Recent years have seen a growing amount of research on question answering (QA) over Semantic Web data, shaping an interaction paradigm that allows end users to profit from the expressive power of Semantic Web standards. At the same time, QA systems hide their complexity behind an intuitive and easy-touse interface. However, the growing amount of data available on the Semantic Web has led to a heterogeneous data landscape where QA systems struggle to keep up with the volume, variety and veracity of the underlying knowledge. The Question Answering over Linked Data (QALD) challenges aim to provide up-to-date benchmarks for assessing and comparing state-of-the-art systems that mediate between a user, expressing his or her information need in natural language, and RDF data. In the past few years, more than 40 research groups and their systems have taken part in the last nine QALD challenges. The QALD challenge targets all researchers and practitioners working on querying Linked Data, natural language processing for question answering, multilingual information retrieval and related topics. The main goal is to gain insights into the strengths and shortcomings of different approaches and into possible solutions for coping with the large, heterogeneous and distributed nature of Semantic Web data. QALD has a 8-year history. The challenge began in 2011 and is developing benchmarks that are increasingly being used as a standard evaluation venue for question answering over Linked Data. Overviews of past instantiations of the challenge are available from the CLEF Working Notes, CEUR workshop notes as well as ESWC proceedings, see Table 1. This article will give a technical overview of the task and results of the 9th Question Answering over Linked Data challenge.
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