Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
Bridge: A Unified Framework to Knowledge Graph Completion via Language Models and Knowledge Representation
Knowledge graph completion (KGC) is a task of inferring missing triples based on existing Knowledge Graphs (KGs). Both structural and semantic information are vital for successful KGC. However, existing methods only use …
Knowledge Graph CompletionKnowledge GraphsRepresentation LearningFully Hyperbolic Rotation for Knowledge Graph Embedding
Hyperbolic rotation is commonly used to effectively model knowledge graphs and their inherent hierarchies. However, existing hyperbolic rotation models rely on logarithmic and exponential mappings for feature transformat…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge GraphsPageRank Bandits for Link Prediction
Link prediction is a critical problem in graph learning with broad applications such as recommender systems and knowledge graph completion. Numerous research efforts have been directed at solving this problem, including …
Decision MakingGraph LearningKnowledge Graph CompletionLink Prediction+4ATAP: Automatic Template-Augmented Commonsense Knowledge Graph Completion via Pre-Trained Language Models
The mission of commonsense knowledge graph completion (CKGC) is to infer missing facts from known commonsense knowledge. CKGC methods can be roughly divided into two categories: triple-based methods and text-based method…
Knowledge Graph CompletionContext-aware Inductive Knowledge Graph Completion with Latent Type Constraints and Subgraph Reasoning
Inductive knowledge graph completion (KGC) aims to predict missing triples with unseen entities. Recent works focus on modeling reasoning paths between the head and tail entity as direct supporting evidence. However, the…
Inductive knowledge graph completionKnowledge Graph CompletionChartKG: A Knowledge-Graph-Based Representation for Chart Images
Chart images, such as bar charts, pie charts, and line charts, are explosively produced due to the wide usage of data visualizations. Accordingly, knowledge mining from chart images is becoming increasingly important, wh…
Chart Question AnsweringKnowledge Graph CompletionObject RecognitionOptical Character Recognition+2A Pluggable Common Sense-Enhanced Framework for Knowledge Graph Completion
Knowledge graph completion (KGC) tasks aim to infer missing facts in a knowledge graph (KG) for many knowledge-intensive applications. However, existing embedding-based KGC approaches primarily rely on factual triples, p…
Common Sense ReasoningGraph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingReplacing Paths with Connection-Biased Attention for Knowledge Graph Completion
Knowledge graph (KG) completion aims to identify additional facts that can be inferred from the existing facts in the KG. Recent developments in this field have explored this task in the inductive setting, where at test …
Hyperparameter OptimizationKnowledge Graph CompletionLink PredictionRelation PredictionComplex Logical Query Answering by Calibrating Knowledge Graph Completion Models
Complex logical query answering (CLQA) is a challenging task that involves finding answer entities for complex logical queries over incomplete knowledge graphs (KGs). Previous research has explored the use of pre-trained…
Knowledge Graph CompletionKnowledge GraphsMUSE: Integrating Multi-Knowledge for Knowledge Graph Completion
Knowledge Graph Completion (KGC) aims to predict the missing [relation] part of (head entity)--[relation]->(tail entity) triplet. Most existing KGC methods focus on single features (e.g., relation types) or sub-graph agg…
Knowledge Graph CompletionRelationRelation PredictionRepresentation Learning+1Cyber Knowledge Completion Using Large Language Models
The integration of the Internet of Things (IoT) into Cyber-Physical Systems (CPSs) has expanded their cyber-attack surface, introducing new and sophisticated threats with potential to exploit emerging vulnerabilities. As…
Binary ClassificationKnowledge Graph CompletionRAGRetrieval-augmented GenerationDSparsE: Dynamic Sparse Embedding for Knowledge Graph Completion
Addressing the incompleteness problem in knowledge graph remains a significant challenge. Current knowledge graph completion methods have their limitations. For example, ComDensE is prone to overfitting and suffers from …
DecoderKnowledge Graph CompletionRelationHyperCausalLP: Causal Link Prediction using Hyper-Relational Knowledge Graph
Causal networks are often incomplete with missing causal links. This is due to various issues, such as missing observation data. Recent approaches to the issue of incomplete causal networks have used knowledge graph link…
Knowledge Graph CompletionLink PredictionPredictionThe Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models
Knowledge Graph Completion has been increasingly adopted as a useful method for several tasks in biomedical research, like drug repurposing or drug-target identification. To that end, a variety of datasets and Knowledge …
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge GraphsLarge Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features
Relation classification (RC) plays a pivotal role in both natural language understanding and knowledge graph completion. It is generally formulated as a task to recognize the relationship between two entities of interest…
Feature EngineeringFew-Shot LearningFew-Shot Relation ClassificationKnowledge Graph Completion+5Learning Granularity Representation for Temporal Knowledge Graph Completion
Temporal Knowledge Graphs (TKGs) incorporate temporal information to reflect the dynamic structural knowledge and evolutionary patterns of real-world facts. Nevertheless, TKGs are still limited in downstream applications…
Knowledge Graph CompletionKnowledge GraphsLink PredictionRepresentation Learning+1GS-KGC: A Generative Subgraph-based Framework for Knowledge Graph Completion with Large Language Models
Knowledge graph completion (KGC) focuses on identifying missing triples in a knowledge graph (KG) , which is crucial for many downstream applications. Given the rapid development of large language models (LLMs), some LLM…
Knowledge Graph CompletionKnowledge GraphsLink PredictionPrompt Engineering+1Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion
Temporal knowledge graph completion aims to infer the missing facts in temporal knowledge graphs. Current approaches usually embed factual knowledge into continuous vector space and apply geometric operations to learn po…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graphs+3Bridging LLMs and KGs without Fine-Tuning: Intermediate Probing Meets Subgraph-Aware Entity Descriptions
Traditional knowledge graph completion (KGC) methods rely solely on structural information, struggling with the inherent sparsity of knowledge graphs (KGs). Large Language Models (LLMs) learn extensive knowledge from lar…
GPUKnowledge Graph CompletionKnowledge GraphsMUSE: Multi-Knowledge Passing on the Edges, Boosting Knowledge Graph Completion
Knowledge Graph Completion (KGC) aims to predict the missing information in the (head entity)-[relation]-(tail entity) triplet. Deep Neural Networks have achieved significant progress in the relation prediction task. How…
Knowledge Graph CompletionRelationRelation PredictionRepresentation Learning+1