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 …
A survey of knowledge graph reasoning on graph types: Static, dynamic, and multi-modal
Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …
The survey on multi-source data fusion in cyber-physical-social systems: Foundational infrastructure for industrial metaverses and industries 5.0
X Wang, Y Wang, J Yang, X Jia, L Li, W Ding… - Information Fusion, 2024 - Elsevier
As the concept of Industries 5.0 develops, industrial metaverses are expected to operate in
parallel with the actual industrial processes to offer “Human-Centric” Safe, Secure …
parallel with the actual industrial processes to offer “Human-Centric” Safe, Secure …
RETIA: relation-entity twin-interact aggregation for temporal knowledge graph extrapolation
Temporal knowledge graph (TKG) extrapolation aims to predict future unknown events
(facts) based on historical information, and has attracted considerable attention due to its …
(facts) based on historical information, and has attracted considerable attention due to its …
Simplifying graph-based collaborative filtering for recommendation
Graph Convolutional Networks (GCNs) are a popular type of machine learning models that
use multiple layers of convolutional aggregation operations and non-linear activations to …
use multiple layers of convolutional aggregation operations and non-linear activations to …
THCN: A Hawkes Process Based Temporal Causal Convolutional Network for Extrapolation Reasoning in Temporal Knowledge Graphs
T Chen, J Long, Z Wang, S Luo… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Temporal Knowledge Graphs (TKGs) serve as indispensable tools for dynamic facts storage
and reasoning. However, predicting future facts in TKGs presents a formidable challenge …
and reasoning. However, predicting future facts in TKGs presents a formidable challenge …
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 rule-and query-guided reinforcement learning for extrapolation reasoning in temporal knowledge graphs
T Chen, L Yang, Z Wang, J Long - Neural Networks, 2025 - Elsevier
Extrapolation reasoning in temporal knowledge graphs (TKGs) aims at predicting future facts
based on historical data, and finds extensive application in diverse real-world scenarios …
based on historical data, and finds extensive application in diverse real-world scenarios …
DHyper: A Recurrent Dual Hypergraph Neural Network for Event Prediction in Temporal Knowledge Graphs
X Tang, L Chen, H Shi, D Lyu - ACM Transactions on Information …, 2024 - dl.acm.org
Event prediction is a vital and challenging task in temporal knowledge graphs (TKGs), which
have played crucial roles in various applications. Recently, many graph neural networks …
have played crucial roles in various applications. Recently, many graph neural networks …
Decoupled Progressive Distillation for Sequential Prediction with Interaction Dynamics
Sequential prediction has great value for resource allocation due to its capability in
analyzing intents for next prediction. A fundamental challenge arises from real-world …
analyzing intents for next prediction. A fundamental challenge arises from real-world …