Papers Knowledge Graph Embedding
“Knowledge Graph Embedding” 태그가 달린 논문 499편 · 필터 해제
Embedding Method for Knowledge Graph with Densely Defined Ontology
Knowledge graph embedding (KGE) is a technique that enhances knowledge graphs by addressing incompleteness and improving knowledge retrieval. A limitation of the existing KGE models is their underutilization of ontologie…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsRetrievalMulti-dataset and Transfer Learning Using Gene Expression Knowledge Graphs
Gene expression datasets offer insights into gene regulation mechanisms, biochemical pathways, and cellular functions. Additionally, comparing gene expression profiles between disease and control patients can deepen the …
Data IntegrationGraph EmbeddingGraph Neural NetworkKnowledge Graph Embedding+2SoTCKGE:Continual Knowledge Graph Embedding Based on Spatial Offset Transformation
Current Continual Knowledge Graph Embedding (CKGE) methods primarily rely on translation-based embedding methods, leveraging previously acquired knowledge to initialize new facts. To enhance learning efficiency, these me…
Continual LearningGraph EmbeddingKnowledge Graph EmbeddingTranslationA Zero-shot Learning Method Based on Large Language Models for Multi-modal Knowledge Graph Embedding
Zero-shot learning (ZL) is crucial for tasks involving unseen categories, such as natural language processing, image classification, and cross-lingual transfer. Current applications often fail to accurately infer and han…
Cross-Lingual TransferGraph Embeddingimage-classificationImage Classification+4Transformer-Based Multimodal Knowledge Graph Completion with Link-Aware Contexts
Multimodal knowledge graph completion (MMKGC) aims to predict missing links in multimodal knowledge graphs (MMKGs) by leveraging information from various modalities alongside structural data. Existing MMKGC approaches pr…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge GraphsLarge Language Models for Knowledge Graph Embedding Techniques, Methods, and Challenges: A Survey
Large Language Models (LLMs) have attracted a lot of attention in various fields due to their superior performance, aiming to train hundreds of millions or more parameters on large amounts of text data to understand and …
Graph EmbeddingKnowledge Graph EmbeddingMultimodal Contrastive Representation Learning in Augmented Biomedical Knowledge Graphs
Biomedical Knowledge Graphs (BKGs) integrate diverse datasets to elucidate complex relationships within the biomedical field. Effective link prediction on these graphs can uncover valuable connections, such as potential …
Contrastive LearningGraph EmbeddingKnowledge Graph EmbeddingKnowledge Graphs+2Efficient 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+3A 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+2Prediction of Occluded Pedestrians in Road Scenes using Human-like Reasoning: Insights from the OccluRoads Dataset
Pedestrian detection is a critical task in autonomous driving, aimed at enhancing safety and reducing risks on the road. Over recent years, significant advancements have been made in improving detection performance. Howe…
Autonomous DrivingBayesian InferenceGraph EmbeddingKnowledge Graph Embedding+1Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation
Quaternion contains one real part and three imaginary parts, which provided a more expressive hypercomplex space for learning knowledge graph. Existing quaternion embedding models measure the plausibility of a triplet ei…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingLink Prediction+2Learn to Unlearn: Meta-Learning-Based Knowledge Graph Embedding Unlearning
Knowledge graph (KG) embedding methods map entities and relations into continuous vector spaces, improving performance in tasks like link prediction and question answering. With rising privacy concerns, machine unlearnin…
Graph EmbeddingKnowledge Graph EmbeddingLink PredictionMachine Unlearning+2Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models
Knowledge Graph Embedding models, representing entities and edges in a low-dimensional space, have been extremely successful at solving tasks related to completing and exploring Knowledge Graphs (KGs). One of the key asp…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsVisualization 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+1Fully 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 GraphsDetecting 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+3Geometric Feature Enhanced Knowledge Graph Embedding and Spatial Reasoning
Geospatial Knowledge Graphs (GeoKGs) model geoentities (e.g., places and natural features) and spatial relationships in an interconnected manner, providing strong knowledge support for geographic applications, including …
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsLink Prediction+3Multidimensional 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 EmbeddingsKnowledge 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+3