Papers Knowledge Graph Embedding
“Knowledge Graph Embedding” 태그가 달린 논문 499편 · 필터 해제
Multi-Scale Diffusion Transformer for Jointly Simulating User Mobility and Mobile Traffic Pattern
User mobility trajectory and mobile traffic data are essential for a wide spectrum of applications including urban planning, network optimization, and emergency management. However, large-scale and fine-grained mobility …
Knowledge Graph EmbeddingOntoAligner Meets Knowledge Graph Embedding Aligners
Ontology Alignment (OA) is essential for enabling semantic interoperability across heterogeneous knowledge systems. While recent advances have focused on large language models (LLMs) for capturing contextual semantics, t…
Knowledge Graph EmbeddingLink PredictionBio-KGvec2go: Serving up-to-date Dynamic Biomedical Knowledge Graph Embeddings
Knowledge graphs and ontologies represent entities and their relationships in a structured way, having gained significance in the development of modern AI applications. Integrating these semantic resources with machine l…
Knowledge Graph EmbeddingKnowledge GraphsReviewGraph: A Knowledge Graph Embedding Based Framework for Review Rating Prediction with Sentiment Features
In the hospitality industry, understanding the factors that drive customer review ratings is critical for improving guest satisfaction and business performance. This work proposes ReviewGraph for Review Rating Prediction…
Knowledge Graph EmbeddingKnowledge GraphsSAGE: Scale-Aware Gradual Evolution for Continual Knowledge Graph Embedding
Traditional knowledge graph (KG) embedding methods aim to represent entities and relations in a low-dimensional space, primarily focusing on static graphs. However, real-world KGs are dynamically evolving with the consta…
Knowledge Graph EmbeddingEnhancing PyKEEN with Multiple Negative Sampling Solutions for Knowledge Graph Embedding Models
Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs. Embedding models are trained relying on both positive and negative samples of trip…
Knowledge Graph EmbeddingTriple ClassificationKnowledge GraphsLink PredictionUnderstanding the Embedding Models on Hyper-relational Knowledge Graph
Recently, Hyper-relational Knowledge Graphs (HKGs) have been proposed as an extension of traditional Knowledge Graphs (KGs) to better represent real-world facts with additional qualifiers. As a result, researchers have a…
Knowledge Graph EmbeddingKnowledge GraphsLearning to Evolve: Bayesian-Guided Continual Knowledge Graph Embedding
As social media and the World Wide Web become hubs for information dissemination, effectively organizing and understanding the vast amounts of dynamically evolving Web content is crucial. Knowledge graphs (KGs) provide a…
Knowledge Graph EmbeddingContinual LearningBayesian InferenceKnowledge GraphsEntity Representation Learning Through Onsite-Offsite Graph for Pinterest Ads
Graph Neural Networks (GNN) have been extensively applied to industry recommendation systems, as seen in models like GraphSage\cite{GraphSage}, TwHIM\cite{TwHIM}, LiGNN\cite{LiGNN} etc. In these works, graphs were constr…
Knowledge Graph EmbeddingRepresentation LearningRecommendation SystemsUnlearning of Knowledge Graph Embedding via Preference Optimization
Existing knowledge graphs (KGs) inevitably contain outdated or erroneous knowledge that needs to be removed from knowledge graph embedding (KGE) models. To address this challenge, knowledge unlearning can be applied to e…
Knowledge Graph EmbeddingKnowledge GraphsJust Ask for Music (JAM): Multimodal and Personalized Natural Language Music Recommendation
Natural language interfaces offer a compelling approach for music recommendation, enabling users to express complex preferences conversationally. While Large Language Models (LLMs) show promise in this direction, their s…
Knowledge Graph EmbeddingSMART: Relation-Aware Learning of Geometric Representations for Knowledge Graphs
Knowledge graph representation learning approaches provide a mapping between symbolic knowledge in the form of triples in a knowledge graph (KG) and their feature vectors. Knowledge graph embedding (KGE) models often rep…
Graph EmbeddingGraph Representation LearningKnowledge Graph EmbeddingKnowledge Graphs+1Aligning Knowledge Graphs and Language Models for Factual Accuracy
Large language models like GPT-4, Gemini, and Claude have transformed natural language processing (NLP) tasks such as question answering, dialogue generation, summarization, and so forth; yet their susceptibility to hall…
Knowledge Graph EmbeddingDialogue GenerationQuestion AnsweringKnowledge GraphsETT-CKGE: Efficient Task-driven Tokens for Continual Knowledge Graph Embedding
Continual Knowledge Graph Embedding (CKGE) seeks to integrate new knowledge while preserving past information. However, existing methods struggle with efficiency and scalability due to two key limitations: (1) suboptimal…
Graph EmbeddingKnowledge Graph EmbeddingTransfer LearningPredicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings
Uncertainty quantification in Knowledge Graph Embedding (KGE) methods is crucial for ensuring the reliability of downstream applications. A recent work applies conformal prediction to KGE methods, providing uncertainty e…
Conformal PredictionGraph EmbeddingKnowledge Graph EmbeddingKnowledge Graph Embeddings+2Robust Knowledge Graph Embedding via Denoising
We focus on obtaining robust knowledge graph embedding under perturbation in the embedding space. To address these challenges, we introduce a novel framework, Robust Knowledge Graph Embedding via Denoising, which enhance…
DenoisingGraph EmbeddingKnowledge Graph EmbeddingInjecting Knowledge Graphs into Large Language Models
Integrating structured knowledge from Knowledge Graphs (KGs) into Large Language Models (LLMs) remains a key challenge for symbolic reasoning. Existing methods mainly rely on prompt engineering or fine-tuning, which lose…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsPrompt EngineeringQuatE-D: A Distance-Based Quaternion Model for Knowledge Graph Embedding
Knowledge graph embedding (KGE) methods aim to represent entities and relations in a continuous space while preserving their structural and semantic properties. Quaternion-based KGEs have demonstrated strong potential in…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingOn Large-scale Evaluation of Embedding Models for Knowledge Graph Completion
Knowledge graph embedding (KGE) models are extensively studied for knowledge graph completion, yet their evaluation remains constrained by unrealistic benchmarks. Standard evaluation metrics rely on the closed-world assu…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingLink Prediction+2Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction
Knowledge graph embedding (KGE) models encode the structural information of knowledge graphs to predicting new links. Effective training of these models requires distinguishing between positive and negative samples with …
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsLink Prediction