poster

QAnswer: Towards Question Answering Search over Websites

Authors:

Clement Defretiere,

Dennis Diefenbach,

Christophe Gravier,

Antoine GourruAuthors Info & Claims

WWW '22: Companion Proceedings of the Web Conference 2022

Pages 252 - 255

https://doi.org/10.1145/3487553.3524250

Published: 16 August 2022 Publication History

Abstract

Question Answering (QA) is increasingly used by search engines to provide results to their end-users, yet very few websites currently use QA technologies for their search functionality. To illustrate the potential of QA technologies for the website search practitioner, we demonstrate web searches that combine QA over knowledge graphs and QA over free text – each being usually tackled separately. We also discuss the different benefits and drawbacks of both approaches for web site searches. We use the case studies made of websites hosted by the Wikimedia Foundation (namely Wikipedia and Wikidata). Differently from a search engine (e.g. Google, Bing, etc), the data are indexed integrally, i.e. we do not index only a subset, and they are indexed exclusively, i.e. we index only data available on the corresponding website.

References

[1]

Nilesh Chakraborty, Denis Lukovnikov, Gaurav Maheshwari, Priyansh Trivedi, Jens Lehmann, and Asja Fischer. 2019. Introduction to neural network based approaches for question answering over knowledge graphs. arXiv preprint arXiv:1907.09361(2019).

[2]

Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017. Reading Wikipedia to Answer Open-Domain Questions. arxiv:1704.00051 [cs.CL]

[3]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the NAACL: Human Language Technologies. 4171–4186.

[4]

Dennis Diefenbach, Andreas Both, Kamal Singh, and Pierre Maret. 2020. Towards a question answering system over the semantic web. Semantic Web 11, 3 (2020), 421–439.

Digital Library

[5]

Dennis Diefenbach, Vanessa Lopez, Kamal Singh, and Pierre Maret. 2018. Core techniques of question answering systems over knowledge bases: a survey. Knowledge and Information systems 55, 3 (2018), 529–569.

Digital Library

[6]

Dennis Diefenbach, Pedro Henrique Migliatti, Omar Qawasmeh, Vincent Lully, Kamal Singh, and Pierre Maret. 2019. QAnswer: A Question Answering prototype bridging the gap between a considerable part of the LOD cloud and end-users. In The World Wide Web Conference. 3507–3510.

Digital Library

[7]

Daya Guo, Duyu Tang, Nan Duan, Ming Zhou, and Jian Yin. 2018. Dialog-to-action: Conversational question answering over a large-scale knowledge base. Advances in Neural Information Processing Systems 31 (2018).

[8]

Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S. Weld, Luke Zettlemoyer, and Omer Levy. 2020. SpanBERT: Improving Pre-training by Representing and Predicting Spans. arxiv:1907.10529 [cs.CL]

[9]

Omar Khattab and Matei Zaharia. 2020. ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT. arxiv:2004.12832 [cs.IR]

[10]

Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017. RACE: Large-scale ReAding Comprehension Dataset From Examinations. arxiv:1704.04683 [cs.CL]

[11]

Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692(2019).

[12]

Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, and John Schulman. 2021. WebGPT: Browser-assisted question-answering with human feedback. arxiv:2112.09332 [cs.CL]

[13]

Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250(2016).

[14]

Denny Vrandečić and Markus Krötzsch. 2014. Wikidata: a free collaborative knowledgebase. Commun. ACM 57, 10 (2014), 78–85.

Digital Library

[15]

Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019. GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. arxiv:1804.07461 [cs.CL]

[16]

Kun Xu, Yansong Feng, Songfang Huang, and Dongyan Zhao. 2016. Hybrid question answering over knowledge base and free text. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. 2397–2407.

[17]

Wei Yang, Yuqing Xie, Aileen Lin, Xingyu Li, Luchen Tan, Kun Xiong, Ming Li, and Jimmy Lin. 2019. End-to-End Open-Domain Question Answering with. Proceedings of the 2019 Conference of the North (2019). https://doi.org/10.18653/v1/n19-4013

[18]

Fengbin Zhu, Wenqiang Lei, Chao Wang, Jianming Zheng, Soujanya Poria, and Tat-Seng Chua. 2021. Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering. arxiv:2101.00774 [cs.AI]

Cited By

Pramanik SAlabi JRoy RWeikum G(2024)Uniqorn: Unified question answering over RDF knowledge graphs and natural language textJournal of Web Semantics10.1016/j.websem.2024.10083383(100833)Online publication date: Dec-2024
https://doi.org/10.1016/j.websem.2024.100833
Alemayehu SBisen KMaret PCreighton AGorman RKundi BMgwgwi TMuhlenbach FDinca-Panaitescu SMorr C(2022)Methodology for Creating a Community Corpus Using a Wikibase Knowledge GraphKnowledge Graphs and Semantic Web10.1007/978-3-031-21422-6_21(285-297)Online publication date: 13-Nov-2022
https://doi.org/10.1007/978-3-031-21422-6_21

Index Terms

QAnswer: Towards Question Answering Search over Websites
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Question answering
  2. World Wide Web
    1. Web searching and information discovery

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Information & Contributors

Information

Published In

cover image ACM Conferences

WWW '22: Companion Proceedings of the Web Conference 2022

April 2022

1338 pages

ISBN:9781450391306

DOI:10.1145/3487553

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Lionel Médini
Université Lyon 1, France
,
Ivan Herman
W3C / retired

Copyright © 2022 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 August 2022

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WWW '22

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SIGWEB

WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Cited By

Pramanik SAlabi JRoy RWeikum G(2024)Uniqorn: Unified question answering over RDF knowledge graphs and natural language textJournal of Web Semantics10.1016/j.websem.2024.10083383(100833)Online publication date: Dec-2024
https://doi.org/10.1016/j.websem.2024.100833
Alemayehu SBisen KMaret PCreighton AGorman RKundi BMgwgwi TMuhlenbach FDinca-Panaitescu SMorr C(2022)Methodology for Creating a Community Corpus Using a Wikibase Knowledge GraphKnowledge Graphs and Semantic Web10.1007/978-3-031-21422-6_21(285-297)Online publication date: 13-Nov-2022
https://doi.org/10.1007/978-3-031-21422-6_21

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