Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
Temporal Knowledge Graph Completion using a Linear Temporal Regularizer and Multivector Embeddings
Representation learning approaches for knowledge graphs have been mostly designed for static data. However, many knowledge graphs involve evolving data, e.g., the fact (The President of the United States is Barack Obama)…
Knowledge Graph CompletionKnowledge GraphsLink PredictionRepresentation Learning+2Integrating Lexical Information into Entity Neighbourhood Representations for Relation Prediction
Relation prediction informed from a combination of text corpora and curated knowledge bases, combining knowledge graph completion with relation extraction, is a relatively little studied task. A system that can perform t…
Knowledge Graph CompletionRelationRelation ExtractionRelation PredictionPath-based knowledge reasoning with textual semantic information for medical knowledge graph completion
Background Knowledge graphs (KGs), especially medical knowledge graphs, are often significantly incomplete, so it necessitating a demand for medical knowledge graph completion (MedKGC). MedKGC can find new facts based on…
Knowledge Graph CompletionKnowledge GraphsQuatDE: Dynamic Quaternion Embedding for Knowledge Graph Completion
Knowledge graph embedding has been an active research topic for knowledge base completion (KGC), with progressive improvement from the initial TransE, TransH, RotatE et al to the current state-of-the-art QuatE. However, …
Graph EmbeddingKnowledge Base CompletionKnowledge Graph CompletionKnowledge Graph Embedding+3CAFE: Knowledge graph completion using neighborhood-aware features
Knowledge Graphs (KGs) currently contain a vast amount of structured information in the form of entities and relations. Because KGs are often constructed automatically by means of information extraction processes, they m…
Knowledge Graph CompletionKnowledge GraphsIs Knowledge Embedding Fully Exploited in Language Understanding? An Empirical Study
The recent development of knowledge embedding (KE) enables machines to represent knowledge graphs (KGs) with low-dimensional embeddings, which facilitates utilizing KGs for various downstream natural language understandi…
Knowledge Graph CompletionKnowledge GraphsNatural Language UnderstandingAn Adversarial Transfer Network for Knowledge Representation Learning
Knowledge representation learning has received a lot of attention in the past few years. The success of existing methods heavily relies on the quality of knowledge graphs. The entities with few triplets tend to be learne…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graphs+1Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion
Aiming at expanding few-shot relations' coverage in knowledge graphs (KGs), few-shot knowledge graph completion (FKGC) has recently gained more research interests. Some existing models employ a few-shot relation's multi-…
Few-Shot LearningKnowledge Graph CompletionKnowledge GraphsMeta-Learning+1Mixed-Curvature Multi-Relational Graph Neural Network for Knowledge Graph Completion
Knowledge graphs (KGs) have gradually become valuable assets for many AI applications. In a KG, a node denotes an entity, and an edge (or link) denotes a relationship between the entities represented by the nodes. Knowle…
Graph Neural NetworkKnowledge Graph CompletionKnowledge Graph EmbeddingsKnowledge GraphsMultilingual Knowledge Graph Completion with Joint Relation and Entity Alignment
Knowledge Graph Completion (KGC) predicts missing facts in an incomplete Knowledge Graph. Almost all of existing KGC research is applicable to only one KG at a time, and in one language only. However, different language …
Entity AlignmentKnowledge Graph CompletionRelationTIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion
Reasoning in a temporal knowledge graph (TKG) is a critical task for information retrieval and semantic search. It is particularly challenging when the TKG is updated frequently. The model has to adapt to changes in the …
Decision MakingInformation RetrievalKnowledge Graph CompletionRepresentation Learning+2Membership Inference Attacks on Knowledge Graphs
Membership inference attacks (MIAs) infer whether a specific data record is used for target model training. MIAs have provoked many discussions in the information security community since they give rise to severe data pr…
Graph EmbeddingInference AttackKnowledge Graph CompletionKnowledge Graph Embedding+4Improving Hyper-Relational Knowledge Graph Completion
Different from traditional knowledge graphs (KGs) where facts are represented as entity-relation-entity triplets, hyper-relational KGs (HKGs) allow triplets to be associated with additional relation-entity pairs (a.k.a q…
Computational EfficiencyGraph Neural NetworkKnowledge Graph CompletionKnowledge Graphs+2Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction
We introduce a novel embedding model, named NoGE, which aims to integrate co-occurrence among entities and relations into graph neural networks to improve knowledge graph completion (i.e., link prediction). Given a knowl…
Knowledge Graph CompletionLink PredictionNePTuNe: Neural Powered Tucker Network for Knowledge Graph Completion
Knowledge graphs link entities through relations to provide a structured representation of real world facts. However, they are often incomplete, because they are based on only a small fraction of all plausible facts. The…
Knowledge Graph CompletionKnowledge GraphsLink PredictionPredictionMultiple Run Ensemble Learning with Low-Dimensional Knowledge Graph Embeddings
Among the top approaches of recent years, link prediction using knowledge graph embedding (KGE) models has gained significant attention for knowledge graph completion. Various embedding models have been proposed so far, …
Ensemble LearningGraph EmbeddingKnowledge Graph CompletionKnowledge Graph Embedding+2Text-guided Legal Knowledge Graph Reasoning
Recent years have witnessed the prosperity of legal artificial intelligence with the development of technologies. In this paper, we propose a novel legal application of legal provision prediction (LPP), which aims to pre…
Knowledge Graph CompletionSwitch Spaces: Learning Product Spaces with Sparse Gating
Learning embedding spaces of suitable geometry is critical for representation learning. In order for learned representations to be effective and efficient, it is ideal that the geometric inductive bias aligns well with t…
Inductive BiasKnowledge Graph CompletionRepresentation LearningRepresenting Hierarchical Structure by Using Cone Embedding
Graph embedding is becoming an important method with applications in various areas, including social networks and knowledge graph completion. In particular, Poincar\'e embedding has been proposed to capture the hierarchi…
Graph EmbeddingKnowledge Graph CompletionOntoZSL: Ontology-enhanced Zero-shot Learning
Zero-shot Learning (ZSL), which aims to predict for those classes that have never appeared in the training data, has arisen hot research interests. The key of implementing ZSL is to leverage the prior knowledge of classe…
image-classificationImage ClassificationKnowledge Graph CompletionWord Embeddings+1