Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
One Pass for All: A Discrete Diffusion Model for Knowledge Graph Triple Set Prediction
Knowledge Graphs (KGs) are composed of triples, and the goal of Knowledge Graph Completion (KGC) is to infer the missing factual triples. Traditional KGC tasks predict missing elements in a triple given one or two of its…
Knowledge Graph CompletionGraph GenerationKnowledge GraphsTime is Not a Label: Continuous Phase Rotation for Temporal Knowledge Graphs and Agentic Memory
Structured memory representations such as knowledge graphs are central to autonomous agents and other long-lived systems. However, most existing approaches model time as discrete metadata, either sorting by recency (bury…
Knowledge Graph CompletionKnowledge GraphsAlertStar: Path-Aware Alert Prediction on Hyper-Relational Knowledge Graphs
Cyber-attacks continue to grow in scale and sophistication, yet existing network intrusion detection approaches lack the semantic depth required for path reasoning over attacker-victim interactions. We address this by fi…
Network Intrusion DetectionKnowledge Graph CompletionKnowledge GraphsOMNIA: Closing the Loop by Leveraging LLMs for Knowledge Graph Completion
Knowledge Graphs (KGs) are widely used to represent structured knowledge, yet their automatic construction, especially with Large Language Models (LLMs), often results in incomplete or noisy outputs. Knowledge Graph Comp…
Knowledge Graph CompletionKnowledge GraphsTokenization, Fusion and Decoupling: Bridging the Granularity Mismatch Between Large Language Models and Knowledge Graphs
Leveraging Large Language Models (LLMs) for Knowledge Graph Completion (KGC) is promising but hindered by a fundamental granularity mismatch. LLMs operate on fragmented token sequences, whereas entities are the fundament…
Knowledge Graph CompletionKnowledge GraphsFederated Personal Knowledge Graph Completion with Lightweight Large Language Models for Personalized Recommendations
Personalized recommendation increasingly relies on private user data, motivating approaches that can adapt to individuals without centralizing their information. We present Federated Targeted Recommendations with Evolvin…
Knowledge Graph CompletionFederated LearningKnowledge GraphsNeural-Symbolic Logic Query Answering in Non-Euclidean Space
Answering complex first-order logic (FOL) queries on knowledge graphs is essential for reasoning. Symbolic methods offer interpretability but struggle with incomplete graphs, while neural approaches generalize better but…
Knowledge Graph CompletionKnowledge GraphsSynergyKGC: Reconciling Topological Heterogeneity in Knowledge Graph Completion via Topology-Aware Synergy
Knowledge Graph Completion (KGC) fundamentally hinges on the coherent fusion of pre-trained entity semantics with heterogeneous topological structures to facilitate robust relational reasoning. However, existing paradigm…
Knowledge Graph CompletionRelational ReasoningWhen Does Embedding Magnitude Matter? A Cross-Task Functional-Symmetry Framework
Cosine similarity normalizes both sides; dot product normalizes neither. We propose a 2x2 framework that independently controls query-side and document-side normalization, exposing two intermediate variants (QNorm, DNorm…
Semantic Textual SimilarityKnowledge Graph CompletionFactNet: A Billion-Scale Knowledge Graph for Multilingual Factual Grounding
Large language models hallucinate factual claims and struggle to ground their outputs in retrievable evidence, particularly in non-English languages. Existing resources impose a trade-off: structured knowledge bases lack…
Knowledge Graph CompletionQuestion AnsweringFact CheckingTA-KAND: Two-stage Attention Triple Enhancement and U-KAN based Diffusion For Few-shot Knowledge Graph Completion
Knowledge Graphs have become fundamental infrastructure for applications such as intelligent question answering and recommender systems due to their expressive representation. Nevertheless, real-world knowledge is hetero…
Knowledge Graph CompletionQuestion AnsweringKnowledge GraphsVisKnow: Constructing Visual Knowledge Base for Object Understanding
Understanding objects is fundamental to computer vision. Beyond object recognition that provides only a category label as typical output, in-depth object understanding represents a comprehensive perception of an object c…
Knowledge Graph CompletionQuestion AnsweringObject RecognitionHow Sharp and Bias-Robust is a Model? Dual Evaluation Perspectives on Knowledge Graph Completion
Knowledge graph completion (KGC) aims to predict missing facts from the observed KG. While a number of KGC models have been studied, the evaluation of KGC still remain underexplored. In this paper, we observe that existi…
Knowledge Graph CompletionLLM-Guided Dynamic-UMAP for Personalized Federated Graph Learning
We propose a method that uses large language models to assist graph machine learning under personalization and privacy constraints. The approach combines data augmentation for sparse graphs, prompt and instruction tuning…
Personalized Federated LearningKnowledge Graph CompletionNode ClassificationData AugmentationDANS-KGC: Diffusion Based Adaptive Negative Sampling for Knowledge Graph Completion
Negative sampling (NS) strategies play a crucial role in knowledge graph representation. In order to overcome the limitations of existing negative sampling strategies, such as vulnerability to false negatives, limited ge…
Knowledge Graph CompletionBreaking the Dyadic Barrier: Rethinking Fairness in Link Prediction Beyond Demographic Parity
Link prediction is a fundamental task in graph machine learning with applications, ranging from social recommendation to knowledge graph completion. Fairness in this setting is critical, as biased predictions can exacerb…
Knowledge Graph CompletionLink PredictionSound Logical Explanations for Mean Aggregation Graph Neural Networks
Graph neural networks (GNNs) are frequently used for knowledge graph completion. Their black-box nature has motivated work that uses sound logical rules to explain predictions and characterise their expressivity. However…
Knowledge Graph CompletionConjugate Relation Modeling for Few-Shot Knowledge Graph Completion
Few-shot Knowledge Graph Completion (FKGC) infers missing triples from limited support samples, tackling long-tail distribution challenges. Existing methods, however, struggle to capture complex relational patterns and m…
Knowledge Graph CompletionELMM: Efficient Lightweight Multimodal Large Language Models for Multimodal Knowledge Graph Completion
Multimodal Knowledge Graphs (MKGs) extend traditional knowledge graphs by incorporating visual and textual modalities, enabling richer and more expressive entity representations. However, existing MKGs often suffer from …
Knowledge Graph CompletionKnowledge GraphsUncertain Knowledge Graph Completion via Semi-Supervised Confidence Distribution Learning
Uncertain knowledge graphs (UKGs) associate each triple with a confidence score to provide more precise knowledge representations. Recently, since real-world UKGs suffer from the incompleteness, uncertain knowledge graph…
Knowledge Graph CompletionKnowledge Graphs