Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
Adaptive Attentional Network for Few-Shot Knowledge Graph Completion
Few-shot Knowledge Graph (KG) completion is a focus of current research, where each task aims at querying unseen facts of a relation given its few-shot reference entity pairs. Recent attempts solve this problem by learni…
Knowledge Graph CompletionLink PredictionDrug Repurposing for COVID-19 via Knowledge Graph Completion
Objective: To discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods. Methods: We propose a novel, integrative, and neural network-based literature-ba…
Knowledge Graph CompletionMulDE: Multi-teacher Knowledge Distillation for Low-dimensional Knowledge Graph Embeddings
Link prediction based on knowledge graph embeddings (KGE) aims to predict new triples to automatically construct knowledge graphs (KGs). However, recent KGE models achieve performance improvements by excessively increasi…
Graph EmbeddingKnowledge DistillationKnowledge Graph CompletionKnowledge Graph Embedding+3Neural, Symbolic and Neural-Symbolic Reasoning on Knowledge Graphs
Knowledge graph reasoning is the fundamental component to support machine learning applications such as information extraction, information retrieval, and recommendation. Since knowledge graphs can be viewed as the discr…
Information RetrievalKnowledge Graph CompletionKnowledge GraphsQuestion Answering+2RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion
Many graph embedding approaches have been proposed for knowledge graph completion via link prediction. Among those, translating embedding approaches enjoy the advantages of light-weight structure, high efficiency and gre…
Graph EmbeddingKnowledge Graph CompletionLink PredictionRelation+1Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion
A case-based reasoning (CBR) system solves a new problem by retrieving `cases' that are similar to the given problem. If such a system can achieve high accuracy, it is appealing owing to its simplicity, interpretability,…
Knowledge Graph CompletionLink PredictionWorld KnowledgeTeMP: Temporal Message Passing for Temporal Knowledge Graph Completion
Inferring missing facts in temporal knowledge graphs (TKGs) is a fundamental and challenging task. Previous works have approached this problem by augmenting methods for static knowledge graphs to leverage time-dependent …
ImputationKnowledge Graph CompletionKnowledge GraphsTemporal Knowledge Graph CompletionMultilingual Knowledge Graph Completion via Ensemble Knowledge Transfer
Predicting missing facts in a knowledge graph (KG) is a crucial task in knowledge base construction and reasoning, and it has been the subject of much research in recent works using KG embeddings. While existing KG embed…
Knowledge Base ConstructionKnowledge Graph CompletionSelf-LearningTransfer LearningCoLAKE: Contextualized Language and Knowledge Embedding
With the emerging branch of incorporating factual knowledge into pre-trained language models such as BERT, most existing models consider shallow, static, and separately pre-trained entity embeddings, which limits the per…
Entity EmbeddingsKnowledge Graph CompletionKnowledge ProbingRTFE: A Recursive Temporal Fact Embedding Framework for Temporal Knowledge Graph Completion
Static knowledge graph (SKG) embedding (SKGE) has been studied intensively in the past years. Recently, temporal knowledge graph (TKG) embedding (TKGE) has emerged. In this paper, we propose a Recursive Temporal Fact Emb…
Graph EmbeddingKnowledge Graph CompletionKnowledge GraphsTemporal Knowledge Graph CompletionMulti-hop Attention Graph Neural Network
Self-attention mechanism in graph neural networks (GNNs) led to state-of-the-art performance on many graph representation learning tasks. Currently, at every layer, attention is computed between connected pairs of nodes …
Graph Neural NetworkGraph Representation LearningKnowledge Graph CompletionNode Classification+1Multi-Relational Embedding for Knowledge Graph Representation and Analysis
Multi-relational data, such as knowledge graphs, bibliographic data, and information networks are prevalent in real-world datasets. Managing, exploring, and utilizing these large and complex datasets effectively are chal…
Computational EfficiencyGraph EmbeddingKnowledge Graph CompletionKnowledge Graph Embedding+8Inductively Representing Out-of-Knowledge-Graph Entities by Optimal Estimation Under Translational Assumptions
Conventional Knowledge Graph Completion (KGC) assumes that all test entities appear during training. However, in real-world scenarios, Knowledge Graphs (KG) evolve fast with out-of-knowledge-graph (OOKG) entities added f…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge GraphsQuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings
We propose a simple yet effective embedding model to learn quaternion embeddings for entities and relations in knowledge graphs. Our model aims to enhance correlations between head and tail entities given a relation with…
Knowledge Graph CompletionKnowledge Graph EmbeddingsKnowledge GraphsRelationInductive Learning on Commonsense Knowledge Graph Completion
Commonsense knowledge graph (CKG) is a special type of knowledge graph (KG), where entities are composed of free-form text. However, most existing CKG completion methods focus on the setting where all the entities are pr…
DecoderEntity EmbeddingsInductive LearningKnowledge Graph Completion+2EM-RBR: a reinforced framework for knowledge graph completion from reasoning perspective
Knowledge graph completion aims to predict the new links in given entities among the knowledge graph (KG). Most mainstream embedding methods focus on fact triplets contained in the given KG, however, ignoring the rich ba…
Knowledge Graph CompletionLink PredictionRelationCoDEx: A Comprehensive Knowledge Graph Completion Benchmark
We present CoDEx, a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. In terms of scope, C…
BenchmarkingKnowledge Graph CompletionKnowledge GraphsLink Prediction+1Type-augmented Relation Prediction in Knowledge Graphs
Knowledge graphs (KGs) are of great importance to many real world applications, but they generally suffer from incomplete information in the form of missing relations between entities. Knowledge graph completion (also kn…
Knowledge Graph CompletionKnowledge GraphsPredictionRelation+2LowFER: Low-rank Bilinear Pooling for Link Prediction
Knowledge graphs are incomplete by nature, with only a limited number of observed facts from the world knowledge being represented as structured relations between entities. To partly address this issue, an important task…
Knowledge Graph CompletionKnowledge GraphsLink PredictionPrediction+2Quaternion Graph Neural Networks
Recently, graph neural networks (GNNs) have become an important and active research direction in deep learning. It is worth noting that most of the existing GNN-based methods learn graph representations within the Euclid…
General ClassificationGraph ClassificationGraph EmbeddingKnowledge Graph Completion+4