Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
Few-Shot Knowledge Graph Completion with Data Fusion and Augmentation
This paper addresses the few-shot knowledge graph completion problem, which aims to infer facts for long-tail distributed relations for completing knowledge graphs. The few-shot knowledge graph completion task confronts …
Few-Shot LearningKnowledge Graph CompletionKnowledge GraphsWorld KnowledgeSequence-to-Sequence Knowledge Graph Completion and Question Answering
Knowledge graph embedding (KGE) models represent each entity and relation of a knowledge graph (KG) with low-dimensional embedding vectors. These methods have recently been applied to KG link prediction and question answ…
DecoderGraph EmbeddingKnowledge Graph CompletionKnowledge Graph Embedding+2On the Use of Entity Embeddings from Pre-Trained Language Models for Knowledge Graph Completion
Recent work has found that entity representations can be extracted from pre-trained language models to develop knowledge graph completion models that are more robust to the naturally occurring sparsity found in knowledge…
Entity EmbeddingsKnowledge Graph CompletionKnowledge GraphsLanguage Modeling+2Mixture-of-Graphs: Zero-shot Relational Learning for Knowledge Graph by Fusing Ontology and Textual Experts
Knowledge Graph Embedding (KGE) have been proposed and succeed utilized to knowledge Graph Completion (KGC). But dominant KGE models often fail in zero-shot relational learning because they cannot learn effective represe…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingRelation+2Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach
In recent years, pre-trained language models (PLMs) have been shown to capture factual knowledge from massive texts, which encourages the proposal of PLM-based knowledge graph completion (KGC) models. However, these mode…
Knowledge Graph CompletionLink PredictionQubitE: Qubit Embedding for Knowledge Graph Completion
Knowledge graph embeddings (KGEs) learn low-dimensional representations of entities and relations to predict missing facts based on existing ones. Quantum-based KGEs utilise variational quantum circuits for link predict…
Complex Query AnsweringKnowledge Graph CompletionKnowledge Graph EmbeddingsKnowledge Graphs+1ReadE: Learning Relation-Dependent Entity Representation for Knowledge Graph Completion
Conventional knowledge graph embedding methods learn semantic representations for entities considering their intrinsic interactions through powerful graph neural networks. However, previous methods represent each node so…
Contrastive LearningGraph EmbeddingGraph Neural NetworkKnowledge Graph Completion+2Time in a Box: Advancing Knowledge Graph Completion with Temporal Scopes
Almost all statements in knowledge bases have a temporal scope during which they are valid. Hence, knowledge base completion (KBC) on temporal knowledge bases (TKB), where each statement \textit{may} be associated with a…
Knowledge Base CompletionKnowledge Graph CompletionLink PredictionTime-interval Prediction+1RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule Mining
Although the state-of-the-art traditional representation learning (TRL) models show competitive performance on knowledge graph completion, there is no parameter sharing between the embeddings of entities, and the connect…
Graph Representation LearningKnowledge Graph CompletionRepresentation LearningTransductive Data Augmentation with Relational Path Rule Mining for Knowledge Graph Embedding
For knowledge graph completion, two major types of prediction models exist: one based on graph embeddings, and the other based on relation path rule induction. They have different advantages and disadvantages. To take ad…
Data AugmentationGraph EmbeddingKnowledge Graph CompletionKnowledge Graph Embedding+1P-INT: A Path-based Interaction Model for Few-shot Knowledge Graph Completion
Few-shot knowledge graph completion is to infer the unknown facts (i.e., query head-tail entity pairs) of a given relation with only a few observed reference entity pairs. Its general process is to first encode the impli…
Knowledge Graph CompletionRelationData Collection vs. Knowledge Graph Completion: What is Needed to Improve Coverage?
This survey/position paper discusses ways to improve coverage of resources such as WordNet. Rapp estimated correlations, rho, between corpus statistics and pyscholinguistic norms. rho improves with quantity (corpus size)…
Knowledge Graph CompletionPositionA Semantic Filter Based on Relations for Knowledge Graph Completion
Knowledge graph embedding, representing entities and relations in the knowledge graphs with high-dimensional vectors, has made significant progress in link prediction. More researchers have explored the representational …
AttributeGraph EmbeddingKnowledge Graph CompletionKnowledge Graph Embedding+3Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework
Various temporal knowledge graph (KG) completion models have been proposed in the recent literature. The models usually contain two parts, a temporal embedding layer and a score function derived from existing static KG m…
AllEntity EmbeddingsGPUKnowledge Graph Completion+1Spatio-Temporal Urban Knowledge Graph Enabled Mobility Prediction
With the rapid development of the mobile communication technology, mobile trajectories of humans are massively collected by Internet service providers (ISPs) and application service providers (ASPs). On the other hand, t…
Knowledge Graph CompletionPredictionParallel Training of Knowledge Graph Embedding Models: A Comparison of Techniques
Knowledge graph embedding (KGE) models represent the entities and relations of a knowledge graph (KG) using dense continuous representations called embeddings. KGE methods have recently gained traction for tasks such as …
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingLink PredictionSMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs
Knowledge graphs (KGs) capture knowledge in the form of head--relation--tail triples and are a crucial component in many AI systems. There are two important reasoning tasks on KGs: (1) single-hop knowledge graph completi…
CPUGPUKnowledge Graph CompletionKnowledge Graphs+1Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones
Hierarchical relations are prevalent and indispensable for organizing human knowledge captured by a knowledge graph (KG). The key property of hierarchical relations is that they induce a partial ordering over the entitie…
Ancestor-descendant predictionKnowledge Graph CompletionLink PredictionRelationVector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices
We propose the use of the vector-valued distance to compute distances and extract geometric information from the manifold of symmetric positive definite matrices (SPD), and develop gyrovector calculus, constructing analo…
Knowledge Graph CompletionQuestion AnsweringA Probabilistic Framework for Knowledge Graph Data Augmentation
We present NNMFAug, a probabilistic framework to perform data augmentation for the task of knowledge graph completion to counter the problem of data scarcity, which can enhance the learning process of neural link predict…
Data AugmentationKnowledge Graph CompletionLink Prediction