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WIREs Data Mining Knowl Discov
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Introduction to neural network‐based question answering over knowledge graphs

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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. This article is categorized under: Technologies > Machine Learning Technologies > Prediction Technologies > Artificial Intelligence
An example knowledge graph (KG) which will serve as a running example throughout this article
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An example of (a) a (factual) NLQ and (b) a SPARQL query corresponding to the NLQ, which when executed over the KG presented in Figure 1 would return as the result. KG, knowledge graph; NLQ, natural language query
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Technologies > Artificial Intelligence
Technologies > Prediction
Technologies > Machine Learning

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