Abstract
The past 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 while, at the same time, hiding their complexity behind an intuitive and easy-to-use interface. On the other hand, the growing amount of data has led to a heterogeneous data landscape where QA systems struggle to keep up with the volume, variety and veracity of the underlying knowledge.
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https://github.com/AKSW/gerbil/wiki/Question-Answering and the results are formatted according to https://www.w3.org/TR/sparql11-results-json/.
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Acknowledgments
This work was supported by the Eurostars projects DIESEL (E!9367) and QAMEL (E!9725) as well as the European Union’s H2020 research and innovation action HOBBIT under the Grant Agreement number 688227. We also want to thank Christina Unger and Sebastian Walter for supporting this challenge.
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Usbeck, R., Ngomo, AC.N., Haarmann, B., Krithara, A., Röder, M., Napolitano, G. (2017). 7th Open Challenge on Question Answering over Linked Data (QALD-7). In: Dragoni, M., Solanki, M., Blomqvist, E. (eds) Semantic Web Challenges. SemWebEval 2017. Communications in Computer and Information Science, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-319-69146-6_6
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DOI: https://doi.org/10.1007/978-3-319-69146-6_6
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