zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models

TL;DR

This paper introduces a method that leverages large language models to generate semantic relation representations for temporal knowledge graphs, significantly improving zero-shot relation forecasting without prior graph context.

Contribution

The paper proposes integrating LLM-generated relation embeddings into TKG forecasting models to effectively handle unseen relations in temporal knowledge graphs.

Findings

Enhanced zero-shot relation forecasting performance

Maintained accuracy on seen relation predictions

Demonstrated effectiveness across multiple TKG benchmarks

Abstract

Modeling evolving knowledge over temporal knowledge graphs (TKGs) has become a heated topic. Various methods have been proposed to forecast links on TKGs. Most of them are embedding-based, where hidden representations are learned to represent knowledge graph (KG) entities and relations based on the observed graph contexts. Although these methods show strong performance on traditional TKG forecasting (TKGF) benchmarks, they face a strong challenge in modeling the unseen zero-shot relations that have no prior graph context. In this paper, we try to mitigate this problem as follows. We first input the text descriptions of KG relations into large language models (LLMs) for generating relation representations, and then introduce them into embedding-based TKGF methods. LLM-empowered representations can capture the semantic information in the relation descriptions. This makes the relations, whether seen or unseen, with similar semantic meanings stay close in the embedding space, enabling TKGF models to recognize zero-shot relations even without any observed graph context. Experimental results show that our approach helps TKGF models to achieve much better performance in forecasting the facts with previously unseen relations, while still maintaining their ability in link forecasting regarding seen relations.