Papers Knowledge Graph Embedding
“Knowledge Graph Embedding” 태그가 달린 논문 499편 · 필터 해제
Diversified and Adaptive Negative Sampling on Knowledge Graphs
In knowledge graph embedding, aside from positive triplets (ie: facts in the knowledge graph), the negative triplets used for training also have a direct influence on the model performance. In reality, since knowledge gr…
Graph EmbeddingInformativenessKnowledge Graph EmbeddingKnowledge Graphs+1Inference over Unseen Entities, Relations and Literals on Knowledge Graphs
In recent years, knowledge graph embedding models have been successfully applied in the transductive setting to tackle various challenging tasks including link prediction, and query answering. Yet, the transductive setti…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsLink PredictionA Pluggable Common Sense-Enhanced Framework for Knowledge Graph Completion
Knowledge graph completion (KGC) tasks aim to infer missing facts in a knowledge graph (KG) for many knowledge-intensive applications. However, existing embedding-based KGC approaches primarily rely on factual triples, p…
Common Sense ReasoningGraph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graph Embedding by Normalizing Flows
A key to knowledge graph embedding (KGE) is to choose a proper representation space, e.g., point-wise Euclidean space and complex vector space. In this paper, we propose a unified perspective of embedding and introduce u…
Graph EmbeddingKnowledge Graph EmbeddingEmbedding Knowledge Graph in Function Spaces
We introduce a novel embedding method diverging from conventional approaches by operating within function spaces of finite dimension rather than finite vector space, thus departing significantly from standard knowledge g…
Graph EmbeddingKnowledge Graph EmbeddingThe Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models
Knowledge Graph Completion has been increasingly adopted as a useful method for several tasks in biomedical research, like drug repurposing or drug-target identification. To that end, a variety of datasets and Knowledge …
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge GraphsCL4KGE: A Curriculum Learning Method for Knowledge Graph Embedding
Knowledge graph embedding (KGE) constitutes a foundational task, directed towards learning representations for entities and relations within knowledge graphs (KGs), with the objective of crafting representations comprehe…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsConformalized Answer Set Prediction for Knowledge Graph Embedding
Knowledge graph embeddings (KGE) apply machine learning methods on knowledge graphs (KGs) to provide non-classical reasoning capabilities based on similarities and analogies. The learned KG embeddings are typically used …
Conformal PredictionGraph EmbeddingKnowledge Graph EmbeddingKnowledge Graph Embeddings+3Predictive Multiplicity of Knowledge Graph Embeddings in Link Prediction
Knowledge graph embedding (KGE) models are often used to predict missing links for knowledge graphs (KGs). However, multiple KG embeddings can perform almost equally well for link prediction yet give conflicting predicti…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsKnowledge Graphs+2Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion
Temporal knowledge graph completion aims to infer the missing facts in temporal knowledge graphs. Current approaches usually embed factual knowledge into continuous vector space and apply geometric operations to learn po…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graphs+3Low-Dimensional Federated Knowledge Graph Embedding via Knowledge Distillation
Federated Knowledge Graph Embedding (FKGE) aims to facilitate collaborative learning of entity and relation embeddings from distributed Knowledge Graphs (KGs) across multiple clients, while preserving data privacy. Train…
Graph EmbeddingKnowledge DistillationKnowledge Graph EmbeddingKnowledge GraphsCPa-WAC: Constellation Partitioning-based Scalable Weighted Aggregation Composition for Knowledge Graph Embedding
Scalability and training time are crucial for any graph neural network model processing a knowledge graph (KG). While partitioning knowledge graphs helps reduce the training time, the prediction accuracy reduces signifca…
Graph EmbeddingGraph Neural NetworkKnowledge Graph CompletionKnowledge Graph Embedding+1On The Expressive Power of Knowledge Graph Embedding Methods
Knowledge Graph Embedding (KGE) is a popular approach, which aims to represent entities and relations of a knowledge graph in latent spaces. Their representations are known as embeddings. To measure the plausibility of t…
Graph EmbeddingKnowledge Graph EmbeddingResearch on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learning
In clinical treatment, identifying potential adverse reactions of drugs can help assist doctors in making medication decisions. In response to the problems in previous studies that features are high-dimensional and spars…
Graph EmbeddingKnowledge Graph EmbeddingPredictionPerformance Evaluation of Knowledge Graph Embedding Approaches under Non-adversarial Attacks
Knowledge Graph Embedding (KGE) transforms a discrete Knowledge Graph (KG) into a continuous vector space facilitating its use in various AI-driven applications like Semantic Search, Question Answering, or Recommenders. …
Graph EmbeddingKnowledge Graph EmbeddingQuestion AnsweringFast and Continual Knowledge Graph Embedding via Incremental LoRA
Continual Knowledge Graph Embedding (CKGE) aims to efficiently learn new knowledge and simultaneously preserve old knowledge. Dominant approaches primarily focus on alleviating catastrophic forgetting of old knowledge bu…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsLink PredictionUnified Interpretation of Smoothing Methods for Negative Sampling Loss Functions in Knowledge Graph Embedding
Knowledge Graphs (KGs) are fundamental resources in knowledge-intensive tasks in NLP. Due to the limitation of manually creating KGs, KG Completion (KGC) has an important role in automatically completing KGs by scoring t…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsTripletCroppable Knowledge Graph Embedding
Knowledge Graph Embedding (KGE) is a common method for Knowledge Graphs (KGs) to serve various artificial intelligence tasks. The suitable dimensions of the embeddings depend on the storage and computing conditions of th…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsLanguage ModellingCommunication-Efficient Federated Knowledge Graph Embedding with Entity-Wise Top-K Sparsification
Federated Knowledge Graphs Embedding learning (FKGE) encounters challenges in communication efficiency stemming from the considerable size of parameters and extensive communication rounds. However, existing FKGE methods …
Entity EmbeddingsGraph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsPersonalized Federated Knowledge Graph Embedding with Client-Wise Relation Graph
Federated Knowledge Graph Embedding (FKGE) has recently garnered considerable interest due to its capacity to extract expressive representations from distributed knowledge graphs, while concurrently safeguarding the priv…
Entity Embeddingsglobal-optimizationGraph EmbeddingKnowledge Graph Embedding+2