TY - CHAP

T1 - Why reinvent the wheel

T2 - 27th International World Wide Web, WWW 2018

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, Sören

N1 - Publisher Copyright: © 2018 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC BY 4.0 License.

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 - QA framework

KW - Question answering

KW - Semantic search

KW - Semantic web

KW - Software reusability

KW - Informatics

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=85076220402&partnerID=8YFLogxK

U2 - 10.1145/3178876.3186023

DO - 10.1145/3178876.3186023

M3 - Article in conference proceedings

AN - SCOPUS:85076220402

SN - 978-1-4503-5639-8

T3 - The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018

SP - 1247

EP - 1256

BT - The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018

A2 - Champin, Pierre-Antoine

A2 - Gandon, Fabien

A2 - Medini, Lionel

PB - Association for Computing Machinery, Inc

Y2 - 23 April 2018 through 27 April 2018

ER -