Question Answering over Linked Data (QALD-6)

 

QALD-6 is the sixth in a series of evaluation campaigns on question answering over linked data, with a strong emphasis on multilinguality and hybrid approaches using information from both structured and unstructured data.
QALD-6 is a challenge at ESWC 2016.

 

Motivation

The past years have seen a growing amount of research on question answering over Semantic Web data, shaping an interaction paradigm that allows end users to profit from the expressive power of Semantic Web standards while at the same time hiding their complexity behind an intuitive and easy-to-use interface. The Question Answering over Linked Data challenge provides an up-to-date benchmark for assessing and comparing systems that mediate between a user, expressing his or her information need in natural language, and RDF data. It thus targets all researchers and practitioners working on querying linked data, natural language processing for question answering, multilingual information retrieval and related topics. The key challenge for question answering over linked data is to translate a user's information need into a form such that it can be evaluated using standard Semantic Web query processing and inferencing techniques. In order to focus on specific aspects and involved challenges, QALD comprises three tasks: multilingual question answering over DBpedia, hybrid question answering over both RDF and free text data, and question answering over statistical data in RDF data cubes. The main goal is to gain insights into the strengths and shortcomings of different approaches and into possible solutions for coping with the heterogenuous and distributed nature of Semantic Web data.