Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learning
Probabilistic logical rule learning has shown great strength in logical rule mining and knowledge graph completion. It learns logical rules to predict missing edges by reasoning on existing edges in the knowledge graph. …
Knowledge Graph CompletionKnowledge GraphsLink PredictionSelf-Distillation with Meta Learning for Knowledge Graph Completion
In this paper, we propose a selfdistillation framework with meta learning(MetaSD) for knowledge graph completion with dynamic pruning, which aims to learn compressed graph embeddings and tackle the longtail samples. Spec…
Knowledge Graph CompletionMeta-LearningTransfer LearningKnowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs
Knowledge Graphs (KGs) play a crucial role in enhancing e-commerce system performance by providing structured information about entities and their relationships, such as complementary or substitutable relations between p…
Knowledge Graph CompletionKnowledge GraphsPrompt EngineeringRecommendation Systems+1HaSa: Hardness and Structure-Aware Contrastive Knowledge Graph Embedding
We consider a contrastive learning approach to knowledge graph embedding (KGE) via InfoNCE. For KGE, efficient learning relies on augmenting the training data with negative triples. However, most KGE works overlook the b…
Contrastive LearningGraph EmbeddingKnowledge Graph CompletionKnowledge Graph Embedding+2Iteratively Learning Representations for Unseen Entities with Inter-Rule Correlations
Recent work on knowledge graph completion (KGC) focused on learning embeddings of entities and relations in knowledge graphs. These embedding methods require that all test entities are observed at training time, resultin…
Knowledge Graph CompletionKnowledge GraphsLink PredictionTriple ClassificationA Knowledge Graph Perspective on Supply Chain Resilience
Global crises and regulatory developments require increased supply chain transparency and resilience. Companies do not only need to react to a dynamic environment but have to act proactively and implement measures to pre…
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+1ACTC: Active Threshold Calibration for Cold-Start Knowledge Graph Completion
Self-supervised knowledge-graph completion (KGC) relies on estimating a scoring model over (entity, relation, entity)-tuples, for example, by embedding an initial knowledge graph. Prediction quality can be improved by ca…
Knowledge Graph CompletionRelationBuilding Neural Networks on Matrix Manifolds: A Gyrovector Space Approach
Matrix manifolds, such as manifolds of Symmetric Positive Definite (SPD) matrices and Grassmann manifolds, appear in many applications. Recently, by applying the theory of gyrogroups and gyrovector spaces that is a power…
Action RecognitionKnowledge Graph CompletionTemporal Action LocalizationGeometric Relational Embeddings: A Survey
Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for structured/relational reasoning, typically …
Hierarchical Multi-label ClassificationKnowledge Graph CompletionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion
Knowledge graphs (KGs), as a structured form of knowledge representation, have been widely applied in the real world. Recently, few-shot knowledge graph completion (FKGC), which aims to predict missing facts for unseen r…
Graph Neural NetworkKnowledge Graph CompletionKnowledge GraphsMeta-Learning+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+3Pre-training Transformers for Knowledge Graph Completion
Learning transferable representation of knowledge graphs (KGs) is challenging due to the heterogeneous, multi-relational nature of graph structures. Inspired by Transformer-based pretrained language models' success on le…
Knowledge Graph CompletionKnowledge GraphsFarspredict: A benchmark dataset for link prediction
Link prediction with knowledge graph embedding (KGE) is a popular method for knowledge graph completion. Furthermore, training KGEs on non-English knowledge graph promote knowledge extraction and knowledge graph reasonin…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingLink Prediction+1Knowledge Graphs: Opportunities and Challenges
With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately. As graph data, knowledge graphs accumulate…
Knowledge Graph CompletionKnowledge Graph EmbeddingsKnowledge GraphsEnhancing Embedding Representations of Biomedical Data using Logic Knowledge
Knowledge Graph Embeddings (KGE) have become a quite popular class of models specifically devised to deal with ontologies and graph structure data, as they can implicitly encode statistical dependencies between entities …
Knowledge Graph CompletionKnowledge Graph EmbeddingsKnowledge GraphsRelational ReasoningComprehensive Event Representations using Event Knowledge Graphs and Natural Language Processing
Recent work has utilised knowledge-aware approaches to natural language understanding, question answering, recommendation systems, and other tasks. These approaches rely on well-constructed and large-scale knowledge grap…
Event ExtractionKnowledge Graph CompletionKnowledge GraphsNatural Language Understanding+3Neural Compositional Rule Learning for Knowledge Graph Reasoning
Learning logical rules is critical to improving reasoning in KGs. This is due to their ability to provide logical and interpretable explanations when used for predictions, as well as their ability to generalize to other …
Knowledge Graph CompletionSystematic Generalization