Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
Transformer-Based Multimodal Knowledge Graph Completion with Link-Aware Contexts
Multimodal knowledge graph completion (MMKGC) aims to predict missing links in multimodal knowledge graphs (MMKGs) by leveraging information from various modalities alongside structural data. Existing MMKGC approaches pr…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge GraphsDiffusion-based Hierarchical Negative Sampling for Multimodal Knowledge Graph Completion
Multimodal Knowledge Graph Completion (MMKGC) aims to address the critical issue of missing knowledge in multimodal knowledge graphs (MMKGs) for their better applications. However, both the previous MMGKC and negative sa…
Knowledge Graph CompletionKnowledge GraphsLLM-Assisted Knowledge Graph Completion for Curriculum and Domain Modelling in Personalized Higher Education Recommendations
While learning personalization offers great potential for learners, modern practices in higher education require a deeper consideration of domain models and learning contexts, to develop effective personalization algorit…
Knowledge Graph CompletionHalal or Not: Knowledge Graph Completion for Predicting Cultural Appropriateness of Daily Products
The growing demand for halal cosmetic products has exposed significant challenges, especially in Muslim-majority countries. Recently, various machine learning-based strategies, e.g., image-based methods, have shown remar…
Graph AttentionKnowledge Graph CompletionKG-TRICK: Unifying Textual and Relational Information Completion of Knowledge for Multilingual Knowledge Graphs
Multilingual knowledge graphs (KGs) provide high-quality relational and textual information for various NLP applications, but they are often incomplete, especially in non-English languages. Previous research has shown th…
Knowledge Graph CompletionKnowledge GraphsKG-CF: Knowledge Graph Completion with Context Filtering under the Guidance of Large Language Models
Large Language Models (LLMs) have shown impressive performance in various tasks, including knowledge graph completion (KGC). However, current studies mostly apply LLMs to classification tasks, like identifying missing tr…
Knowledge Graph CompletionTowards Pattern-aware Data Augmentation for Temporal Knowledge Graph Completion
Predicting missing facts for temporal knowledge graphs (TKGs) is a fundamental task, called temporal knowledge graph completion (TKGC). One key challenge in this task is the imbalance in data distribution, where facts ar…
Data AugmentationKnowledge Graph CompletionKnowledge GraphsTemporal Knowledge Graph CompletionEfficient Relational Context Perception for Knowledge Graph Completion
Knowledge Graphs (KGs) provide a structured representation of knowledge but often suffer from challenges of incompleteness. To address this, link prediction or knowledge graph completion (KGC) aims to infer missing new f…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graphs+3Is Large Language Model Good at Triple Set Prediction? An Empirical Study
The core of the Knowledge Graph Completion (KGC) task is to predict and complete the missing relations or nodes in a KG. Common KGC tasks are mostly about inferring unknown elements with one or two elements being known i…
Knowledge Graph CompletionLanguage ModelingLanguage ModellingLarge Language ModelS$^2$DN: Learning to Denoise Unconvincing Knowledge for Inductive Knowledge Graph Completion
Inductive Knowledge Graph Completion (KGC) aims to infer missing facts between newly emerged entities within knowledge graphs (KGs), posing a significant challenge. While recent studies have shown promising results in in…
DenoisingInductive knowledge graph completionKnowledge Graph CompletionKnowledge GraphsA Contextualized BERT model for Knowledge Graph Completion
Knowledge graphs (KGs) are valuable for representing structured, interconnected information across domains, enabling tasks like semantic search, recommendation systems and inference. A pertinent challenge with KGs, howev…
Knowledge Graph CompletionKnowledge GraphsmodelRecommendation Systems+1Multi-Class and Multi-Task Strategies for Neural Directed Link Prediction
Link Prediction is a foundational task in Graph Representation Learning, supporting applications like link recommendation, knowledge graph completion and graph generation. Graph Neural Networks have shown the most promis…
Graph GenerationGraph Representation LearningKnowledge Graph CompletionLink Prediction+2Filter-then-Generate: Large Language Models with Structure-Text Adapter for Knowledge Graph Completion
Large Language Models (LLMs) present massive inherent knowledge and superior semantic comprehension capability, which have revolutionized various tasks in natural language processing. Despite their success, a critical ga…
HallucinationKnowledge Graph CompletionMultiple-choiceIn-Context Learning with Topological Information for Knowledge Graph Completion
Knowledge graphs (KGs) are crucial for representing and reasoning over structured information, supporting a wide range of applications such as information retrieval, question answering, and decision-making. However, thei…
In-Context LearningInformation RetrievalKnowledge Graph CompletionKnowledge Graphs+1A*Net and NBFNet Learn Negative Patterns on Knowledge Graphs
In this technical report, we investigate the predictive performance differences of a rule-based approach and the GNN architectures NBFNet and A*Net with respect to knowledge graph completion. For the two most common benc…
Knowledge Graph CompletionKnowledge GraphsKaLM: Knowledge-aligned Autoregressive Language Modeling via Dual-view Knowledge Graph Contrastive Learning
Autoregressive large language models (LLMs) pre-trained by next token prediction are inherently proficient in generative tasks. However, their performance on knowledge-driven tasks such as factual knowledge querying rema…
Contrastive LearningGraph Question AnsweringKnowledge Graph CompletionKnowledge Graphs+3Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation
Quaternion contains one real part and three imaginary parts, which provided a more expressive hypercomplex space for learning knowledge graph. Existing quaternion embedding models measure the plausibility of a triplet ei…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingLink Prediction+2Deep Sparse Latent Feature Models for Knowledge Graph Completion
Recent progress in knowledge graph completion (KGC) has focused on text-based approaches to address the challenges of large-scale knowledge graphs (KGs). Despite their achievements, these methods often overlook the intri…
Knowledge Graph CompletionKnowledge GraphsLink PredictionRetrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion
The Knowledge Graph Completion~(KGC) task aims to infer the missing entity from an incomplete triple. Existing embedding-based methods rely solely on triples in the KG, which is vulnerable to specious relation patterns a…
Knowledge Graph CompletionLanguage ModelingLanguage ModellingLarge Language Model+2MoCoKGC: Momentum Contrast Entity Encoding for Knowledge Graph Completion
In recent years, numerous studies have sought to enhance the capabilities of pretrained language models (PLMs) for Knowledge Graph Completion (KGC) tasks by integrating structural information from knowledge graphs. Howev…
Contrastive LearningKnowledge Graph CompletionKnowledge GraphsLink Prediction+1