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 …
Dynamic graph representation learning with neural networks: A survey
In recent years, Dynamic Graph (DG) representations have been increasingly used for
modeling dynamic systems due to their ability to integrate both topological and temporal …
modeling dynamic systems due to their ability to integrate both topological and temporal …
RoAN: A relation-oriented attention network for temporal knowledge graph completion
L Bai, X Ma, X Meng, X Ren, Y Ke - Engineering Applications of Artificial …, 2023 - Elsevier
In the last few years, the availability of temporal knowledge graphs (TKGs), which associate
time information for each event, increased the need for completing in these TKGs. In order to …
time information for each event, increased the need for completing in these TKGs. In order to …
Few-shot inductive learning on temporal knowledge graphs using concept-aware information
Knowledge graph completion (KGC) aims to predict the missing links among knowledge
graph (KG) entities. Though various methods have been developed for KGC, most of them …
graph (KG) entities. Though various methods have been developed for KGC, most of them …
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 …
Learning Meta-Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction
Few-shot relational learning for static knowledge graphs (KGs) has drawn greater interest in
recent years, while few-shot learning for temporal knowledge graphs (TKGs) has hardly …
recent years, while few-shot learning for temporal knowledge graphs (TKGs) has hardly …
ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs
Question answering over temporal knowledge graphs (TKGQA) has recently found
increasing interest. Previous related works aim to develop QA systems that answer temporal …
increasing interest. Previous related works aim to develop QA systems that answer temporal …
TaReT: Temporal knowledge graph reasoning based on topology-aware dynamic relation graph and temporal fusion
J Ma, K Li, F Zhang, Y Wang, X Luo, C Li… - Information Processing & …, 2024 - Elsevier
Previous temporal knowledge graph (TKG) reasoning methods often focus exclusively on
evolving representations. However, these methods suffer from the inadequacy of capturing …
evolving representations. However, these methods suffer from the inadequacy of capturing …
A survey on temporal knowledge graph embedding: Models and applications
Y Zhang, X Kong, Z Shen, J Li, Q Yi, G Shen… - Knowledge-Based …, 2024 - Elsevier
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 …
COSIGN: Contextual Facts Guided Generation for Knowledge Graph Completion
Abstract Knowledge graph completion (KGC) aims to infer missing facts based on existing
facts within a KG. Recently, research on generative models (GMs) has addressed the …
facts within a KG. Recently, research on generative models (GMs) has addressed the …