Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
Context-Driven Knowledge Graph Completion with Semantic-Aware Relational Message Passing
Semantic context surrounding a triplet $(h, r, t)$ is crucial for Knowledge Graph Completion (KGC), providing vital cues for prediction. However, traditional node-based message passing mechanisms, when applied to knowled…
Knowledge Graph CompletionKnowledge GraphsLink PredictionTripletRethinking Regularization Methods for Knowledge Graph Completion
Knowledge graph completion (KGC) has attracted considerable attention in recent years because it is critical to improving the quality of knowledge graphs. Researchers have continuously explored various models. However, m…
Knowledge Graph CompletionKnowledge GraphsGenIC: An LLM-Based Framework for Instance Completion in Knowledge Graphs
Knowledge graph completion aims to address the gaps of knowledge bases by adding new triples that represent facts. The complexity of this task depends on how many parts of a triple are already known. Instance completion …
Knowledge Graph CompletionKnowledge GraphsLink PredictionMulti-Label Classification+2Is Architectural Complexity Overrated? Competitive and Interpretable Knowledge Graph Completion with RelatE
We revisit the efficacy of simple, real-valued embedding models for knowledge graph completion and introduce RelatE, an interpretable and modular method that efficiently integrates dual representations for entities and r…
GPUKnowledge Graph CompletionEnhancing Knowledge Graph Completion with GNN Distillation and Probabilistic Interaction Modeling
Knowledge graphs (KGs) serve as fundamental structures for organizing interconnected data across diverse domains. However, most KGs remain incomplete, limiting their effectiveness in downstream applications. Knowledge gr…
Knowledge Graph CompletionKnowledge GraphsReCDAP: Relation-Based Conditional Diffusion with Attention Pooling for Few-Shot Knowledge Graph Completion
Knowledge Graphs (KGs), composed of triples in the form of (head, relation, tail) and consisting of entities and relations, play a key role in information retrieval systems such as question answering, entity search, and …
Information RetrievalKnowledge Graph CompletionKnowledge GraphsQuestion Answering+2Prompted Meta-Learning for Few-shot Knowledge Graph Completion
Few-shot knowledge graph completion (KGC) has obtained significant attention due to its practical applications in real-world scenarios, where new knowledge often emerges with limited available data. While most existing m…
Knowledge Graph CompletionMeta-LearningTransfer LearningSoft Reasoning Paths for Knowledge Graph Completion
Reasoning paths are reliable information in knowledge graph completion (KGC) in which algorithms can find strong clues of the actual relation between entities. However, in real-world applications, it is difficult to guar…
Knowledge Graph CompletionRelationScientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions
Large Language Models (LLMs) are transforming scientific hypothesis generation and validation by enabling information synthesis, latent relationship discovery, and reasoning augmentation. This survey provides a structure…
Causal InferenceDomain AdaptationIn-Context LearningKnowledge Graph Completion+5Efficient Learning on Large Graphs using a Densifying Regularity Lemma
Learning on large graphs presents significant challenges, with traditional Message Passing Neural Networks suffering from computational and memory costs scaling linearly with the number of edges. We introduce the Interse…
Graph Neural NetworkKnowledge Graph CompletionLEMMANode ClassificationQuatE-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+2DiffusionCom: Structure-Aware Multimodal Diffusion Model for Multimodal Knowledge Graph Completion
Most current MKGC approaches are predominantly based on discriminative models that maximize conditional likelihood. These approaches struggle to efficiently capture the complex connections in real-world knowledge graphs,…
Graph AttentionKnowledge Graph CompletionKnowledge GraphsRepresentation Learning+1Knowledge Graph Completion with Relation-Aware Anchor Enhancement
Text-based knowledge graph completion methods take advantage of pre-trained language models (PLM) to enhance intrinsic semantic connections of raw triplets with detailed text descriptions. Typical methods in this branch …
Knowledge Graph CompletionLink PredictionRelationKnowledge Graph Completion with Mixed Geometry Tensor Factorization
In this paper, we propose a new geometric approach for knowledge graph completion via low rank tensor approximation. We augment a pretrained and well-established Euclidean model based on a Tucker tensor decomposition wit…
Knowledge Graph CompletionKnowledge GraphsLink PredictionTensor Decomposition+1Enhancing Knowledge Graph Completion with Entity Neighborhood and Relation Context
Knowledge Graph Completion (KGC) aims to infer missing information in Knowledge Graphs (KGs) to address their inherent incompleteness. Traditional structure-based KGC methods, while effective, face significant computatio…
Knowledge Graph CompletionKnowledge GraphsRelationMuCo-KGC: Multi-Context-Aware Knowledge Graph Completion
Knowledge graph completion (KGC) seeks to predict missing entities (e.g., heads or tails) or relationships in knowledge graphs (KGs), which often contain incomplete data. Traditional embedding-based methods, such as Tran…
Knowledge Graph CompletionKnowledge GraphsTripletTRIX: A More Expressive Model for Zero-shot Domain Transfer in Knowledge Graphs
Fully inductive knowledge graph models can be trained on multiple domains and subsequently perform zero-shot knowledge graph completion (KGC) in new unseen domains. This is an important capability towards the goal of hav…
Knowledge Graph CompletionKnowledge GraphsRelationRelation Prediction+1GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion
Knowledge Graph Completion (KGC), which aims to infer missing or incomplete facts, is a crucial task for KGs. However, integrating the vital structural information of KGs into Large Language Models (LLMs) and outputting …
Knowledge Graph CompletionLanguage ModelingLanguage ModellingUnified Graph Networks (UGN): A Deep Neural Framework for Solving Graph Problems
Deep neural networks have enabled researchers to create powerful generalized frameworks, such as transformers, that can be used to solve well-studied problems in various application domains, such as text and image. Howev…
Community DetectionGraph LearningGraph-To-Graph TranslationKnowledge Graph Completion+2