Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
VN Network: Embedding Newly Emerging Entities with Virtual Neighbors
Embedding entities and relations into continuous vector spaces has attracted a surge of interest in recent years. Most embedding methods assume that all test entities are available during training, which makes it time-co…
Graph Neural NetworkKnowledge Graph CompletionNetwork EmbeddingKnowledge Graph Assisted Automatic Sports News Writing
In this paper, we present a novel method for automatically generating sports news, which employs a unique algorithm that extracts pivotal moments from live text broadcasts and uses them to create an initial draft of the …
Knowledge Graph CompletionEntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completion
Commonsense knowledge graph completion is a new challenge for commonsense knowledge graph construction and application. In contrast to factual knowledge graphs such as Freebase and YAGO, commonsense knowledge graphs (CSK…
graph constructionGraph EmbeddingKnowledge Graph CompletionKnowledge Graph Embedding+2UrbanKGent: A Unified Large Language Model Agent Framework for Urban Knowledge Graph Construction
Urban knowledge graph has recently worked as an emerging building block to distill critical knowledge from multi-sourced urban data for diverse urban application scenarios. Despite its promising benefits, urban knowledge…
graph constructionKnowledge Graph CompletionLanguage ModelingLanguage Modelling+2Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the semantic web community's exploration into multi-modal dimensions unlocking new avenues for innovation. In this survey, we carefull…
ArticlesEntity Alignmentimage-classificationImage Classification+7KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion
Knowledge Graph Completion (KGC) is crucial for addressing knowledge graph incompleteness and supporting downstream applications. Many models have been proposed for KGC. They can be categorized into two main classes: tri…
In-Context LearningKnowledge Graph CompletionKnowledge GraphsLanguage Modeling+2Contextualization Distillation from Large Language Model for Knowledge Graph Completion
While textual information significantly enhances the performance of pre-trained language models (PLMs) in knowledge graph completion (KGC), the static and noisy nature of existing corpora collected from Wikipedia article…
ArticlesKnowledge Graph CompletionLanguage ModelingLanguage Modelling+1Are We Wasting Time? A Fast, Accurate Performance Evaluation Framework for Knowledge Graph Link Predictors
The standard evaluation protocol for measuring the quality of Knowledge Graph Completion methods - the task of inferring new links to be added to a graph - typically involves a step which ranks every entity of a Knowledg…
Knowledge Graph CompletionKnowledge GraphsProgressive Distillation Based on Masked Generation Feature Method for Knowledge Graph Completion
In recent years, knowledge graph completion (KGC) models based on pre-trained language model (PLM) have shown promising results. However, the large number of parameters and high computational cost of PLM models pose chal…
Knowledge Graph CompletionLanguage ModellingTransfer LearningEdge-Enabled Anomaly Detection and Information Completion for Social Network Knowledge Graphs
In the rapidly advancing information era, various human behaviors are being precisely recorded in the form of data, including identity information, criminal records, and communication data. Law enforcement agencies can e…
Anomaly DetectionEdge-computingGraph EmbeddingKnowledge Graph Completion+3Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion
Temporal Knowledge Graph Completion (TKGC) is a complex task involving the prediction of missing event links at future timestamps by leveraging established temporal structural knowledge. This paper aims to provide a comp…
Data AugmentationKnowledge Graph CompletionKnowledge GraphsLink Prediction+2Path-based Explanation for Knowledge Graph Completion
Graph Neural Networks (GNNs) have achieved great success in Knowledge Graph Completion (KGC) by modelling how entities and relations interact in recent years. However, the explanation of the predicted facts has not caugh…
Knowledge Graph CompletionFedMKGC: Privacy-Preserving Federated Multilingual Knowledge Graph Completion
Knowledge graph completion (KGC) aims to predict missing facts in knowledge graphs (KGs), which is crucial as modern KGs remain largely incomplete. While training KGC models on multiple aligned KGs can improve performanc…
Entity AlignmentFederated LearningKnowledge Graph CompletionKnowledge Graphs+4ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completion
Knowledge graphs generally suffer from incompleteness, which can be alleviated by completing the missing information. Deep knowledge convolutional embedding models based on neural networks are currently popular methods f…
Entity EmbeddingsKnowledge Graph CompletionKnowledge GraphsRelationPrompting Disentangled Embeddings for Knowledge Graph Completion with Pre-trained Language Model
Both graph structures and textual information play a critical role in Knowledge Graph Completion (KGC). With the success of Pre-trained Language Models (PLMs) such as BERT, they have been applied for text encoding for KG…
Knowledge Graph CompletionLanguage ModelingLanguage ModellingIncreasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs
Recent work in Natural Language Processing and Computer Vision has been using textual information -- e.g., entity names and descriptions -- available in knowledge graphs to ground neural models to high-quality structured…
Entity LinkingKnowledge Graph CompletionKnowledge GraphsMachine Translation+1Federated Knowledge Graph Completion via Latent Embedding Sharing and Tensor Factorization
Knowledge graphs (KGs), which consist of triples, are inherently incomplete and always require completion procedure to predict missing triples. In real-world scenarios, KGs are distributed across clients, complicating co…
Knowledge Graph CompletionKnowledge GraphsDoes Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion?
Knowledge graphs (KGs) consist of links that describe relationships between entities. Due to the difficulty of manually enumerating all relationships between entities, automatically completing them is essential for KGs. …
Knowledge Graph CompletionKnowledge GraphsLanguage ModelingLanguage Modelling+1DynaSemble: Dynamic Ensembling of Textual and Structure-Based Models for Knowledge Graph Completion
We consider two popular approaches to Knowledge Graph Completion (KGC): textual models that rely on textual entity descriptions, and structure-based models that exploit the connectivity structure of the Knowledge Graph (…
Knowledge Graph CompletionUnifying Structure and Language Semantic for Efficient Contrastive Knowledge Graph Completion with Structured Entity Anchors
The goal of knowledge graph completion (KGC) is to predict missing links in a KG using trained facts that are already known. In recent, pre-trained language model (PLM) based methods that utilize both textual and structu…
Contrastive LearningKnowledge Graph CompletionLanguage ModellingLink Prediction