Introduction to neural network‐based question answering over knowledge graphs,WIREs Data Mining and Knowledge Discovery

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Introduction to neural network‐based question answering over knowledge graphs
WIREs Data Mining and Knowledge Discovery ( IF 6.4 ) Pub Date : 2021-02-01 , DOI: 10.1002/widm.1389
Nilesh Chakraborty ₁ , Denis Lukovnikov ₂ , Gaurav Maheshwari ₃ , Priyansh Trivedi ₃ , Jens Lehmann _{1,

3} , Asja Fischer ₂

Affiliation

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.

中文翻译：

基于神经网络的知识图问题解答简介

问题解答已经成为一种查询结构化数据源的直观方法，并且在过去几年中已经取得了重大进展。最近有关知识图问题解答（KGQA）的大量工作都使用基于神经网络的系统。在本文中，我们概述了这些基于神经网络的KGQA方法。我们向读者介绍形式主义和任务的挑战，不同的范式和方法，讨论显着的进步，并概述该领域的新兴趋势。通过本文，我们旨在为该领域的新手提供适合KGQA语义解析的切入点，并简化他们在创建自己的QA系统时做出明智决策的过程。

更新日期：2021-02-01

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