Cargando…

Embedding Learning with Triple Trustiness on Noisy Knowledge Graph

Embedding learning on knowledge graphs (KGs) aims to encode all entities and relationships into a continuous vector space, which provides an effective and flexible method to implement downstream knowledge-driven artificial intelligence (AI) and natural language processing (NLP) tasks. Since KG const...

Descripción completa

Detalles Bibliográficos
Autores principales:	Zhao, Yu, Feng, Huali, Gallinari, Patrick
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2019
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514427/ http://dx.doi.org/10.3390/e21111083

Ejemplares similares

Serology for visceral leishmaniasis: How trusty is the accuracy reported by the manufacturers?
por: Freire, Mariana Lourenço, et al.
Publicado: (2023)

Exploration of chemical space with partial labeled noisy student self-training and self-supervised graph embedding
por: Liu, Yang, et al.
Publicado: (2022)

A type-augmented knowledge graph embedding framework for knowledge graph completion
por: He, Peng, et al.
Publicado: (2023)

Learning from Knowledge Graphs: Neural Fine-Grained Entity Typing with Copy-Generation Networks
por: Yu, Zongjian, et al.
Publicado: (2022)

Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion
por: Kolyvakis, Prodromos, et al.
Publicado: (2020)

Representation Learning: Recommendation With Knowledge Graph via Triple-Autoencoder
por: Geng, Yishuai, et al.
Publicado: (2022)

Embedding-Based Recommendations on Scholarly Knowledge Graphs
por: Nayyeri, Mojtaba, et al.
Publicado: (2020)

A Knowledge Graph Entity Disambiguation Method Based on Entity-Relationship Embedding and Graph Structure Embedding
por: Ma, Jiangtao, et al.
Publicado: (2021)

Knowledge Graph Embeddings for ICU readmission prediction
por: Carvalho, Ricardo M. S., et al.
Publicado: (2023)

GFCNet: Utilizing graph feature collection networks for coronavirus knowledge graph embeddings
por: Xie, Zhiwen, et al.
Publicado: (2022)

Prediction of adverse drug reactions based on knowledge graph embedding
por: Zhang, Fei, et al.
Publicado: (2021)

Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization
por: Taillé, Bruno, et al.
Publicado: (2020)

Adverse Drug Event Prediction Using Noisy Literature-Derived Knowledge Graphs: Algorithm Development and Validation
por: Dasgupta, Soham, et al.
Publicado: (2021)

Survey on graph embeddings and their applications to machine learning problems on graphs
por: Makarov, Ilya, et al.
Publicado: (2021)

Application and evaluation of knowledge graph embeddings in biomedical data
por: Alshahrani, Mona, et al.
Publicado: (2021)

Assessing the effects of hyperparameters on knowledge graph embedding quality
por: Lloyd, Oliver, et al.
Publicado: (2023)

A Method to Learn Embedding of a Probabilistic Medical Knowledge Graph: Algorithm Development
por: Li, Linfeng, et al.
Publicado: (2020)

Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque
por: Fernández-Torras, Adrià, et al.
Publicado: (2022)

Embedded graphs
por: Lando, S, et al.
Publicado: (2001)

Node Classification in Complex Social Graphs via Knowledge-Graph Embeddings and Convolutional Neural Network
por: Molokwu, Bonaventure C., et al.
Publicado: (2020)

CGNet: A graph-knowledge embedded convolutional neural network for detection of pneumonia
por: Yu, Xiang, et al.
Publicado: (2021)

A new Graph Gaussian embedding method for analyzing the effects of cognitive training
por: Xu, Mengjia, et al.
Publicado: (2020)

Multimodal reasoning based on knowledge graph embedding for specific diseases
por: Zhu, Chaoyu, et al.
Publicado: (2022)

Time-aware Embeddings of Clinical Data using a Knowledge Graph
por: Soman, Karthik, et al.
Publicado: (2023)

An inductive knowledge graph embedding via combination of subgraph and type information
por: Liu, Hongbo, et al.
Publicado: (2023)

Predicting biomedical relationships using the knowledge and graph embedding cascade model
por: Liang, Xiaomin, et al.
Publicado: (2019)

Knowledge graph embedding for profiling the interaction between transcription factors and their target genes
por: Wu, Yang-Han, et al.
Publicado: (2023)

Ensembles of knowledge graph embedding models improve predictions for drug discovery
por: Rivas-Barragan, Daniel, et al.
Publicado: (2022)

Multi-domain knowledge graph embeddings for gene-disease association prediction
por: Nunes, Susana, et al.
Publicado: (2023)

Text-Graph Enhanced Knowledge Graph Representation Learning
por: Hu, Linmei, et al.
Publicado: (2021)

Improving Knowledge Graph Embedding Using Locally and Globally Attentive Relation Paths
por: Jia, Ningning, et al.
Publicado: (2020)

Joint Word and Entity Embeddings for Entity Retrieval from a Knowledge Graph
por: Nikolaev, Fedor, et al.
Publicado: (2020)

On dihedral flows in embedded graphs
por: Litjens, Bart
Publicado: (2018)

Iterative heterogeneous graph learning for knowledge graph-based recommendation
por: Liu, Tieyuan, et al.
Publicado: (2023)

MedGraph: A semantic biomedical information retrieval framework using knowledge graph embedding for PubMed
por: Ebeid, Islam Akef
Publicado: (2022)

Embedding graphs in Lorentzian spacetime
por: Clough, James R., et al.
Publicado: (2017)

Graph Embedding Deep Learning Guides Microbial Biomarkers' Identification
por: Zhu, Qiang, et al.
Publicado: (2019)

Enhancing drug–drug interaction prediction by three-way decision and knowledge graph embedding
por: Hao, Xinkun, et al.
Publicado: (2022)

Learning Gait Representations with Noisy Multi-Task Learning
por: Cosma, Adrian, et al.
Publicado: (2022)

Graph Embedding for Pattern Analysis
por: Fu, Yun, et al.
Publicado: (2013)

Cannot write session to /tmp/vufind_sessions/sess_7kaptl1ccervf4jl90un1sh3c3