Benchmarking Geospatial Question Answering Engines Using the Dataset GeoQuestions1089-Reference-Cited by-同舟云学术

Benchmarking Geospatial Question Answering Engines Using the Dataset GeoQuestions1089

Published:2023 Issue: Volume: Page:266-284
ISSN:0302-9743
Container-title:The Semantic Web – ISWC 2023
language:
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Author:

Kefalidis Sergios-Anestis,Punjani Dharmen,Tsalapati Eleni,Plas Konstantinos,Pollali Mariangela,Mitsios Michail,Tsokanaridou Myrto,Koubarakis Manolis,Maret Pierre

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-47243-5_15

Reference31 articles.

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3. Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, 2–7 June 2019, Volume 1 (Long and Short Papers). pp. 4171–4186. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/n19-1423

4. Dsouza, A., Tempelmeier, N., Yu, R., Gottschalk, S., Demidova, E.: WorldKG: A world-scale geographic knowledge graph. In: CIKM ’21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, 1–5 November 2021, pp. 4475–4484. ACM (2021)

5. Dubey, M., Banerjee, D., Abdelkawi, A., Lehmann, J.: LC-QuAD 2.0: a large dataset for complex question answering over wikidata and DBpedia. In: ISWC (2019)

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