Papers Temporal Knowledge Graph Completion
“Temporal Knowledge Graph Completion” 태그가 달린 논문 42편 · 필터 해제
Towards Pattern-aware Data Augmentation for Temporal Knowledge Graph Completion
Predicting missing facts for temporal knowledge graphs (TKGs) is a fundamental task, called temporal knowledge graph completion (TKGC). One key challenge in this task is the imbalance in data distribution, where facts ar…
Data AugmentationKnowledge Graph CompletionKnowledge GraphsTemporal Knowledge Graph CompletionLearning Granularity Representation for Temporal Knowledge Graph Completion
Temporal Knowledge Graphs (TKGs) incorporate temporal information to reflect the dynamic structural knowledge and evolutionary patterns of real-world facts. Nevertheless, TKGs are still limited in downstream applications…
Knowledge Graph CompletionKnowledge GraphsLink PredictionRepresentation Learning+1Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion
Temporal knowledge graph completion aims to infer the missing facts in temporal knowledge graphs. Current approaches usually embed factual knowledge into continuous vector space and apply geometric operations to learn po…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graphs+3Mitigating Heterogeneity among Factor Tensors via Lie Group Manifolds for Tensor Decomposition Based Temporal Knowledge Graph Embedding
Recent studies have highlighted the effectiveness of tensor decomposition methods in the Temporal Knowledge Graphs Embedding (TKGE) task. However, we found that inherent heterogeneity among factor tensors in tensor decom…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsLink Prediction+2IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion
Temporal Knowledge Graphs (TKGs) incorporate a temporal dimension, allowing for a precise capture of the evolution of knowledge and reflecting the dynamic nature of the real world. Typically, TKGs contain complex geometr…
Knowledge Graph CompletionKnowledge GraphsTemporal Knowledge Graph CompletionTemporal Knowledge Graph Completion with Time-sensitive Relations in Hypercomplex Space
Temporal knowledge graph completion (TKGC) aims to fill in missing facts within a given temporal knowledge graph at a specific time. Existing methods, operating in real or complex spaces, have demonstrated promising perf…
Knowledge Graph CompletionTemporal Knowledge Graph CompletionChain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion
Temporal Knowledge Graph Completion (TKGC) is a complex task involving the prediction of missing event links at future timestamps by leveraging established temporal structural knowledge. This paper aims to provide a comp…
Data AugmentationKnowledge Graph CompletionKnowledge GraphsLink Prediction+2Re-Temp: Relation-Aware Temporal Representation Learning for Temporal Knowledge Graph Completion
Temporal Knowledge Graph Completion (TKGC) under the extrapolation setting aims to predict the missing entity from a fact in the future, posing a challenge that aligns more closely with real-world prediction problems. Ex…
Knowledge Graph CompletionRelationRepresentation LearningTemporal Knowledge Graph CompletionLeveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge Graphs
Most knowledge graph completion (KGC) methods learn latent representations of entities and relations of a given graph by mapping them into a vector space. Although the majority of these methods focus on static knowledge …
Knowledge Graph CompletionKnowledge GraphsLink PredictionRepresentation Learning+3A 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 and industry. However, TKGs often suffer from…
Knowledge Graph CompletionKnowledge GraphsMissing ElementsTemporal Knowledge Graph CompletionHistory Repeats: Overcoming Catastrophic Forgetting For Event-Centric Temporal Knowledge Graph Completion
Temporal knowledge graph (TKG) completion models typically rely on having access to the entire graph during training. However, in real-world scenarios, TKG data is often received incrementally as events unfold, leading t…
ClusteringKnowledge Graph CompletionTemporal Knowledge Graph CompletionNeuSTIP: A Novel Neuro-Symbolic Model for Link and Time Prediction in Temporal Knowledge Graphs
While Knowledge Graph Completion (KGC) on static facts is a matured field, Temporal Knowledge Graph Completion (TKGC), that incorporates validity time into static facts is still in its nascent stage. The KGC methods fall…
Knowledge Graph CompletionKnowledge GraphsLanguage ModelingLanguage Modelling+4Pre-trained Language Model with Prompts for Temporal Knowledge Graph Completion
Temporal Knowledge graph completion (TKGC) is a crucial task that involves reasoning at known timestamps to complete the missing part of facts and has attracted more and more attention in recent years. Most existing meth…
Knowledge Graph CompletionKnowledge GraphsLanguage ModelingLanguage Modelling+1Incorporating Structured Sentences with Time-enhanced BERT for Fully-inductive Temporal Relation Prediction
Temporal relation prediction in incomplete temporal knowledge graphs (TKGs) is a popular temporal knowledge graph completion (TKGC) problem in both transductive and inductive settings. Traditional embedding-based TKGC mo…
Knowledge Graph CompletionKnowledge GraphsRelationRelation Prediction+1Improving 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 knwoledge graph (TKG). Most previous TKGC methods only consider predicting the missing links among the entitie…
Few-Shot LearningInductive LearningKnowledge Graph CompletionKnowledge Graphs+3Logic and Commonsense-Guided Temporal Knowledge Graph Completion
A temporal knowledge graph (TKG) stores the events derived from the data involving time. Predicting events is extremely challenging due to the time-sensitive property of events. Besides, the previous TKG completion (TKGC…
Causal InferenceKnowledge Graph CompletionTemporal Knowledge Graph CompletionFew-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 can only deal with the KG entities seen in the trai…
Inductive LearningKnowledge Graph CompletionKnowledge GraphsLink Prediction+2Search to Pass Messages for Temporal Knowledge Graph Completion
Completing missing facts is a fundamental task for temporal knowledge graphs (TKGs). Recently, graph neural network (GNN) based methods, which can simultaneously explore topological and temporal information, have become …
Graph Neural NetworkKnowledge Graph CompletionKnowledge GraphsLink Prediction+3Along the Time: Timeline-traced Embedding for Temporal Knowledge Graph Completion
Recent years have witnessed remarkable progress on knowledge graph embedding (KGE) methods to learn the representations of entities and relations in static knowledge graphs (SKGs). However, knowledge changes over time. I…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graphs+2Temporal Knowledge Graph Completion with Approximated Gaussian Process Embedding
Knowledge Graphs (KGs) stores world knowledge that benefits various reasoning-based applications. Due to their incompleteness, a fundamental task for KGs, which is known as Knowledge Graph Completion (KGC), is to perform…
Gaussian ProcessesKnowledge Graph CompletionKnowledge GraphsLink Prediction+2