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 …
Learn from relational correlations and periodic events for temporal knowledge graph reasoning
Reasoning on temporal knowledge graphs (TKGR), aiming to infer missing events along the
timeline, has been widely studied to alleviate incompleteness issues in TKG, which is …
timeline, has been widely studied to alleviate incompleteness issues in TKG, which is …
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 joint relational co-evolution in spatial-temporal knowledge graph for SMEs supply chain prediction
To effectively explore the supply chain relationships among Small and Medium-sized
Enterprises (SMEs), some remarkable progress in such a relation modeling problem …
Enterprises (SMEs), some remarkable progress in such a relation modeling problem …
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 …
HGE: Embedding Temporal Knowledge Graphs in a Product Space of Heterogeneous Geometric Subspaces
Temporal knowledge graphs represent temporal facts (s, p, o,?) relating a subject s and an
object o via a relation label p at time?, where? could be a time point or time interval …
object o via a relation label p at time?, where? could be a time point or time interval …
A Survey on Temporal Knowledge Graph: Representation Learning and Applications
Knowledge graphs have garnered significant research attention and are widely used to
enhance downstream applications. However, most current studies mainly focus on static …
enhance downstream applications. However, most current studies mainly focus on static …
Learning to compensate for lack of information: Extracting latent knowledge for effective temporal knowledge graph completion
The goal of temporal knowledge graph embedding (TKGE) is to represent the entities and
relations in a given temporal knowledge graph (TKG) as low-dimensional vectors (ie …
relations in a given temporal knowledge graph (TKG) as low-dimensional vectors (ie …
HTCCN: Temporal Causal Convolutional Networks with Hawkes Process for Extrapolation Reasoning in Temporal Knowledge Graphs
T Chen, J Long, L Yang, Z Wang… - Proceedings of the …, 2024 - aclanthology.org
Temporal knowledge graphs (TKGs) serve as powerful tools for storing and modeling
dynamic facts, holding immense potential in anticipating future facts. Since future facts are …
dynamic facts, holding immense potential in anticipating future facts. Since future facts are …
TODEAR: Promoting Explainable TKG Reasoning through Temporal Offset Enhanced Dynamic Embedding and Adaptive Reinforcement Learning
Abstract Recently, Reinforcement Learning (RL) is utilized in Temporal Knowledge Graph
(TKG) reasoning to generate analyzable reasoning paths, which achieves explainable …
(TKG) reasoning to generate analyzable reasoning paths, which achieves explainable …