Papers Knowledge Graph Embedding
“Knowledge Graph Embedding” 태그가 달린 논문 499편 · 필터 해제
PyKEEN-NSX: A Modular Framework for Static, Dynamic and Schema-Aware Negative Sampling in PyKEEN
Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs (KGs). Embedding models are trained relying on both positive and negative samples o…
Knowledge Graph EmbeddingTriple ClassificationKnowledge GraphsLink PredictionNeural Regression with Embeddings for Numerical Attribute Prediction in Knowledge Graphs
In recent years, transductive knowledge graph embedding models have been applied to tasks such as link prediction and query answering. Although knowledge graphs often contain rich numerical attributes, most embedding mod…
Knowledge Graph EmbeddingKnowledge GraphsLink PredictionMatched Excess-Outranker Regularization for Candidate-Set Interference in Continual Knowledge Graph Embedding
Continual knowledge graph embedding updates entity and relation representations as a graph grows. Existing methods primarily address catastrophic forgetting, but entity admission also changes the candidate universe of ev…
Knowledge Graph EmbeddingTeRoR: Decoupled Temporal Rotation with Relational Circular Region for Temporal Knowledge Graph Embedding
In recent years, with the emergence of Temporal Knowledge Graphs (TKGs), research on learning entity and relation representations in TKGs has attracted increasing attention, giving rise to a large number of TKG embedding…
Knowledge Graph EmbeddingKnowledge GraphsHolographic Memory for Zero-Shot Compositional Reasoning in Knowledge Graphs: A Mechanistic Study of Where and Why It Fails
Knowledge graph embedding (KGE) models predict single-hop links well but have no mechanism for zero-shot compositional queries: multi-hop questions whose relation chains never appeared during training. Holographic Reduce…
Knowledge Graph EmbeddingKnowledge GraphsInferring Sensitive Attributes from Knowledge Graph Embeddings: Attack and Defense Strategies
Knowledge Graphs (KGs) are a powerful representation of linked data, offering flexibility, semantic richness, and support for knowledge enrichment and reasoning. They help data owners organize and exploit heterogeneous d…
Knowledge Graph EmbeddingKnowledge GraphsRelations Are Channels: Knowledge Graph Embedding via Kraus Decompositions
Knowledge graph embedding (KGE) models typically represent each relation as an operator on entity embeddings. In this work, we identify three structural axioms that any principled relation operator must satisfy, linearit…
Knowledge Graph EmbeddingCMKL: Modality-Aware Continual Learning for Evolving Biomedical Knowledge Graphs
Biomedical knowledge graphs are increasingly large, dynamic, and multimodal, driven by rapid advances in biotechnology such as high-throughput sequencing. Machine learning models can infer previously unobserved biomedica…
Knowledge Graph EmbeddingContinual LearningKnowledge GraphsSelf-Aware Vector Embeddings for Retrieval-Augmented Generation: A Neuroscience-Inspired Framework for Temporal, Confidence-Weighted, and Relational Knowledge
Modern retrieval-augmented generation (RAG) systems treat vector embeddings as static, context-free artifacts: an embedding has no notion of when it was created, how trustworthy its source is, or which other embeddings d…
Knowledge Graph EmbeddingRevisiting Catastrophic Forgetting in Continual Knowledge Graph Embedding
Knowledge Graph Embeddings (KGEs) support a wide range of downstream tasks over Knowledge Graphs (KGs). In practice, KGs evolve as new entities and facts are added, motivating Continual Knowledge Graph Embedding (CKGE) m…
Knowledge Graph EmbeddingKnowledge GraphsLearning Chain Of Thoughts Prompts for Predicting Entities, Relations, and even Literals on Knowledge Graphs
Knowledge graph embedding (KGE) models perform well on link prediction but struggle with unseen entities, relations, and especially literals, limiting their use in dynamic, heterogeneous graphs. In contrast, pretrained l…
Knowledge Graph EmbeddingKnowledge GraphsLink PredictionKG-CMI: Knowledge graph enhanced cross-Mamba interaction for medical visual question answering
Medical visual question answering (Med-VQA) is a crucial multimodal task in clinical decision support and telemedicine. Recent methods fail to fully leverage domain-specific medical knowledge, making it difficult to accu…
Knowledge Graph EmbeddingVisual Question AnsweringMulti-Task LearningPharmacology Knowledge Graphs: Do We Need Chemical Structure for Drug Repurposing?
The contributions of model complexity, data volume, and feature modalities to knowledge graph-based drug repurposing remain poorly quantified under rigorous temporal validation. We constructed a pharmacology knowledge gr…
Knowledge Graph EmbeddingGraph Neural NetworkKnowledge GraphsVL-KGE: Vision-Language Models Meet Knowledge Graph Embeddings
Real-world multimodal knowledge graphs (MKGs) are inherently heterogeneous, modeling entities that are associated with diverse modalities. Traditional knowledge graph embedding (KGE) methods excel at learning continuous …
Knowledge Graph EmbeddingKnowledge GraphsLink PredictionImproving Enzyme Prediction with Chemical Reaction Equations by Hypergraph-Enhanced Knowledge Graph Embeddings
Predicting enzyme-substrate interactions has long been a fundamental problem in biochemistry and metabolic engineering. While existing methods could leverage databases of expert-curated enzyme-substrate pairs for models …
Knowledge Graph EmbeddingLocal-Curvature-Aware Knowledge Graph Embedding: An Extended Ricci Flow Approach
Knowledge graph embedding (KGE) relies on the geometry of the embedding space to encode semantic and structural relations. Existing methods place all entities on one homogeneous manifold, Euclidean, spherical, hyperbolic…
Knowledge Graph EmbeddingNode ClassificationLink PredictionSKGE: Spherical Knowledge Graph Embedding with Geometric Regularization
Knowledge graph embedding (KGE) has become a fundamental technique for representation learning on multi-relational data. Many seminal models, such as TransE, operate in an unbounded Euclidean space, which presents inhere…
Knowledge Graph EmbeddingRepresentation LearningParameter Averaging in Link Prediction
Ensemble methods are widely employed to improve generalization in machine learning. This has also prompted the adoption of ensemble learning for the knowledge graph embedding (KGE) models in performing link prediction. T…
Knowledge Graph EmbeddingEnsemble LearningLink PredictionCertainty in Uncertainty: Reasoning over Uncertain Knowledge Graphs with Statistical Guarantees
Uncertain knowledge graph embedding (UnKGE) methods learn vector representations that capture both structural and uncertainty information to predict scores of unseen triples. However, existing methods produce only point …
Knowledge Graph EmbeddingKnowledge GraphsFrom Knowledge to Treatment: Large Language Model Assisted Biomedical Concept Representation for Drug Repurposing
Drug repurposing plays a critical role in accelerating treatment discovery, especially for complex and rare diseases. Biomedical knowledge graphs (KGs), which encode rich clinical associations, have been widely adopted t…
Knowledge Graph EmbeddingKnowledge Graphs