Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
Graph Stochastic Neural Process for Inductive Few-shot Knowledge Graph Completion
Knowledge graphs (KGs) store enormous facts as relationships between entities. Due to the long-tailed distribution of relations and the incompleteness of KGs, there is growing interest in few-shot knowledge graph complet…
Knowledge Graph CompletionKnowledge GraphsCPa-WAC: Constellation Partitioning-based Scalable Weighted Aggregation Composition for Knowledge Graph Embedding
Scalability and training time are crucial for any graph neural network model processing a knowledge graph (KG). While partitioning knowledge graphs helps reduce the training time, the prediction accuracy reduces signifca…
Graph EmbeddingGraph Neural NetworkKnowledge Graph CompletionKnowledge Graph Embedding+1Finetuning Generative Large Language Models with Discrimination Instructions for Knowledge Graph Completion
Traditional knowledge graph (KG) completion models learn embeddings to predict missing facts. Recent works attempt to complete KGs in a text-generation manner with large language models (LLMs). However, they need to grou…
Knowledge Graph CompletionText GenerationSubgraph-Aware Training of Language Models for Knowledge Graph Completion Using Structure-Aware Contrastive Learning
Fine-tuning pre-trained language models (PLMs) has recently shown a potential to improve knowledge graph completion (KGC). However, most PLM-based methods focus solely on encoding textual information, neglecting the long…
Contrastive LearningInductive BiasKnowledge Graph CompletionKnowledge GraphsContrast then Memorize: Semantic Neighbor Retrieval-Enhanced Inductive Multimodal Knowledge Graph Completion
A large number of studies have emerged for Multimodal Knowledge Graph Completion (MKGC) to predict the missing links in MKGs. However, fewer studies have been proposed to study the inductive MKGC (IMKGC) involving emergi…
Contrastive LearningKnowledge Graph CompletionRetrievalSimple Augmentations of Logical Rules for Neuro-Symbolic Knowledge Graph Completion
High-quality and high-coverage rule sets are imperative to the success of Neuro-Symbolic Knowledge Graph Completion (NS-KGC) models, because they form the basis of all symbolic inferences. Recent literature builds neural…
Knowledge Graph CompletionStart from Zero: Triple Set Prediction for Automatic Knowledge Graph Completion
Knowledge graph (KG) completion aims to find out missing triples in a KG. Some tasks, such as link prediction and instance completion, have been proposed for KG completion. They are triple-level tasks with some elements …
Knowledge Graph CompletionLink PredictionMultilingual Knowledge Graph Completion from Pretrained Language Models with Knowledge Constraints
Multilingual Knowledge Graph Completion (mKGC) aim at solving queries like (h, r, ?) in different languages by reasoning a tail entity t thus improving multilingual knowledge graphs. Previous studies leverage multilingua…
Knowledge Graph CompletionKnowledge GraphsCogMG: Collaborative Augmentation Between Large Language Model and Knowledge Graph
Large language models have become integral to question-answering applications despite their propensity for generating hallucinations and factually inaccurate content. Querying knowledge graphs to reduce hallucinations in…
Knowledge Graph CompletionKnowledge GraphsLanguage ModelingLanguage Modelling+2Towards Better Benchmark Datasets for Inductive Knowledge Graph Completion
Knowledge Graph Completion (KGC) attempts to predict missing facts in a Knowledge Graph (KG). Recently, there's been an increased focus on designing KGC methods that can excel in the {\it inductive setting}, where a port…
Inductive knowledge graph completionKnowledge Graph CompletionQuery-Enhanced Adaptive Semantic Path Reasoning for Inductive Knowledge Graph Completion
Conventional Knowledge graph completion (KGC) methods aim to infer missing information in incomplete Knowledge Graphs (KGs) by leveraging existing information, which struggle to perform effectively in scenarios involving…
Inductive knowledge graph completionKnowledge Graph CompletionKnowledge GraphsFrom Latent to Lucid: Transforming Knowledge Graph Embeddings into Interpretable Structures with KGEPrisma
In this paper, we introduce a post-hoc and local explainable AI method tailored for Knowledge Graph Embedding (KGE) models. These models are essential to Knowledge Graph Completion yet criticized for their opaque, black-…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graph Embeddings+1Logical Reasoning with Relation Network for Inductive Knowledge Graph Completion
Inductive knowledge graph completion (KGC) aims to infer the missing relation for a set of newly-coming entities that never appeared in the training set. Such a setting is more in line with reality, as real-world KGs are…
Inductive knowledge graph completionKnowledge Graph CompletionLogical ReasoningRelation+1Assessing LLMs Suitability for Knowledge Graph Completion
Recent work has shown the capability of Large Language Models (LLMs) to solve tasks related to Knowledge Graphs, such as Knowledge Graph Completion, even in Zero- or Few-Shot paradigms. However, they are known to halluci…
Knowledge Graph CompletionKnowledge GraphsMultiple Heads are Better than One: Mixture of Modality Knowledge Experts for Entity Representation Learning
Learning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning, which can enhance reasoning tasks within the MMKGs, such as MMKG completion (MM…
Knowledge Graph CompletionKnowledge GraphsMulti-modal Knowledge GraphRelation+1Predicting from a Different Perspective: A Re-ranking Model for Inductive Knowledge Graph Completion
Rule-induction models have demonstrated great power in the inductive setting of knowledge graph completion. In this setting, the models are tested on a knowledge graph entirely composed of unseen entities. These models l…
Inductive knowledge graph completionKnowledge Graph CompletionLink PredictionRe-RankingEmpowering Small-Scale Knowledge Graphs: A Strategy of Leveraging General-Purpose Knowledge Graphs for Enriched Embeddings
Knowledge-intensive tasks pose a significant challenge for Machine Learning (ML) techniques. Commonly adopted methods, such as Large Language Models (LLMs), often exhibit limitations when applied to such tasks. Neverthel…
Entity AlignmentKnowledge Graph CompletionKnowledge GraphsRepresentation LearningHarmonizing Human Insights and AI Precision: Hand in Hand for Advancing Knowledge Graph Task
Knowledge graph embedding (KGE) has caught significant interest for its effectiveness in knowledge graph completion (KGC), specifically link prediction (LP), with recent KGE models cracking the LP benchmarks. Despite the…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingLink PredictionMulti-level Shared Knowledge Guided Learning for Knowledge Graph Completion
In the task of Knowledge Graph Completion (KGC), the existing datasets and their inherent subtasks carry a wealth of shared knowledge that can be utilized to enhance the representation of knowledge triplets and overall p…
Knowledge Graph CompletionMulti-Task LearningPredictionRelation Prediction+1Relations Prediction for Knowledge Graph Completion using Large Language Models
Knowledge Graphs have been widely used to represent facts in a structured format. Due to their large scale applications, knowledge graphs suffer from being incomplete. The relation prediction task obtains knowledge graph…
Knowledge Graph CompletionKnowledge GraphsLanguage ModelingLanguage Modelling+4