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  4. Introduction to neural network-based question answering over knowledge graphs
 
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2021
Journal Article
Title

Introduction to neural network-based question answering over knowledge graphs

Abstract
Question answering has emerged as an intuitive way of querying structured data sources and has attracted significant advancements over the years. A large body of recent work on question answering over knowledge graphs (KGQA) employs neural network-based systems. In this article, we provide an overview of these neural network-based methods for KGQA. We introduce readers to the formalism and the challenges of the task, different paradigms and approaches, discuss notable advancements, and outline the emerging trends in the field. Through this article, we aim to provide newcomers to the field with a suitable entry point to semantic parsing for KGQA, and ease their process of making informed decisions while creating their own QA systems.
Author(s)
Chakraborty, Nilesh  
Universität Bonn
Lukovnikov, Denis  
Universität Bochum
Maheshwari, Gaurav  
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Trivedi, Priyansh  
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Lehmann, Jens  
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Fischer, Asja
Universität Bochum
Journal
Wiley interdisciplinary reviews. Data mining and knowledge discovery  
Project(s)
Cleopatra
Funder
European Commission EC  
Open Access
DOI
10.1002/widm.1389
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Keyword(s)
  • deep learning

  • knowledge graph

  • question answering

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