Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
Relational Message Passing for Knowledge Graph Completion
Knowledge graph completion aims to predict missing relations between entities in a knowledge graph. In this work, we propose a relational message passing method for knowledge graph completion. Different from existing emb…
Knowledge Graph CompletionRelationRelation PredictionRevisiting Evaluation of Knowledge Base Completion Models
Representing knowledge graphs (KGs) by learning embeddings for entities and relations has led to accurate models for existing KG completion benchmarks. However, due to the open-world assumption of existing KGs, evaluatio…
Knowledge Base CompletionKnowledge Graph CompletionKnowledge GraphsTriple ClassificationA Survey on Knowledge Graphs: Representation, Acquisition and Applications
Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level…
Graph EmbeddingGraph Representation LearningKnowledge Graph CompletionKnowledge Graph Embedding+5Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs
Large-scale knowledge graphs (KGs) are shown to become more important in current information systems. To expand the coverage of KGs, previous studies on knowledge graph completion need to collect adequate training instan…
Knowledge Graph CompletionKnowledge GraphsRelational ReasoningZero-Shot LearningKnowledge Graph Embedding via Graph Attenuated Attention Networks
Knowledge graphs contain a wealth of real-world knowledge that can provide strong support for artificial intelligence applications. Much progress has been made in knowledge graph completion, state-of-the-art models are b…
Graph EmbeddingKnowledge Base CompletionKnowledge Graph CompletionKnowledge Graph Embedding+5Clustering as an Evaluation Protocol for Knowledge Embedding Representation of Categorised Multi-relational Data in the Clinical Domain
Learning knowledge representation is an increasingly important technology applicable in many domain-specific machine learning problems. We discuss the effectiveness of traditional Link Prediction or Knowledge Graph Compl…
ClusteringKnowledge Graph CompletionLink PredictionPredictionFrom Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)
This paper proposes a tentative and original survey of meeting points between Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have been developing quite separately in the last thre…
BIG-bench Machine LearningInductive logic programmingKnowledge Graph CompletionBinarized Canonical Polyadic Decomposition for Knowledge Graph Completion
Methods based on vector embeddings of knowledge graphs have been actively pursued as a promising approach to knowledge graph completion.However, embedding models generate storage-inefficient representations, particularly…
Knowledge Graph CompletionKnowledge GraphsPretrain-KGEs: Learning Knowledge Representation from Pretrained Models for Knowledge Graph Embeddings
Learning knowledge graph embeddings (KGEs) is an efficient approach to knowledge graph completion. Conventional KGEs often suffer from limited knowledge representation, which causes less accuracy especially when training…
Knowledge Graph CompletionKnowledge Graph EmbeddingsKnowledge GraphsLink Prediction+2Few-Shot Knowledge Graph Completion
Knowledge graphs (KGs) serve as useful resources for various natural language processing applications. Previous KG completion approaches require a large number of training instances (i.e., head-tail entity pairs) for eve…
Knowledge Graph CompletionKnowledge GraphsOne-Shot LearningRelationLearning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
Knowledge graph embedding, which aims to represent entities and relations as low dimensional vectors (or matrices, tensors, etc.), has been shown to be a powerful technique for predicting missing links in knowledge graph…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graph Embeddings+2Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition
Knowledge Graph (KG) embedding has attracted more attention in recent years. Most KG embedding models learn from time-unaware triples. However, the inclusion of temporal information beside triples would further improve t…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingLink Prediction+4Inductive Relation Prediction by Subgraph Reasoning
The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i.e., embeddings) of entities and relations. However, these embedding-based methods do not expl…
Graph EmbeddingGraph Neural NetworkInductive BiasInductive knowledge graph completion+9KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text. In contrast, knowledge embedding (KE) methods can effectively represent the relational facts in knowledge graphs (KGs) wi…
Entity EmbeddingsEntity TypingInductive knowledge graph completionKnowledge Graph Completion+7A Re-evaluation of Knowledge Graph Completion Methods
Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs. A vast number of state-of-the-art KGC techniques have got published at top conferences in several research…
Knowledge Graph CompletionKnowledge GraphsLink PredictionRelation Adversarial Network for Low Resource Knowledge Graph Completion
Knowledge Graph Completion (KGC) has been proposed to improve Knowledge Graphs by filling in missing connections via link prediction or relation extraction. One of the main difficulties for KGC is a low resource problem.…
Domain AdaptationKnowledge Graph CompletionKnowledge GraphsLink Prediction+3Relation Prediction for Unseen-Entities Using Entity-Word Graphs
Knowledge graphs (KGs) are generally used for various NLP tasks. However, as KGs still miss some information, it is necessary to develop Knowledge Graph Completion (KGC) methods. Most KGC researches do not focus on the O…
Knowledge Graph CompletionKnowledge GraphsRelationRelation PredictionUsing Pairwise Occurrence Information to Improve Knowledge Graph Completion on Large-Scale Datasets
Bilinear models such as DistMult and ComplEx are effective methods for knowledge graph (KG) completion. However, they require large batch sizes, which becomes a performance bottleneck when training on large scale dataset…
Knowledge Graph CompletionLink PredictionLearning High-order Structural and Attribute information by Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding
The goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding spaces. Many recent works have demonstrated the benefits of knowledge graph embedding on kn…
AttributeGraph AttentionGraph EmbeddingKnowledge Graph Completion+4Representation Learning with Ordered Relation Paths for Knowledge Graph Completion
Incompleteness is a common problem for existing knowledge graphs (KGs), and the completion of KG which aims to predict links between entities is challenging. Most existing KG completion methods only consider the direct r…
Knowledge Graph CompletionKnowledge GraphsLink PredictionPrediction+2