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Introduction to neural network-based question answering over knowledge graphs

 
: Chakraborty, Nilesh; Lukovnikov, Denis; Maheshwari, Gaurav; Trivedi, Priyansh; Lehmann, Jens; Fischer, Aslj

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Wiley interdisciplinary reviews. Data mining and knowledge discovery 11 (2021), Nr.3, Art. e1389, 25 S.
ISSN: 1942-4795
ISSN: 1942-4787
European Commission EC
H2020; 812997; Cleopatra
Cross-lingual Event-centric Open Analytics Research Academy
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IAIS ()
deep learning; knowledge graph; question answering

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.

: http://publica.fraunhofer.de/dokumente/N-636776.html