresp
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The paper proposes GenTKG, a framework that combines temporal logical rule-based retrieval and few-shot parameter-efficient instruction tuning to address challenges in temporal knowledge graph forecasting.

Experiments show that GenTKG outperforms conventional methods with low computation resources and limited training data. The work highlights the potential of large language models in the temporal knowledge graph domain.

Abstract

The paper proposes GenTKG, a framework that combines temporal logical rule-based retrieval and few-shot parameter-efficient instruction tuning to address challenges in temporal knowledge graph forecasting. Experiments show that GenTKG outperforms conventional methods with low computation resources and limited training data. The work highlights the potential of large language models in the temporal knowledge graph domain.

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generative forecastingtemporal knowledge graphslarge language modelsretrieval-augmented generationfew-shot learningKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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