Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
LLM-Based Knowledge Graph Completion Combining Discrete Structural Coding with Similar Entity Information
Knowledge graph completion requires models to use both textual descriptions and relational structure. Existing LLM-based methods either encode KG structure as discrete tokens or refine a restricted set of candidate entit…
Knowledge Graph CompletionViSR-KGC: Visual Subgraph Reasoning with Vision-Language Models for Multimodal Knowledge Graph Completion
Knowledge graph completion (KGC) aims to infer missing entities or relations from incomplete graph structures, and has evolved into multimodal knowledge graph completion (MMKGC), where entities are associated with multip…
Knowledge Graph CompletionRepresentation LearningMultimodal ReasoningKnowledge GraphsDual-Path LLM Reasoning for Multimodal Few-Shot Knowledge Graph Completion
Knowledge graph completion (KGC) aims to infer missing facts in knowledge graphs (KGs), thereby improving their completeness and supporting downstream intelligent applications. However, emerging entities and relations in…
Knowledge Graph CompletionKnowledge GraphsSentence Splitter: Uncovering Latent Factual Structure for Self-Supervised Learning
This paper introduces Sentence Splitter, a self-supervised framework built upon a T5-based encoder--decoder architecture for uncovering the latent factual structure of natural language sentences. The proposed method iden…
Knowledge Graph CompletionSelf-Supervised LearningQuestion AnsweringMGDT: MLLM-Guided Diffusion Transformer with Relation-Adaptive Mixture-of-Experts for Multimodal Knowledge Graph Completion
Multimodal Knowledge Graph Completion (MKGC) requires inferring missing entities from structural, textual, and visual cues. Existing diffusion-based MKGC methods usually denoise directly on raw multimodal features. Such …
Knowledge Graph CompletionConditional Diffusion Guided Knowledge Transfer for Multi-Domain Knowledge Graph Completion
Multi-domain knowledge graph completion (MKGC) aims to improve missing triple prediction in a target KG by transferring knowledge from other support KGs. Existing methods typically enforce consistency constraints on equi…
Knowledge Graph CompletionBeyond Triplet Plausibility: Relation Set Completion in Knowledge Graphs
Knowledge graphs (KGs) organize real-world knowledge as triplets and underpin many downstream applications. Due to their inherent incompleteness, knowledge graph completion (KGC) is widely studied and is typically formul…
Knowledge Graph CompletionKnowledge GraphsLink PredictionFirst-Order Temporal Logic Tensor Networks
Most of the existing neuro-symbolic AI methods focus on the scenario of static knowledge where objects do not change according to a temporal dimension. Temporal neuro-symbolic works are still under explored and are mainl…
Knowledge Graph CompletionRelBall: Relation Ball with Quaternion Rotation for Knowledge Graph Completion
Real-world knowledge graphs are often incomplete, lacking many valid facts. Knowledge Graph Completion (KGC) aims to predict missing links using known triples, thereby enhancing graph coverage. A key challenge is modelin…
Knowledge Graph CompletionKnowledge GraphsLink PredictionModel Graph Inductive Learning for Knowledge Graph Completion
Link prediction in knowledge graphs fundamentally depends on the quality of learned embeddings for entities and relations. However, most existing methods derive these embeddings by aggregating only the local neighborhood…
Knowledge Graph CompletionInductive Link PredictionKnowledge GraphsWhen Metrics Disagree: A Meta-Analysis of Knowledge-Graph-Completion Model Benchmarking
Evaluating Knowledge Graph Completion (KGC) models remains challenging because standard assessment relies on isolated rank-based metrics such as MRR, Hits$@$k, and Mean Rank, which often produce conflicting model orderin…
Knowledge Graph CompletionGeneralized Rank-based Evaluation for Knowledge Graph Completion: Perspectives, Framework, and Analyses
Knowledge graph completion (KGC) aims to predict missing facts from an observed knowledge graph (KG), playing a crucial role in a wide range of real-world applications such as drug discovery, recommender systems, and ret…
Knowledge Graph CompletionDrug DiscoveryPROBE-Web: An Interactive System for Probing Evaluation Landscapes of Knowledge Graph Completion Models
Knowledge graph completion (KGC) models are commonly evaluated using rank-based metrics such as MRR and Hits@K, despite different users often requiring different evaluation perspectives. In this demo, we present PROBE-We…
Knowledge Graph CompletionQ-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion
Knowledge Graph Completion (KGC) aims at predicting missing triplets from incomplete knowledge graphs, which is crucial for downstream applications. Recently, Graph Neural Network (GNN)-based methods have achieved remark…
Knowledge Graph CompletionGraph Neural NetworkKnowledge GraphsLink Prediction or Perdition: the Seeds of Instability in Knowledge Graph Embeddings
Embedding models (KGEMs) constitute the main link prediction approach to complete knowledge graphs. Standard evaluation protocols emphasize rank-based metrics such as MRR or Hits@$K$, but usually overlook the influence o…
Knowledge Graph CompletionKnowledge GraphsLink PredictionBoosting Knowledge Graph Foundation Models via Enhanced Negative Sampling
Knowledge graphs (KGs) have become the core backbone of numerous downstream tasks such as question answering and recommender systems. However, despite all this, KGs are often very incomplete. To perform zero-shot knowled…
Knowledge Graph CompletionQuestion AnsweringKnowledge GraphsMultimodal Cultural Heritage Knowledge Graph Extension with Language and Vision Models
The preservation and interpretation of cultural heritage increasingly rely on digital technologies, among which Knowledge Graphs (KGs) stand out for their ability to structure vast amounts of data. However, the construct…
Knowledge Graph CompletionKnowledge GraphsCORE: Cyclic Orthotope Relation Embedding for Knowledge Graph Completion
Knowledge graph completion (KGC) aims to automatically infer missing facts in multi-relational data by mapping entities and relations into continuous representation spaces. Recent region-based embedding models have shown…
Knowledge Graph CompletionLink PredictionRADD: Retrieval-Augmented Discrete Diffusion for Multi-Modal Knowledge Graph Completion
Most multi-modal knowledge graph completion (MMKGC) models use one embedding scorer to do both retrieval over the full entity set and final decision making. We argue that this coupling is a core bottleneck: global high-r…
Knowledge Graph CompletionDecision MakingGS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion
Large Language Models (LLMs) have shown immense potential in Knowledge Graph Completion (KGC), yet bridging the modality gap between continuous graph embeddings and discrete LLM tokens remains a critical challenge. While…
Knowledge Graph Completion