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

arXiv:2311.10112v2 Announce Type: replace-cross
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 …

abstract arxiv become cs.ai cs.cl cs.lg embedding forecast graph graphs hidden knowledge knowledge graph knowledge graphs language language models large language large language models modeling relational relations temporal them type zero-shot