RQUERY: Rewriting natural language queries on knowledge graphs to alleviate the vocabulary mismatch problem

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

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Externe Organisationen

  • Kno.e.sis Center
  • Universität Leipzig
  • Rheinische Friedrich-Wilhelms-Universität Bonn
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OriginalspracheEnglisch
Titel des SammelwerksProceedings of the Thirty-First AAAI Conference on Artificial Intelligence, Twenty-Ninth Innovative Applications of Artificial Intelligence Conference, Seventh Symposium on Educational Advances in Artificial Intelligence
Untertitel4-9 February 2017, San Francisco, California, USA / AAAI-17 San Francisco ; sponsored by the Association for the Advancement of Artificial Intelligence
ErscheinungsortPalo Alto
Seiten3936-3943
Seitenumfang8
PublikationsstatusVeröffentlicht - 1 Jan. 2017
Extern publiziertJa
Veranstaltung31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, USA / Vereinigte Staaten
Dauer: 4 Feb. 201710 Feb. 2017

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RQUERY: Rewriting natural language queries on knowledge graphs to alleviate the vocabulary mismatch problem. / Shekarpour, Saeedeh; Marx, Edgard; Auer, Sören et al.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, Twenty-Ninth Innovative Applications of Artificial Intelligence Conference, Seventh Symposium on Educational Advances in Artificial Intelligence: 4-9 February 2017, San Francisco, California, USA / AAAI-17 San Francisco ; sponsored by the Association for the Advancement of Artificial Intelligence. Palo Alto, 2017. S. 3936-3943.

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Shekarpour, S, Marx, E, Auer, S & Sheth, A 2017, RQUERY: Rewriting natural language queries on knowledge graphs to alleviate the vocabulary mismatch problem. in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, Twenty-Ninth Innovative Applications of Artificial Intelligence Conference, Seventh Symposium on Educational Advances in Artificial Intelligence: 4-9 February 2017, San Francisco, California, USA / AAAI-17 San Francisco ; sponsored by the Association for the Advancement of Artificial Intelligence. Palo Alto, S. 3936-3943, 31st AAAI Conference on Artificial Intelligence, AAAI 2017, San Francisco, USA / Vereinigte Staaten, 4 Feb. 2017.
Shekarpour, S., Marx, E., Auer, S., & Sheth, A. (2017). RQUERY: Rewriting natural language queries on knowledge graphs to alleviate the vocabulary mismatch problem. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, Twenty-Ninth Innovative Applications of Artificial Intelligence Conference, Seventh Symposium on Educational Advances in Artificial Intelligence: 4-9 February 2017, San Francisco, California, USA / AAAI-17 San Francisco ; sponsored by the Association for the Advancement of Artificial Intelligence (S. 3936-3943).
Shekarpour S, Marx E, Auer S, Sheth A. RQUERY: Rewriting natural language queries on knowledge graphs to alleviate the vocabulary mismatch problem. in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, Twenty-Ninth Innovative Applications of Artificial Intelligence Conference, Seventh Symposium on Educational Advances in Artificial Intelligence: 4-9 February 2017, San Francisco, California, USA / AAAI-17 San Francisco ; sponsored by the Association for the Advancement of Artificial Intelligence. Palo Alto. 2017. S. 3936-3943
Shekarpour, Saeedeh ; Marx, Edgard ; Auer, Sören et al. / RQUERY : Rewriting natural language queries on knowledge graphs to alleviate the vocabulary mismatch problem. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, Twenty-Ninth Innovative Applications of Artificial Intelligence Conference, Seventh Symposium on Educational Advances in Artificial Intelligence: 4-9 February 2017, San Francisco, California, USA / AAAI-17 San Francisco ; sponsored by the Association for the Advancement of Artificial Intelligence. Palo Alto, 2017. S. 3936-3943
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title = "RQUERY: Rewriting natural language queries on knowledge graphs to alleviate the vocabulary mismatch problem",
author = "Saeedeh Shekarpour and Edgard Marx and S{\"o}ren Auer and Amit Sheth",
note = "Funding information: Acknowledgments. We acknowledge partial support from the National Science Foundation (NSF) awards: (1) EAR 1520870: Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response. (2) CNS 1513721: Context-Aware Harassment Detection on Social Media. Any opinions, findings, and conclusions/recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF.; 31st AAAI Conference on Artificial Intelligence, AAAI 2017 ; Conference date: 04-02-2017 Through 10-02-2017",
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AU - Auer, Sören

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N1 - Funding information: Acknowledgments. We acknowledge partial support from the National Science Foundation (NSF) awards: (1) EAR 1520870: Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response. (2) CNS 1513721: Context-Aware Harassment Detection on Social Media. Any opinions, findings, and conclusions/recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF.

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