Papers Knowledge Graph Embeddings
“Knowledge Graph Embeddings” 태그가 달린 논문 269편 · 필터 해제
Universal Preprocessing Operators for Embedding Knowledge Graphs with Literals
Knowledge graph embeddings are dense numerical representations of entities in a knowledge graph (KG). While the majority of approaches concentrate only on relational information, i.e., relations between entities, fewer a…
Knowledge Graph EmbeddingsKnowledge GraphsKnowledge Graph Embeddings for Multi-Lingual Structured Representations of Radiology Reports
The way we analyse clinical texts has undergone major changes over the last years. The introduction of language models such as BERT led to adaptations for the (bio)medical domain like PubMedBERT and ClinicalBERT. These m…
ClassificationGraph Embeddingimage-classificationImage Classification+2Large language models converge toward human-like concept organization
Large language models show human-like performance in knowledge extraction, reasoning and dialogue, but it remains controversial whether this performance is best explained by memorization and pattern matching, or whether …
Knowledge Graph EmbeddingsMemorizationWorld KnowledgeLeveraging Knowledge and Reinforcement Learning for Enhanced Reliability of Language Models
The Natural Language Processing(NLP) community has been using crowd sourcing techniques to create benchmark datasets such as General Language Understanding and Evaluation(GLUE) for training modern Language Models such as…
Knowledge Graph Embeddingsreinforcement-learningDevelopment of a Knowledge Graph Embeddings Model for Pain
Pain is a complex concept that can interconnect with other concepts such as a disorder that might cause pain, a medication that might relieve pain, and so on. To fully understand the context of pain experienced by either…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsKnowledge Graphs+1Biomedical Knowledge Graph Embeddings with Negative Statements
A knowledge graph is a powerful representation of real-world entities and their relations. The vast majority of these relations are defined as positive statements, but the importance of negative statements is increasingl…
Graph EmbeddingGraph Representation LearningKnowledge Graph EmbeddingKnowledge Graph Embeddings+5Simple Rule Injection for ComplEx Embeddings
Recent works in neural knowledge graph inference attempt to combine logic rules with knowledge graph embeddings to benefit from prior knowledge. However, they usually cannot avoid rule grounding, and injecting a diverse …
Knowledge Graph CompletionKnowledge Graph EmbeddingsRethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment
As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to identify identical entities across disparate knowledge graphs (KGs) by exploiting associated visual information. However, exist…
BenchmarkingEntity AlignmentKnowledge Graph EmbeddingsKnowledge Graphs+1Benchmark datasets for biomedical knowledge graphs with negative statements
Knowledge graphs represent facts about real-world entities. Most of these facts are defined as positive statements. The negative statements are scarce but highly relevant under the open-world assumption. Furthermore, the…
Disease PredictionKnowledge Graph EmbeddingsKnowledge GraphsPrediction+1A Personalized Recommender System Based-on Knowledge Graph Embeddings
Knowledge graphs have proven to be effective for modeling entities and their relationships through the use of ontologies. The recent emergence in interest for using knowledge graphs as a form of information modeling has …
Knowledge Graph EmbeddingsKnowledge GraphsRecommendation SystemsConcept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs
Knowledge graph embeddings (KGE) have been extensively studied to embed large-scale relational data for many real-world applications. Existing methods have long ignored the fact many KGs contain two fundamentally differe…
Knowledge Graph EmbeddingsKnowledge GraphsExplainable Representations for Relation Prediction in Knowledge Graphs
Knowledge graphs represent real-world entities and their relations in a semantically-rich structure supported by ontologies. Exploring this data with machine learning methods often relies on knowledge graph embeddings, w…
Knowledge Graph EmbeddingsKnowledge GraphsPredictionRelation+1Leveraging Knowledge Graph Embeddings to Enhance Contextual Representations for Relation Extraction
Relation extraction task is a crucial and challenging aspect of Natural Language Processing. Several methods have surfaced as of late, exhibiting notable performance in addressing the task; however, most of these approac…
Knowledge Graph EmbeddingsKnowledge GraphsRelationRelation Extraction+1Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE
Knowledge graph embedding models (KGEMs) have gained considerable traction in recent years. These models learn a vector representation of knowledge graph entities and relations, a.k.a. knowledge graph embeddings (KGEs). …
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsLink Prediction+2What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings
Joint representation learning over multi-sourced knowledge graphs (KGs) yields transferable and expressive embeddings that improve downstream tasks. Entity alignment (EA) is a critical step in this process. Despite recen…
Entity AlignmentKnowledge Graph EmbeddingsKnowledge GraphsRepresentation LearningKnowledge Graph Embeddings in the Biomedical Domain: Are They Useful? A Look at Link Prediction, Rule Learning, and Downstream Polypharmacy Tasks
Knowledge graphs are powerful tools for representing and organising complex biomedical data. Several knowledge graph embedding algorithms have been proposed to learn from and complete knowledge graphs. However, a recent …
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsKnowledge Graphs+1How to Turn Your Knowledge Graph Embeddings into Generative Models
Some of the most successful knowledge graph embedding (KGE) models for link prediction -- CP, RESCAL, TuckER, ComplEx -- can be interpreted as energy-based models. Under this perspective they are not amenable for exact m…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsLink PredictionHaSa: Hardness and Structure-Aware Contrastive Knowledge Graph Embedding
We consider a contrastive learning approach to knowledge graph embedding (KGE) via InfoNCE. For KGE, efficient learning relies on augmenting the training data with negative triples. However, most KGE works overlook the b…
Contrastive LearningGraph EmbeddingKnowledge Graph CompletionKnowledge Graph Embedding+2Growing and Serving Large Open-domain Knowledge Graphs
Applications of large open-domain knowledge graphs (KGs) to real-world problems pose many unique challenges. In this paper, we present extensions to Saga our platform for continuous construction and serving of knowledge …
Entity LinkingFact VerificationKnowledge Graph EmbeddingsKnowledge GraphsFedHGN: A Federated Framework for Heterogeneous Graph Neural Networks
Heterogeneous graph neural networks (HGNNs) can learn from typed and relational graph data more effectively than conventional GNNs. With larger parameter spaces, HGNNs may require more training data, which is often scarc…
Graph LearningKnowledge Graph Embeddings