Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations
Learning good representations on multi-relational graphs is essential to knowledge base completion (KBC). In this paper, we propose a new self-supervised training objective for multi-relational graph representation learn…
Graph Representation LearningKnowledge Base CompletionKnowledge Graph CompletionKnowledge Graph Embedding+6Cycle Representation Learning for Inductive Relation Prediction
In recent years, algebraic topology and its modern development, the theory of persistent homology, has shown great potential in graph representation learning. In this paper, based on the mathematics of algebraic topology…
Graph Representation LearningInductive Relation PredictionKnowledge Graph CompletionKnowledge Graphs+4Is There More Pattern in Knowledge Graph? Exploring Proximity Pattern for Knowledge Graph Embedding
Modeling of relation pattern is the core focus of previous Knowledge Graph Embedding works, which represents how one entity is related to another semantically by some explicit relation. However, there is a more natural a…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingRelationExplainable GNN-Based Models over Knowledge Graphs
Graph Neural Networks (GNNs) are often used to realise learnable transformations of graph data. While effective in practice, GNNs make predictions via numeric manipulations in an embedding space, so their output cannot b…
Knowledge Graph CompletionKnowledge GraphsKnowledge Graph Completion as Tensor Decomposition: A Genreal Form and Tensor N-rank Regularization
Knowledge graph completion (KGC) is a 3rd-order binary tensor completion task. Tensor decomposition based (TDB) models have shown great performance in KGC. In this paper, we summarize existing TDB models and derive a gen…
FormKnowledge Graph CompletionTensor DecompositionA Topological View of Rule Learning in Knowledge Graphs
Inductive relation prediction is an important learning task for knowledge graph completion. One can use the existence of rules, namely a sequence of relations, to predict the relation between two entities. Previous works…
Inductive Relation PredictionKnowledge Graph CompletionKnowledge GraphsRelation+1Inductive Relation Prediction Using Analogy Subgraph Embeddings
Prevailing methods for relation prediction in heterogeneous graphs aim at learning latent representations (i.e., embeddings) of observed nodes and relations, and thus are limited to the transductive setting where the rel…
Inductive BiasInductive Relation PredictionKnowledge Graph CompletionPrediction+2TaCE: Time-aware Convolutional Embedding Learning for Temporal Knowledge Graph Completion
Temporal knowledge graph completion (TKGC) is a challenging task to infer the missing component for quadruples. The key challenge lies at how to integrate time information into the embeddings of entities and relations. R…
Knowledge Graph CompletionKnowledge GraphsTemporal Knowledge Graph CompletionHow Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View
Knowledge Graph Embedding (KGE) aims to learn representations for entities and relations. Most KGE models have gained great success, especially on extrapolation scenarios. Specifically, given an unseen triple (h, r, t), …
Graph EmbeddingGraph Neural NetworkKnowledge Graph CompletionKnowledge Graph Embedding+1Temporal Knowledge Graph Completion using Box Embeddings
Knowledge graph completion is the task of inferring missing facts based on existing data in a knowledge graph. Temporal knowledge graph completion (TKGC) is an extension of this task to temporal knowledge graphs, where e…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graphs+1On Event-Driven Knowledge Graph Completion in Digital Factories
Smart factories are equipped with machines that can sense their manufacturing environments, interact with each other, and control production processes. Smooth operation of such factories requires that the machines and en…
Knowledge Graph CompletionKnowledge GraphsA Temporal Knowledge Graph Completion Method Based on Balanced Timestamp Distribution
Completion through the embedding representation of the knowledge graph (KGE) has been a research hotspot in recent years. Realistic knowledge graphs are mostly related to time, while most of the existing KGE algorithms i…
Knowledge Graph CompletionKnowledge GraphsTemporal Knowledge Graph CompletionDisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network
Knowledge graph completion (KGC) has become a focus of attention across deep learning community owing to its excellent contribution to numerous downstream tasks. Although recently have witnessed a surge of work on KGC, t…
DisentanglementGraph AttentionGraph EmbeddingKnowledge Graph Completion+2Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion
We present InferWiki, a Knowledge Graph Completion (KGC) dataset that improves upon existing benchmarks in inferential ability, assumptions, and patterns. First, each testing sample is predictable with supportive data in…
Knowledge Graph CompletionMetaP: Meta Pattern Learning for One-Shot Knowledge Graph Completion
Knowledge Graphs (KGs) are widely used in various applications of information retrieval. Despite the large scale of KGs, they are still facing incomplete problems. Conventional approaches on Knowledge Graph Completion (K…
Information RetrievalKnowledge Graph CompletionKnowledge GraphsRelation+1KGRefiner: Knowledge Graph Refinement for Improving Accuracy of Translational Link Prediction Methods
The Link Prediction is the task of predicting missing relations between entities of the knowledge graph. Recent work in link prediction has attempted to provide a model for increasing link prediction accuracy by using mo…
Knowledge Graph CompletionKnowledge Graph EmbeddingKnowledge GraphsLink Prediction+1A Joint Training Framework for Open-World Knowledge Graph Embeddings
Knowledge Graphs(KGs) represent factual information as graphs of entities connected by relations. Knowledge graph embeddings have emerged as a popular approach to encode this information for various downstream tasks like…
Dialogue GenerationEntity EmbeddingsKnowledge Graph CompletionKnowledge Graph Embeddings+4Why a Naive Way to Combine Symbolic and Latent Knowledge Base Completion Works Surprisingly Well
We compare a rule-based approach for knowledge graph completion against current state-of-the-art, which is based on embbedings. Instead of focusing on aggregated metrics, we look at several examples that illustrate essen…
Knowledge Base CompletionKnowledge Graph CompletionNeural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction
Link prediction is a very fundamental task on graphs. Inspired by traditional path-based methods, in this paper we propose a general and flexible representation learning framework based on paths for link prediction. Spec…
Graph Neural NetworkInductive Relation PredictionKnowledge Graph CompletionLink Prediction+1Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network
Knowledge Graph (KG) completion research usually focuses on densely connected benchmark datasets that are not representative of real KGs. We curate two KG datasets that include biomedical and encyclopedic knowledge and u…
Knowledge Graph CompletionRe-Ranking