Papers Knowledge Graph Embeddings
“Knowledge Graph Embeddings” 태그가 달린 논문 269편 · 필터 해제
Detecting text level intellectual influence with knowledge graph embeddings
Introduction: Tracing the spread of ideas and the presence of influence is a question of special importance across a wide range of disciplines, ranging from intellectual history to cultural analytics, computational socia…
ArticlesGraph EmbeddingGraph Neural NetworkKnowledge Graph Embedding+2Resilience in Knowledge Graph Embeddings
In recent years, knowledge graphs have gained interest and witnessed widespread applications in various domains, such as information retrieval, question-answering, recommendation systems, amongst others. Large-scale know…
Graph EmbeddingInformation RetrievalKnowledge Graph EmbeddingKnowledge Graph Embeddings+3Information for Conversation Generation: Proposals Utilising Knowledge Graphs
LLMs are frequently used tools for conversational generation. Without additional information LLMs can generate lower quality responses due to lacking relevant content and hallucinations, as well as the perception of poor…
Knowledge Graph EmbeddingsKnowledge GraphsResponse GenerationVisual Representation Learning Guided By Multi-modal Prior Knowledge
Despite the remarkable success of deep neural networks (DNNs) in computer vision, they fail to remain high-performing when facing distribution shifts between training and testing data. In this paper, we propose Knowledge…
image-classificationImage ClassificationKnowledge Graph EmbeddingsRepresentation LearningMultidimensional Knowledge Graph Embeddings for International Trade Flow Analysis
Understanding the complex dynamics of high-dimensional, contingent, and strongly nonlinear economic data, often shaped by multiplicative processes, poses significant challenges for traditional regression methods as such …
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsTransBox: EL++-closed Ontology Embedding
OWL (Web Ontology Language) ontologies, which are able to represent both relational and type facts as standard knowledge graphs and complex domain knowledge in Description Logic (DL) axioms, are widely adopted in domains…
Knowledge Graph EmbeddingsKnowledge GraphsOntology EmbeddingKnowledge Graph Embeddings: A Comprehensive Survey on Capturing Relation Properties
Knowledge Graph Embedding (KGE) techniques play a pivotal role in transforming symbolic Knowledge Graphs (KGs) into numerical representations, thereby enhancing various deep learning models for knowledge-augmented applic…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsKnowledge Graphs+3Evaluating the Predictive Features of Person-Centric Knowledge Graph Embeddings: Unfolding Ablation Studies
Developing novel predictive models with complex biomedical information is challenging due to various idiosyncrasies related to heterogeneity, standardization or sparseness of the data. We previously introduced a person-c…
Knowledge Graph EmbeddingsKnowledge GraphsPerson-Centric Knowledge GraphsReadmission Prediction+1Conformalized Answer Set Prediction for Knowledge Graph Embedding
Knowledge graph embeddings (KGE) apply machine learning methods on knowledge graphs (KGs) to provide non-classical reasoning capabilities based on similarities and analogies. The learned KG embeddings are typically used …
Conformal PredictionGraph EmbeddingKnowledge Graph EmbeddingKnowledge Graph Embeddings+3Predictive Multiplicity of Knowledge Graph Embeddings in Link Prediction
Knowledge graph embedding (KGE) models are often used to predict missing links for knowledge graphs (KGs). However, multiple KG embeddings can perform almost equally well for link prediction yet give conflicting predicti…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsKnowledge Graphs+2OWL2Vec4OA: Tailoring Knowledge Graph Embeddings for Ontology Alignment
Ontology alignment is integral to achieving semantic interoperability as the number of available ontologies covering intersecting domains is increasing. This paper proposes OWL2Vec4OA, an extension of the ontology embedd…
Knowledge Graph EmbeddingsOntology EmbeddingKnowledge Base Embeddings: Semantics and Theoretical Properties
Research on knowledge graph embeddings has recently evolved into knowledge base embeddings, where the goal is not only to map facts into vector spaces but also constrain the models so that they take into account the rele…
Knowledge Graph EmbeddingsApproximating Probabilistic Inference in Statistical EL with Knowledge Graph Embeddings
Statistical information is ubiquitous but drawing valid conclusions from it is prohibitively hard. We explain how knowledge graph embeddings can be used to approximate probabilistic inference efficiently using the exampl…
Knowledge Graph EmbeddingsvalidFrom 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+1Towards Knowledge-Infused Automated Disease Diagnosis Assistant
With the advancement of internet communication and telemedicine, people are increasingly turning to the web for various healthcare activities. With an ever-increasing number of diseases and symptoms, diagnosing patients …
DiagnosticGraph AttentionKnowledge Graph EmbeddingsUntargeted Adversarial Attack on Knowledge Graph Embeddings
Knowledge graph embedding (KGE) methods have achieved great success in handling various knowledge graph (KG) downstream tasks. However, KGE methods may learn biased representations on low-quality KGs that are prevalent i…
Adversarial AttackGraph EmbeddingKnowledge Graph EmbeddingKnowledge Graph Embeddings+1RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models
Prediction of road users' behaviors in the context of autonomous driving has gained considerable attention by the scientific community in the last years. Most works focus on predicting behaviors based on kinematic inform…
Autonomous DrivingBayesian InferenceKnowledge Graph EmbeddingsKnowledge Graphs+3Sharing Parameter by Conjugation for Knowledge Graph Embeddings in Complex Space
A Knowledge Graph (KG) is the directed graphical representation of entities and relations in the real world. KG can be applied in diverse Natural Language Processing (NLP) tasks where knowledge is required. The need to s…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsKnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable Adaptation
Parameter-efficient finetuning (PEFT) is a key technique for adapting large language models (LLMs) to downstream tasks. In this paper, we study leveraging knowledge graph embeddings to improve the effectiveness of PEFT. …
Knowledge Graph EmbeddingsKnowledge GraphsCounterfactual Reasoning with Knowledge Graph Embeddings
Knowledge graph embeddings (KGEs) were originally developed to infer true but missing facts in incomplete knowledge repositories. In this paper, we link knowledge graph completion and counterfactual reasoning via our new…
counterfactualCounterfactual ReasoningKnowledge Graph CompletionKnowledge Graph Embeddings