Link Prediction of Weighted Triples for Knowledge Graph Completion Within the Scholarly Domain-Reference-Cited by-同舟云学术

Link Prediction of Weighted Triples for Knowledge Graph Completion Within the Scholarly Domain

Published:2021 Issue: Volume:9 Page:116002-116014
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Nayyeri Mojtaba¹,Cil Gokce Muge¹,Vahdati Sahar²,Osborne Francesco³^ORCID,Kravchenko Andrey⁴,Angioni Simone⁵^ORCID,Salatino Angelo³^ORCID,Reforgiato Recupero Diego⁵^ORCID,Motta Enrico³^ORCID,Lehmann Jens¹^ORCID

Affiliation:

1. SDA Research Group, University of Bonn, Bonn, Germany

2. Nature-Inspired Machine Intelligence, Institute for Applied Informatics (InfAI), Dresden, Germany

3. Knowledge Media Institute, The Open University, Milton Keynes, U.K

4. Christ Church, University of Oxford, Oxford, U.K

5. Department of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy

Funder

project Speaker

project Joseph (Fraunhofer Zukunftsstiftung)–Cleopatra

project Excellence Clusters ML2R

project MLwin

ScaDS.AI

project Tailor

project H2020-EU PLATOON

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/9312710/09514834.pdf?arnumber=9514834

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