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Introduction to neural network‐based question answering over knowledge graphs
Author(s) -
Chakraborty Nilesh,
Lukovnikov Denis,
Maheshwari Gaurav,
Trivedi Priyansh,
Lehmann Jens,
Fischer Asja
Publication year - 2021
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1389
Subject(s) - computer science , question answering , artificial intelligence , formalism (music) , knowledge graph , field (mathematics) , artificial neural network , parsing , data science , art , musical , mathematics , pure mathematics , visual arts
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. This article is categorized under: Technologies > Machine Learning Technologies > Prediction Technologies > Artificial Intelligence