Papers Triple Classification
“Triple Classification” 태그가 달린 논문 51편 · 필터 해제
PyKEEN-NSX: A Modular Framework for Static, Dynamic and Schema-Aware Negative Sampling in PyKEEN
Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs (KGs). Embedding models are trained relying on both positive and negative samples o…
Knowledge Graph EmbeddingTriple ClassificationKnowledge GraphsLink PredictionRADAR: Reasoning as Discrimination with Aligned Representations for LLM-based Knowledge Graph Reasoning
Knowledge graph reasoning (KGR) infers missing facts, with recent advances increasingly harnessing the semantic priors and reasoning abilities of Large Language Models (LLMs). However, prevailing generative paradigms are…
Reinforcement LearningTriple ClassificationRelational ReasoningLink PredictionSASA: Semantic-Aware Contrastive Learning Framework with Separated Attention for Triple Classification
Knowledge Graphs~(KGs) often suffer from unreliable knowledge, which restricts their utility. Triple Classification~(TC) aims to determine the validity of triples from KGs. Recently, text-based methods learn entity and r…
Representation LearningBinary ClassificationTriple ClassificationContrastive LearningLLMAtKGE: Large Language Models as Explainable Attackers against Knowledge Graph Embeddings
Adversarial attacks on knowledge graph embeddings (KGE) aim to disrupt the model's ability of link prediction by removing or inserting triples. A recent black-box method has attempted to incorporate textual and structura…
Triple ClassificationLink PredictionImproving Knowledge Graph Embeddings through Contrastive Learning with Negative Statements
Knowledge graphs represent information as structured triples and serve as the backbone for a wide range of applications, including question answering, link prediction, and recommendation systems. A prominent line of rese…
Recommendation SystemsTriple ClassificationContrastive LearningQuestion AnsweringEnhancing PyKEEN with Multiple Negative Sampling Solutions for Knowledge Graph Embedding Models
Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs. Embedding models are trained relying on both positive and negative samples of trip…
Knowledge Graph EmbeddingTriple ClassificationKnowledge GraphsLink PredictionOn Large-scale Evaluation of Embedding Models for Knowledge Graph Completion
Knowledge graph embedding (KGE) models are extensively studied for knowledge graph completion, yet their evaluation remains constrained by unrealistic benchmarks. Standard evaluation metrics rely on the closed-world assu…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingLink Prediction+2Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models
Due to the presence of the natural gap between Knowledge Graph (KG) structures and the natural language, the effective integration of holistic structural information of KGs with Large Language Models (LLMs) has emerged a…
Instruction FollowingKnowledge GraphsLink PredictionTriple ClassificationEfficient 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+3Structural damage detection via hierarchical damage information with volumetric assessment
Structural health monitoring (SHM) is essential for ensuring the safety and longevity of infrastructure, but complex image environments, noisy labels, and reliance on manual damage assessments often hinder its effectiven…
Structural Health MonitoringTriple ClassificationProgressive Knowledge Graph Completion
Knowledge Graph Completion (KGC) has emerged as a promising solution to address the issue of incompleteness within Knowledge Graphs (KGs). Traditional KGC research primarily centers on triple classification and link pred…
Knowledge Graph CompletionKnowledge GraphsLink PredictionTriple ClassificationEmbedding Ontologies via Incorporating Extensional and Intensional Knowledge
Ontologies contain rich knowledge within domain, which can be divided into two categories, namely extensional knowledge and intensional knowledge. Extensional knowledge provides information about the concrete instances t…
Language ModelingLanguage ModellingLink PredictionOntology Embedding+1A Study on Knowledge Graph Embeddings and Graph Neural Networks for Web Of Things
Graph data structures are widely used to store relational information between several entities. With data being generated worldwide on a large scale, we see a significant growth in the generation of knowledge graphs. Thi…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsKnowledge Graphs+3Leveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge Graphs
Most knowledge graph completion (KGC) methods learn latent representations of entities and relations of a given graph by mapping them into a vector space. Although the majority of these methods focus on static knowledge …
Knowledge Graph CompletionKnowledge GraphsLink PredictionRepresentation Learning+3Exploring Large Language Models for Knowledge Graph Completion
Knowledge graphs play a vital role in numerous artificial intelligence tasks, yet they frequently face the issue of incompleteness. In this study, we explore utilizing Large Language Models (LLM) for knowledge graph comp…
Knowledge Graph CompletionKnowledge GraphsRelationRelation Prediction+1Iteratively Learning Representations for Unseen Entities with Inter-Rule Correlations
Recent work on knowledge graph completion (KGC) focused on learning embeddings of entities and relations in knowledge graphs. These embedding methods require that all test entities are observed at training time, resultin…
Knowledge Graph CompletionKnowledge GraphsLink PredictionTriple ClassificationStructure Pretraining and Prompt Tuning for Knowledge Graph Transfer
Knowledge graphs (KG) are essential background knowledge providers in many tasks. When designing models for KG-related tasks, one of the key tasks is to devise the Knowledge Representation and Fusion (KRF) module that le…
image-classificationImage ClassificationKnowledge GraphsQuestion Answering+2Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms
Empirical data plays an important role in evolutionary computation research. To make better use of the available data, ontologies have been proposed in the literature to organize their storage in a structured way. Howeve…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsTriple ClassificationKnowledge Graph Refinement based on Triplet BERT-Networks
Knowledge graph embedding techniques are widely used for knowledge graph refinement tasks such as graph completion and triple classification. These techniques aim at embedding the entities and relations of a Knowledge Gr…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsRelation+3Repurposing Knowledge Graph Embeddings for Triple Representation via Weak Supervision
The majority of knowledge graph embedding techniques treat entities and predicates as separate embedding matrices, using aggregation functions to build a representation of the input triple. However, these aggregations ar…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsKnowledge Graphs+1