Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
Beyond Prefixes: Graph-as-Memory Cross-Attention for Knowledge Graph Completion with Large Language Models
Fusing Knowledge Graphs with Large Language Models (LLMs) is crucial for knowledge-intensive tasks like knowledge graph completion. Existing LLM-based approaches typically inject graph information via prefix concatenatio…
Knowledge Graph CompletionKnowledge GraphsReaLM: Residual Quantization Bridging Knowledge Graph Embeddings and Large Language Models
Large Language Models (LLMs) have recently emerged as a powerful paradigm for Knowledge Graph Completion (KGC), offering strong reasoning and generalization capabilities beyond traditional embedding-based approaches. How…
Knowledge Graph CompletionMultilingual Knowledge Graph Completion via Efficient Multilingual Knowledge Sharing
Large language models (LLMs) based Multilingual Knowledge Graph Completion (MKGC) aim to predict missing facts by leveraging LLMs' multilingual understanding capabilities, improving the completeness of multilingual knowl…
Knowledge Graph CompletionKnowledge GraphsRareAgent: Self-Evolving Reasoning for Drug Repurposing in Rare Diseases
Computational drug repurposing for rare diseases is especially challenging when no prior associations exist between drugs and target diseases. Therefore, knowledge graph completion and message-passing GNNs have little re…
Knowledge Graph CompletionCDTP: A Large-Scale Chinese Data-Text Pair Dataset for Comprehensive Evaluation of Chinese LLMs
Large Language Models (LLMs) have achieved remarkable success across a wide range of natural language processing tasks. However, Chinese LLMs face unique challenges, primarily due to the dominance of unstructured free te…
Knowledge Graph CompletionQuestion AnsweringText GenerationCAT: Curvature-Adaptive Transformers for Geometry-Aware Learning
Transformers achieve strong performance across diverse domains but implicitly assume Euclidean geometry in their attention mechanisms, limiting their effectiveness on data with non-Euclidean structure. While recent exten…
Knowledge Graph CompletionRelational ReasoningSLogic: Subgraph-Informed Logical Rule Learning for Knowledge Graph Completion
Logical rule-based methods offer an interpretable approach to knowledge graph completion (KGC) by capturing compositional relationships in the form of human-readable inference rules. While existing logical rule-based met…
Knowledge Graph CompletionCollaboration of Fusion and Independence: Hypercomplex-driven Robust Multi-Modal Knowledge Graph Completion
Multi-modal knowledge graph completion (MMKGC) aims to discover missing facts in multi-modal knowledge graphs (MMKGs) by leveraging both structural relationships and diverse modality information of entities. Existing MMK…
Knowledge Graph CompletionComputational EfficiencyKnowledge GraphsDeepGraphLog for Layered Neurosymbolic AI
Neurosymbolic AI (NeSy) aims to integrate the statistical strengths of neural networks with the interpretability and structure of symbolic reasoning. However, current NeSy frameworks like DeepProbLog enforce a fixed flow…
Knowledge Graph CompletionSLiNT: Structure-aware Language Model with Injection and Contrastive Training for Knowledge Graph Completion
Link prediction in knowledge graphs requires integrating structural information and semantic context to infer missing entities. While large language models offer strong generative reasoning capabilities, their limited ex…
Knowledge Graph CompletionRepresentation LearningContrastive LearningKnowledge GraphsEnhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning
Knowledge graph completion (KGC) aims to infer new knowledge and make predictions from knowledge graphs. Recently, large language models (LLMs) have exhibited remarkable reasoning capabilities. LLM-enhanced KGC methods p…
Knowledge Graph CompletionContrastive LearningKnowledge GraphsLink PredictionKG-EDAS: A Meta-Metric Framework for Evaluating Knowledge Graph Completion Models
Knowledge Graphs (KGs) enable applications in various domains such as semantic search, recommendation systems, and natural language processing. KGs are often incomplete, missing entities and relations, an issue addressed…
Knowledge Graph CompletionRecommendation SystemsKnowledge GraphsKnowledge Graph Completion for Action Prediction on Situational Graphs -- A Case Study on Household Tasks
Knowledge Graphs are used for various purposes, including business applications, biomedical analyses, or digital twins in industry 4.0. In this paper, we investigate knowledge graphs describing household actions, which a…
Knowledge Graph CompletionKnowledge GraphsLink PredictionProbabilistic Circuits for Knowledge Graph Completion with Reduced Rule Sets
Rule-based methods for knowledge graph completion provide explainable results but often require a significantly large number of rules to achieve competitive performance. This can hinder explainability due to overwhelming…
Knowledge Graph CompletionGLIDR: Graph-Like Inductive Logic Programming with Differentiable Reasoning
Differentiable inductive logic programming (ILP) techniques have proven effective at finding approximate rule-based solutions to link prediction and node classification problems on knowledge graphs; however, the common a…
Inductive logic programmingKnowledge Graph CompletionNode ClassificationKnowledge GraphsAgREE: Agentic Reasoning for Knowledge Graph Completion on Emerging Entities
Open-domain Knowledge Graph Completion (KGC) faces significant challenges in an ever-changing world, especially when considering the continual emergence of new entities in daily news. Existing approaches for KGC mainly r…
Knowledge Graph CompletionInformation RetrievalKnowledge GraphsUnifying Post-hoc Explanations of Knowledge Graph Completions
Knowledge Graphs organize information as entity-relation-entity triples, enabling machine learning models to predict plausible missing triples in a task known as Knowledge Graph Completion (KGC). Post-hoc explainability …
Knowledge Graph CompletionKnowledge GraphsComplementarity-driven Representation Learning for Multi-modal Knowledge Graph Completion
Multi-modal Knowledge Graph Completion (MMKGC) aims to uncover hidden world knowledge in multimodal knowledge graphs by leveraging both multimodal and structural entity information. However, the inherent imbalance in mul…
Knowledge Graph CompletionRepresentation LearningKnowledge GraphsOntology-Enhanced Knowledge Graph Completion using Large Language Models
Large Language Models (LLMs) have been extensively adopted in Knowledge Graph Completion (KGC), showcasing significant research advancements. However, as black-box models driven by deep neural architectures, current LLM-…
Knowledge Graph CompletionKnowledge GraphsGraph Collaborative Attention Network for Link Prediction in Knowledge Graphs
Knowledge graphs offer a structured representation of real-world entities and their relationships, enabling a wide range of applications from information retrieval to automated reasoning. In this paper, we conduct a syst…
Graph Neural NetworkInformation RetrievalKnowledge Base CompletionKnowledge Graph Completion+2