A survey on temporal knowledge graph completion: Taxonomy, progress, and prospects
Temporal characteristics are prominently evident in a substantial volume of knowledge,
which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia …
which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia …
Chronobridge: a novel framework for enhanced temporal and relational reasoning in temporal knowledge graphs
The task of predicting entities and relations in Temporal Knowledge Graph (TKG)
extrapolation is crucial and has been studied extensively. Mainstream algorithms, such as …
extrapolation is crucial and has been studied extensively. Mainstream algorithms, such as …
A survey on temporal knowledge graph embedding: Models and applications
Abstract Knowledge graph embedding (KGE), as a pivotal technology in artificial
intelligence, plays a significant role in enhancing the logical reasoning and management …
intelligence, plays a significant role in enhancing the logical reasoning and management …
zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models
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 …
heated topic. Various methods have been proposed to forecast links on TKGs. Most of them …
Zero-shot relational learning on temporal knowledge graphs with large language models
In recent years, modeling evolving knowledge over temporal knowledge graphs (TKGs) has
become a heated topic. Various methods have been proposed to forecast links on TKGs …
become a heated topic. Various methods have been proposed to forecast links on TKGs …
Improving few-shot inductive learning on temporal knowledge graphs using confidence-augmented reinforcement learning
Temporal knowledge graph completion (TKGC) aims to predict the missing links among the
entities in a temporal knowledge graph (TKG). Most previous TKGC methods only consider …
entities in a temporal knowledge graph (TKG). Most previous TKGC methods only consider …
Temporal fact reasoning over hyper-relational knowledge graphs
Stemming from traditional knowledge graphs (KGs), hyper-relational KGs (HKGs) provide
additional key-value pairs (ie, qualifiers) for each KG fact that help to better restrict the fact …
additional key-value pairs (ie, qualifiers) for each KG fact that help to better restrict the fact …
Few-shot multi-hop reasoning via reinforcement learning and path search strategy over temporal knowledge graphs
L Bai, H Zhang, X An, L Zhu - Information Processing & Management, 2025 - Elsevier
Multi-hop reasoning on knowledge graphs is an important way to complete the knowledge
graph. However, existing multi-hop reasoning methods often perform poorly in few-shot …
graph. However, existing multi-hop reasoning methods often perform poorly in few-shot …
MuLAN: Multi-level attention-enhanced matching network for few-shot knowledge graph completion
Recent years have witnessed increasing interest in the few-shot knowledge graph
completion due to its potential to augment the coverage of few-shot relations in knowledge …
completion due to its potential to augment the coverage of few-shot relations in knowledge …
Zero-Shot Relational Learning for Multimodal Knowledge Graphs
Relational learning is an essential task in the domain of knowledge representation,
particularly in knowledge graph completion (KGC). While relational learning in traditional …
particularly in knowledge graph completion (KGC). While relational learning in traditional …