Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
Knowledge Graph Completion with Counterfactual Augmentation
Graph Neural Networks (GNNs) have demonstrated great success in Knowledge Graph Completion (KGC) by modeling how entities and relations interact in recent years. However, most of them are designed to learn from the obser…
counterfactualKnowledge Graph CompletionRelationTripletKnowledge Graph Completion based on Tensor Decomposition for Disease Gene Prediction
Accurate identification of disease genes has consistently been one of the keys to decoding a disease's molecular mechanism. Most current approaches focus on constructing biological networks and utilizing machine learning…
Knowledge Graph CompletionTensor DecompositionNNKGC: Improving Knowledge Graph Completion with Node Neighborhoods
Knowledge graph completion (KGC) aims to discover missing relations of query entities. Current text-based models utilize the entity name and description to infer the tail entity given the head entity and a certain relati…
Knowledge Graph CompletionLink PredictionToward Degree Bias in Embedding-Based Knowledge Graph Completion
A fundamental task for knowledge graphs (KGs) is knowledge graph completion (KGC). It aims to predict unseen edges by learning representations for all the entities and relations in a KG. A common concern when learning re…
Data AugmentationKnowledge Graph CompletionKnowledge GraphsA Comprehensive Survey on Automatic Knowledge Graph Construction
Automatic knowledge graph construction aims to manufacture structured human knowledge. To this end, much effort has historically been spent extracting informative fact patterns from different data sources. However, more …
graph constructionKnowledge Graph CompletionKnowledge GraphsSurveyFFHR: Fully and Flexible Hyperbolic Representation for Knowledge Graph Completion
Learning hyperbolic embeddings for knowledge graph (KG) has gained increasing attention due to its superiority in capturing hierarchies. However, some important operations in hyperbolic space still lack good definitions,…
Knowledge Graph CompletionRepresentation LearningA Pre-training Framework for Knowledge Graph Completion
Knowledge graph completion (KGC) is one of the effective methods to identify new facts in knowledge graph. Except for a few methods based on graph network, most of KGC methods trend to be trained based on independent tri…
Knowledge Graph CompletionPubGraph: A Large-Scale Scientific Knowledge Graph
Research publications are the primary vehicle for sharing scientific progress in the form of new discoveries, methods, techniques, and insights. Unfortunately, the lack of a large-scale, comprehensive, and easy-to-use re…
Community DetectionGraph EmbeddingInductive LearningKnowledge Graph Completion+2Interaction Order Prediction for Temporal Graphs
Link prediction in graphs is a task that has been widely investigated. It has been applied in various domains such as knowledge graph completion, content/item recommendation, social network recommendations and so on. The…
Knowledge Graph CompletionLink PredictionPredictionKnowledge Graph Completion Method Combined With Adaptive Enhanced Semantic Information
Translation models tend to ignore the rich semantic information in triads in the process of knowledge graph complementation. To remedy this shortcoming, this paper constructs a knowledge graph complementation method that…
Knowledge Graph CompletionTranslationDouble Equivariance for Inductive Link Prediction for Both New Nodes and New Relation Types
The task of fully inductive link prediction in knowledge graphs has gained significant attention, with various graph neural networks being proposed to address it. This task presents greater challenges than traditional in…
Inductive Link PredictionKnowledge Graph CompletionKnowledge GraphsLink Prediction+5Can Persistent Homology provide an efficient alternative for Evaluation of Knowledge Graph Completion Methods?
In this paper we present a novel method, $\textit{Knowledge Persistence}$ ($\mathcal{KP}$), for faster evaluation of Knowledge Graph (KG) completion approaches. Current ranking-based evaluation is quadratic in the size o…
Knowledge Graph CompletionTopological Data AnalysisKnowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules
Knowledge graphs (KGs) play a crucial role in many applications, such as question answering, but incompleteness is an urgent issue for their broad application. Much research in knowledge graph completion (KGC) has been p…
Knowledge Graph CompletionKnowledge Graph EmbeddingsKnowledge GraphsQuestion AnsweringA Survey On Few-shot Knowledge Graph Completion with Structural and Commonsense Knowledge
Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-f…
Few-Shot LearningGraph Representation LearningKnowledge Graph CompletionKnowledge Graphs+1A Retrieve-and-Read Framework for Knowledge Graph Link Prediction
Knowledge graph (KG) link prediction aims to infer new facts based on existing facts in the KG. Recent studies have shown that using the graph neighborhood of a node via graph neural networks (GNNs) provides more useful …
Knowledge Graph CompletionLink PredictionPredictionEfficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor Decomposition
Numerous Graph Neural Networks (GNNs) have been developed to tackle the challenge of Knowledge Graph Embedding (KGE). However, many of these approaches overlook the crucial role of relation information and inadequately i…
Contrastive LearningGeneral KnowledgeGraph EmbeddingKnowledge Graph Completion+5Pseudo-Riemannian Embedding Models for Multi-Relational Graph Representations
In this paper we generalize single-relation pseudo-Riemannian graph embedding models to multi-relational networks, and show that the typical approach of encoding relations as manifold transformations translates from the …
Graph EmbeddingKnowledge Graph CompletionLink PredictionLogic 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 CompletionBESS: Balanced Entity Sampling and Sharing for Large-Scale Knowledge Graph Completion
We present the award-winning submission to the WikiKG90Mv2 track of OGB-LSC@NeurIPS 2022. The task is link-prediction on the large-scale knowledge graph WikiKG90Mv2, consisting of 90M+ nodes and 600M+ edges. Our solution…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingLink PredictionRelation-dependent Contrastive Learning with Cluster Sampling for Inductive Relation Prediction
Relation prediction is a task designed for knowledge graph completion which aims to predict missing relationships between entities. Recent subgraph-based models for inductive relation prediction have received increasing …
Contrastive LearningInductive Relation PredictionKnowledge Graph CompletionPrediction+3