Papers Knowledge Graph Embeddings
“Knowledge Graph Embeddings” 태그가 달린 논문 269편 · 필터 해제
Real-World Deployment of a Lane Change Prediction Architecture Based on Knowledge Graph Embeddings and Bayesian Inference
Research on lane change prediction has gained a lot of momentum in the last couple of years. However, most research is confined to simulation or results obtained from datasets, leaving a gap between algorithmic advances …
Bayesian InferenceKnowledge Graph EmbeddingsPredictionPredicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings
Uncertainty quantification in Knowledge Graph Embedding (KGE) methods is crucial for ensuring the reliability of downstream applications. A recent work applies conformal prediction to KGE methods, providing uncertainty e…
Conformal PredictionGraph EmbeddingKnowledge Graph EmbeddingKnowledge Graph Embeddings+2A Systematic Evaluation of Knowledge Graph Embeddings for Gene-Disease Association Prediction
Discovery gene-disease links is important in biology and medicine areas, enabling disease identification and drug repurposing. Machine learning approaches accelerate this process by leveraging biological knowledge repres…
Knowledge Graph EmbeddingsKnowledge GraphsLink PredictionPredictionConceptFormer: Towards Efficient Use of Knowledge-Graph Embeddings in Large Language Models
Retrieval Augmented Generation (RAG) has enjoyed increased attention in the recent past and recent advancements in Large Language Models (LLMs) have highlighted the importance of integrating world knowledge into these sy…
Knowledge Graph EmbeddingsKnowledge GraphsRAGRetrieval-augmented Generation+1Comparison of Metadata Representation Models for Knowledge Graph Embeddings
Hyper-relational Knowledge Graphs (HRKGs) extend traditional KGs beyond binary relations, enabling the representation of contextual, provenance, and temporal information in domains, such as historical events, sensor data…
Knowledge Graph EmbeddingsKnowledge GraphsLink PredictionCorporate Fraud Detection in Rich-yet-Noisy Financial Graph
Corporate fraud detection aims to automatically recognize companies that conduct wrongful activities such as fraudulent financial statements or illegal insider trading. Previous learning-based methods fail to effectively…
Fraud DetectionKnowledge Graph EmbeddingsSparseTransX: Efficient Training of Translation-Based Knowledge Graph Embeddings Using Sparse Matrix Operations
Knowledge graph (KG) learning offers a powerful framework for generating new knowledge and making inferences. Training KG embedding can take a significantly long time, especially for larger datasets. Our analysis shows t…
CPUGPUKnowledge Graph EmbeddingsTranslationPathE: Leveraging Entity-Agnostic Paths for Parameter-Efficient Knowledge Graph Embeddings
Knowledge Graphs (KGs) store human knowledge in the form of entities (nodes) and relations, and are used extensively in various applications. KG embeddings are an effective approach to addressing tasks like knowledge dis…
Entity EmbeddingsKnowledge Graph EmbeddingsKnowledge GraphsLink Prediction+1Explainable Lane Change Prediction for Near-Crash Scenarios Using Knowledge Graph Embeddings and Retrieval Augmented Generation
Lane-changing maneuvers, particularly those executed abruptly or in risky situations, are a significant cause of road traffic accidents. However, current research mainly focuses on predicting safe lane changes. Furthermo…
Bayesian InferenceKnowledge Graph EmbeddingsRAGRetrieval-augmented GenerationA Semantic Partitioning Method for Large-Scale Training of Knowledge Graph Embeddings
In recent years, knowledge graph embeddings have achieved great success. Many methods have been proposed and achieved state-of-the-art results in various tasks. However, most of the current methods present one or more of…
Knowledge Graph EmbeddingsKnowledge GraphsKGIF: Optimizing Relation-Aware Recommendations with Knowledge Graph Information Fusion
While deep-learning-enabled recommender systems demonstrate strong performance benchmarks, many struggle to adapt effectively in real-world environments due to limited use of user-item relationship data and insufficient …
AttributeCollaborative FilteringGraph AttentionKnowledge Graph Embeddings+3Efficient support ticket resolution using Knowledge Graphs
A review of over 160,000 customer cases indicates that about 90% of time is spent by the product support for solving around 10% of subset of tickets where a trivial solution may not exist. Many of these challenging cases…
Knowledge Graph EmbeddingsKnowledge GraphsLearning-To-RankExtending TWIG: Zero-Shot Predictive Hyperparameter Selection for KGEs based on Graph Structure
Knowledge Graphs (KGs) have seen increasing use across various domains -- from biomedicine and linguistics to general knowledge modelling. In order to facilitate the analysis of knowledge graphs, Knowledge Graph Embeddin…
General KnowledgeKnowledge Graph EmbeddingsKnowledge GraphsLink PredictionA Survey on Knowledge Graph Structure and Knowledge Graph Embeddings
Knowledge Graphs (KGs) and their machine learning counterpart, Knowledge Graph Embedding Models (KGEMs), have seen ever-increasing use in a wide variety of academic and applied settings. In particular, KGEMs are typicall…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsKnowledge Graphs+2Visualization of Knowledge Graphs with Embeddings: an Essay on Recent Trends and Methods
In this essay we discuss the recent trends in visual analysis and exploration of Knowledge Graphs, particularly in conjunction with Knowledge Graph Embedding techniques. We present an overview of the current state of vis…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsKnowledge Graphs+1KAAE: Numerical Reasoning for Knowledge Graphs via Knowledge-aware Attributes Learning
Numerical reasoning is pivotal in various artificial intelligence applications, such as natural language processing and recommender systems, where it involves using entities, relations, and attribute values (e.g., weight…
AttributeContrastive LearningKnowledge Graph EmbeddingsKnowledge Graphs+2Addressing Hallucinations in Language Models with Knowledge Graph Embeddings as an Additional Modality
In this paper we present an approach to reduce hallucinations in Large Language Models (LLMs) by incorporating Knowledge Graphs (KGs) as an additional modality. Our method involves transforming input text into a set of K…
Entity LinkingKnowledge Graph EmbeddingsKnowledge GraphsLanguage Modelling+1Knowledge-enhanced Transformer for Multivariate Long Sequence Time-series Forecasting
Multivariate Long Sequence Time-series Forecasting (LSTF) has been a critical task across various real-world applications. Recent advancements focus on the application of transformer architectures attributable to their a…
Knowledge Graph EmbeddingsTime SeriesTime Series ForecastingMEG: Medical Knowledge-Augmented Large Language Models for Question Answering
Question answering is a natural language understanding task that involves reasoning over both explicit context and unstated, relevant domain knowledge. Large language models (LLMs), which underpin most contemporary quest…
Knowledge Graph EmbeddingsMultiple-choiceNatural Language UnderstandingQuestion AnsweringCapturing and Anticipating User Intents in Data Analytics via Knowledge Graphs
In today's data-driven world, the ability to extract meaningful information from data is becoming essential for businesses, organizations and researchers alike. For that purpose, a wide range of tools and systems exist a…
AutoMLData IntegrationKnowledge Graph EmbeddingsKnowledge Graphs+1