Details
| Original language | English |
|---|---|
| Title of host publication | The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018 |
| Subtitle of host publication | Proceedings of the 2018 World Wide Web Conference |
| Place of Publication | Geneva |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 1247-1256 |
| Number of pages | 10 |
| ISBN (electronic) | 9781450356398 |
| Publication status | Published - 10 Apr 2018 |
Publication series
| Name | Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18 |
|---|
Abstract
Modern question answering (QA) systems need to flexibly integrate a number of components specialised to fulfil specific tasks in a QA pipeline. Key QA tasks include Named Entity Recognition and Disambiguation, Relation Extraction, and Query Building. Since a number of different software components exist that implement different strategies for each of these tasks, it is a major challenge to select and combine the most suitable components into a QA system, given the characteristics of a question. We study this optimisation problem and train classifiers, which take features of a question as input and have the goal of optimising the selection of QA components based on those features. We then devise a greedy algorithm to identify the pipelines that include the suitable components and can effectively answer the given question. We implement this model within Frankenstein, a QA framework able to select QA components and compose QA pipelines. We evaluate the effectiveness of the pipelines generated by Frankenstein using the QALD and LC-QuAD benchmarks. These results not only suggest that Frankenstein precisely solves the QA optimisation problem but also enables the automatic composition of optimised QA pipelines, which outperform the static Baseline QA pipeline. Thanks to this flexible and fully automated pipeline generation process, new QA components can be easily included in Frankenstein, thus improving the performance of the generated pipelines.
Keywords
- Question Answering, Software Reusability, Semantic Web, Semantic Search, QA Framework, Semantic web, Semantic search, Question answering, QA framework, Software reusability
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Computer Networks and Communications
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The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018: Proceedings of the 2018 World Wide Web Conference. Geneva: Association for Computing Machinery (ACM), 2018. p. 1247-1256 (Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Why Reinvent the Wheel
T2 - Let's Build Question Answering Systems Together
AU - Singh, Kuldeep
AU - Radhakrishna, Arun Sethupat
AU - Both, Andreas
AU - Shekarpour, Saeedeh
AU - Lytra, Ioanna
AU - Usbeck, Ricardo
AU - Vyas, Akhilesh
AU - Khikmatullaev, Akmal
AU - Punjani, Dharmen
AU - Lange, Christoph
AU - Vidal, Maria Esther
AU - Lehmann, Jens
AU - Auer, Soeren
N1 - Funding information: This work has received funding from the EU H2020 R&I programme for the Marie Sk?odowska-Curie action WDAqua (GA No 642795), Eurostars project QAMEL (E!9725), and EU H2020 R&I HOBBIT (GA 688227). We thank Yakun Li, Osmar Zaiane, and Anant Gupta for their useful suggestions. This work has received funding from the EU H2020 R&I programme for the Marie Sklodowska-Curie action WDAqua (GA No 642795), Eurostars project QAMEL (E!9725), and EU H2020 R&I HOBBIT (GA 688227). We thank Yakun Li, Osmar Zaiane, and Anant Gupta for their useful suggestions.
PY - 2018/4/10
Y1 - 2018/4/10
N2 - Modern question answering (QA) systems need to flexibly integrate a number of components specialised to fulfil specific tasks in a QA pipeline. Key QA tasks include Named Entity Recognition and Disambiguation, Relation Extraction, and Query Building. Since a number of different software components exist that implement different strategies for each of these tasks, it is a major challenge to select and combine the most suitable components into a QA system, given the characteristics of a question. We study this optimisation problem and train classifiers, which take features of a question as input and have the goal of optimising the selection of QA components based on those features. We then devise a greedy algorithm to identify the pipelines that include the suitable components and can effectively answer the given question. We implement this model within Frankenstein, a QA framework able to select QA components and compose QA pipelines. We evaluate the effectiveness of the pipelines generated by Frankenstein using the QALD and LC-QuAD benchmarks. These results not only suggest that Frankenstein precisely solves the QA optimisation problem but also enables the automatic composition of optimised QA pipelines, which outperform the static Baseline QA pipeline. Thanks to this flexible and fully automated pipeline generation process, new QA components can be easily included in Frankenstein, thus improving the performance of the generated pipelines.
AB - Modern question answering (QA) systems need to flexibly integrate a number of components specialised to fulfil specific tasks in a QA pipeline. Key QA tasks include Named Entity Recognition and Disambiguation, Relation Extraction, and Query Building. Since a number of different software components exist that implement different strategies for each of these tasks, it is a major challenge to select and combine the most suitable components into a QA system, given the characteristics of a question. We study this optimisation problem and train classifiers, which take features of a question as input and have the goal of optimising the selection of QA components based on those features. We then devise a greedy algorithm to identify the pipelines that include the suitable components and can effectively answer the given question. We implement this model within Frankenstein, a QA framework able to select QA components and compose QA pipelines. We evaluate the effectiveness of the pipelines generated by Frankenstein using the QALD and LC-QuAD benchmarks. These results not only suggest that Frankenstein precisely solves the QA optimisation problem but also enables the automatic composition of optimised QA pipelines, which outperform the static Baseline QA pipeline. Thanks to this flexible and fully automated pipeline generation process, new QA components can be easily included in Frankenstein, thus improving the performance of the generated pipelines.
KW - Question Answering
KW - Software Reusability
KW - Semantic Web
KW - Semantic Search
KW - QA Framework
KW - Semantic web
KW - Semantic search
KW - Question answering
KW - QA framework
KW - Software reusability
UR - http://www.scopus.com/inward/record.url?scp=85076220402&partnerID=8YFLogxK
U2 - 10.1145/3178876.3186023
DO - 10.1145/3178876.3186023
M3 - Conference contribution
T3 - Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18
SP - 1247
EP - 1256
BT - The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018
PB - Association for Computing Machinery (ACM)
CY - Geneva
ER -