(Q30005091)

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Modeling Relational Data with Graph Convolutional Networks

scientific article (publication date: 30 March 2017)

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title

Modeling Relational Data with Graph Convolutional Networks (English)

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sponsor

European Research Council

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main subject

graph convolutional network

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relational data mining

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convolutional neural network

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6

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Thomas N. Kipf

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2

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Michael Schlichtkrull

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1

object named as

Michael Schlichtkrull

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Ivan Titov

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5

object named as

Ivan Titov

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Peter Bloem

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3

object named as

Peter Bloem

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Rianne van den Berg

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4

object named as

Rianne van den Berg

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reviewed by

Dagmar Gromann

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language of work or name

English

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publication date

30 March 2017

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first line

Knowledge bases play a crucial role in many applications, for example questionanswering and information retrieval. (English)

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official website

https://2018.eswc-conferences.org/paper_4/

language of work or name

English

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full work available at URL

https://arxiv.org/pdf/1703.06103.pdf

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https://2018.eswc-conferences.org/wp-content/uploads/2018/02/ESWC2018_paper_4.pdf

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published in

The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, Proceedings

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describes a project that uses

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Distributed Representations of Words and Phrases and their Compositionality

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27

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RDF2Vec: RDF Graph Embeddings for Data Mining

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23

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Embedding Entities and Relations for Learning and Inference in Knowledge Bases

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11

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Translating Embeddings for Modeling Multi-relational Data

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29

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Holographic Embeddings of Knowledge Graphs

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30

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Information Extraction over Structured Data: Question Answering with Freebase

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1

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Knowledge-Based Question Answering as Machine Translation

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2

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Identifiers

DOI

10.1007/978-3-319-93417-4_38

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arXiv ID

1703.06103

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DBLP publication ID

conf/esws/SchlichtkrullKB18

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stated in

DBLP Dataset 2021-01-02

retrieved

28 January 2021

(Q30005091)

Modeling Relational Data with Graph Convolutional Networks

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