Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
Knowledge Graph Completion with Pre-trained Multimodal Transformer and Twins Negative Sampling
Knowledge graphs (KGs) that modelings the world knowledge as structural triples are inevitably incomplete. Such problems still exist for multimodal knowledge graphs (MMKGs). Thus, knowledge graph completion (KGC) is of g…
Knowledge Graph CompletionKnowledge GraphsLink PredictionWorld KnowledgeKnowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion
Knowledge Graph Completion (KGC) has been recently extended to multiple knowledge graph (KG) structures, initiating new research directions, e.g. static KGC, temporal KGC and few-shot KGC. Previous works often design KGC…
Knowledge Graph CompletionHierarchical Relational Learning for Few-Shot Knowledge Graph Completion
Knowledge graphs (KGs) are known for their large scale and knowledge inference ability, but are also notorious for the incompleteness associated with them. Due to the long-tail distribution of the relations in KGs, few-s…
Knowledge Graph CompletionKnowledge GraphsRelational ReasoningTripletLearning Representations for Hyper-Relational Knowledge Graphs
Knowledge graphs (KGs) have gained prominence for their ability to learn representations for uni-relational facts. Recently, research has focused on modeling hyper-relational facts, which move beyond the restriction of u…
Knowledge Graph CompletionKnowledge GraphsA Review of Knowledge Graph Completion
Information extraction methods proved to be effective at triple extraction from structured or unstructured data. The organization of such triples in the form of (head entity, relation, tail entity) is called the construc…
Graph Neural NetworkKnowledge Graph CompletionKnowledge GraphsRepresentation LearningKGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models
Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples. To augment KGs with new knowledge, researchers proposed models for KG Completion (KGC) tasks such as link prediction; i.e., answeri…
Knowledge Graph CompletionKnowledge GraphsLink PredictionGreenKGC: A Lightweight Knowledge Graph Completion Method
Knowledge graph completion (KGC) aims to discover missing relationships between entities in knowledge graphs (KGs). Most prior KGC work focuses on learning embeddings for entities and relations through a simple scoring f…
Edge-computingKnowledge Graph CompletionKnowledge GraphsLink Prediction+2KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion
Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional spaces and have become the \textit{de-facto} standard for knowledge graph completion. Most existing KGE methods suffer from the sparsi…
Contrastive LearningKnowledge Graph CompletionKnowledge Graph EmbeddingsKnowledge GraphsProjB: An Improved Bilinear Biased ProjE model for Knowledge Graph Completion
Knowledge Graph Embedding (KGE) methods have gained enormous attention from a wide range of AI communities including Natural Language Processing (NLP) for text generation, classification and context induction. Embedding …
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graphs+1KG-NSF: Knowledge Graph Completion with a Negative-Sample-Free Approach
Knowledge Graph (KG) completion is an important task that greatly benefits knowledge discovery in many fields (e.g. biomedical research). In recent years, learning KG embeddings to perform this task has received consider…
Knowledge Graph CompletionLink PredictionReFactor GNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective
Factorisation-based Models (FMs), such as DistMult, have enjoyed enduring success for Knowledge Graph Completion (KGC) tasks, often outperforming Graph Neural Networks (GNNs). However, unlike GNNs, FMs struggle to incorp…
Knowledge Graph CompletionAn Overview of Distant Supervision for Relation Extraction with a Focus on Denoising and Pre-training Methods
Relation Extraction (RE) is a foundational task of natural language processing. RE seeks to transform raw, unstructured text into structured knowledge by identifying relational information between entity pairs found in t…
DenoisingKnowledge Graph CompletionLanguage ModelingLanguage Modelling+4Explainable Sparse Knowledge Graph Completion via High-order Graph Reasoning Network
Knowledge Graphs (KGs) are becoming increasingly essential infrastructures in many applications while suffering from incompleteness issues. The KG completion task (KGC) automatically predicts missing facts based on an in…
Knowledge Graph CompletionKnowledge GraphsVocal Bursts Intensity PredictionVEM$^2$L: A Plug-and-play Framework for Fusing Text and Structure Knowledge on Sparse Knowledge Graph Completion
Knowledge Graph Completion (KGC) aims to reason over known facts and infer missing links but achieves weak performances on those sparse Knowledge Graphs (KGs). Recent works introduce text information as auxiliary feature…
Knowledge DistillationKnowledge Graph CompletionKnowledge GraphsMissing Elements+1StATIK: Structure and Text for Inductive Knowledge Graph Completion
Knowledge graphs (KGs) often represent knowledge bases that are incomplete. Machine learning models can alleviate this by helping automate graph completion. Recently, there has been growing interest in completing knowled…
Inductive knowledge graph completionKnowledge Graph CompletionKnowledge GraphsLearning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion
Prior work on integrating text corpora with knowledge graphs (KGs) to improve Knowledge Graph Embedding (KGE) have obtained good performance for entities that co-occur in sentences in text corpora. Such sentences (textua…
Entity EmbeddingsGraph EmbeddingKnowledge Graph CompletionKnowledge Graph Embedding+4ComDensE : Combined Dense Embedding of Relation-aware and Common Features for Knowledge Graph Completion
Real-world knowledge graphs (KG) are mostly incomplete. The problem of recovering missing relations, called KG completion, has recently become an active research area. Knowledge graph (KG) embedding, a low-dimensional re…
Inductive BiasKnowledge Graph CompletionKnowledge GraphsLink Prediction+2ER: Equivariance Regularizer for Knowledge Graph Completion
Tensor factorization and distanced based models play important roles in knowledge graph completion (KGC). However, the relational matrices in KGC methods often induce a high model complexity, bearing a high risk of overf…
Knowledge Graph CompletionRelation PredictionGeometry Interaction Knowledge Graph Embeddings
Knowledge graph (KG) embeddings have shown great power in learning representations of entities and relations for link prediction tasks. Previous work usually embeds KGs into a single geometric space such as Euclidean spa…
Knowledge Graph CompletionKnowledge Graph EmbeddingsLink PredictionExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completion
Knowledge graphs are inherently incomplete. Therefore substantial research has been directed toward knowledge graph completion (KGC), i.e., predicting missing triples from the information represented in the knowledge gra…
Knowledge Graph CompletionKnowledge Graphs