Template-based Question Answering using Recursive Neural Networks

Ram G. Athreya; Srividya K. Bansal; Axel Cyrille Ngonga Ngomo; Ricardo Usbeck

doi:10.48550/arXiv.2004.13843

Template-based Question Answering using Recursive Neural Networks

Ram G. Athreya
, Srividya K. Bansal
, Axel Cyrille Ngonga Ngomo
, Ricardo Usbeck

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › Begutachtung

29 Zitate (Scopus)

Abstract

Most question answering (QA) systems over Linked Data, i.e. Knowledge Graphs, approach the question answering task as a conversion from a natural language question to its corresponding SPARQL query. A common approach is to use query templates to generate SPARQL queries with slots that need to be filled. Using templates instead of running an extensive NLP pipeline or end-to-end model shifts the QA problem into a classification task, where the system needs to match the input question to the appropriate template. This paper presents an approach to automatically learn and classify natural language questions into corresponding templates using recursive neural networks. Our model was trained on 5000 questions and their respective SPARQL queries from the preexisting LC-QuAD dataset grounded in DBpedia, spanning 5042 entities and 615 predicates. The resulting model was evaluated using the FAIR GERBIL QA framework resulting in 0.419 macro f-measure on LC-QuAD and 0.417 macro f-measure on QALD-7.

Originalsprache	Englisch
Titel	Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021
Seitenumfang	4
Herausgeber (Verlag)	Institute of Electrical and Electronics Engineers Inc.
Erscheinungsdatum	01.2021
Seiten	195-198
Aufsatznummer	9364639
ISBN (Print)	978-1-7281-8900-0
ISBN (elektronisch)	978-1-7281-8899-7
DOIs	https://doi.org/10.48550/arXiv.2004.13843 https://doi.org/10.1109/ICSC50631.2021.00041
Publikationsstatus	Erschienen - 01.2021
Extern publiziert	Ja
Veranstaltung	15th IEEE International Conference on Semantic Computing - ICSC 2021 - Laguna Hills, California, USA, Laguna Hills, USA / Vereinigte Staaten Dauer: 27.01.2021 → 29.01.2021 Konferenznummer: 15 https://www.ieee-icsc.org/ (Offizielle Event-Webseite) https://semanticcomputing.wixsite.com/icsc2021 https://doi.org/10.25798/2x72-ga16 https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9364351 (Proceedings URL) https://doi.org/10.1109/icsc50631.2021 (DOI)

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Athreya, R. G., Bansal, S. K., Ngomo, A. C. N., & Usbeck, R. (2021). Template-based Question Answering using Recursive Neural Networks. in Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021 (S. 195-198). Artikel 9364639 (Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.48550/arXiv.2004.13843, https://doi.org/10.1109/ICSC50631.2021.00041

Athreya, Ram G. ; Bansal, Srividya K. ; Ngomo, Axel Cyrille Ngonga et al. / Template-based Question Answering using Recursive Neural Networks. Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. S. 195-198 (Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021).

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abstract = "Most question answering (QA) systems over Linked Data, i.e. Knowledge Graphs, approach the question answering task as a conversion from a natural language question to its corresponding SPARQL query. A common approach is to use query templates to generate SPARQL queries with slots that need to be filled. Using templates instead of running an extensive NLP pipeline or end-to-end model shifts the QA problem into a classification task, where the system needs to match the input question to the appropriate template. This paper presents an approach to automatically learn and classify natural language questions into corresponding templates using recursive neural networks. Our model was trained on 5000 questions and their respective SPARQL queries from the preexisting LC-QuAD dataset grounded in DBpedia, spanning 5042 entities and 615 predicates. The resulting model was evaluated using the FAIR GERBIL QA framework resulting in 0.419 macro f-measure on LC-QuAD and 0.417 macro f-measure on QALD-7.",

keywords = "Question Answering, Recursive Neural Network, Informatics, Business informatics",

author = "Athreya, \{Ram G.\} and Bansal, \{Srividya K.\} and Ngomo, \{Axel Cyrille Ngonga\} and Ricardo Usbeck",

note = "Funding Information: This paper presents a novel approach for the QA over Linked Data task by converting it into a template classification task followed by a slot filling task. Although earlier template-based approaches have attempted similar solutions, this was the first time (to the best of our knowledge) that recursive neural networks were applied to the template classification task. For completeness, a slot filling approach using an ensemble of the best components for named entity, predicate and class recognition tasks were presented. Our evaluation showed that state-of-the-art neural network techniques such as Long Short Term Memory (LSTM), recursive neural networks, and word embeddings be leveraged for the template classification task. We are aware that our approach has a coverage issue in terms of being bound to the training templates and look forward to mitigating this issue through a finer-grained training process. Acknowledgements. We acknowledge the support of the Federal Ministry for Economic Affairs and Energy (BMWi) project SPEAKER (FKZ 01MK20011A). Publisher Copyright: {\textcopyright} 2021 IEEE.; 15th IEEE International Conference on Semantic Computing - ICSC 2021, ICSC 2021 ; Conference date: 27-01-2021 Through 29-01-2021",

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Athreya, RG, Bansal, SK, Ngomo, ACN & Usbeck, R 2021, Template-based Question Answering using Recursive Neural Networks. in Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021., 9364639, Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021, Institute of Electrical and Electronics Engineers Inc., S. 195-198, 15th IEEE International Conference on Semantic Computing - ICSC 2021, Laguna Hills, California, USA / Vereinigte Staaten, 27.01.21. https://doi.org/10.48550/arXiv.2004.13843, https://doi.org/10.1109/ICSC50631.2021.00041

Template-based Question Answering using Recursive Neural Networks. / Athreya, Ram G.; Bansal, Srividya K.; Ngomo, Axel Cyrille Ngonga et al.
Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. S. 195-198 9364639 (Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021).

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › Begutachtung

TY - CHAP

T1 - Template-based Question Answering using Recursive Neural Networks

AU - Athreya, Ram G.

AU - Bansal, Srividya K.

AU - Ngomo, Axel Cyrille Ngonga

AU - Usbeck, Ricardo

N1 - Conference code: 15

PY - 2021/1

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N2 - Most question answering (QA) systems over Linked Data, i.e. Knowledge Graphs, approach the question answering task as a conversion from a natural language question to its corresponding SPARQL query. A common approach is to use query templates to generate SPARQL queries with slots that need to be filled. Using templates instead of running an extensive NLP pipeline or end-to-end model shifts the QA problem into a classification task, where the system needs to match the input question to the appropriate template. This paper presents an approach to automatically learn and classify natural language questions into corresponding templates using recursive neural networks. Our model was trained on 5000 questions and their respective SPARQL queries from the preexisting LC-QuAD dataset grounded in DBpedia, spanning 5042 entities and 615 predicates. The resulting model was evaluated using the FAIR GERBIL QA framework resulting in 0.419 macro f-measure on LC-QuAD and 0.417 macro f-measure on QALD-7.

AB - Most question answering (QA) systems over Linked Data, i.e. Knowledge Graphs, approach the question answering task as a conversion from a natural language question to its corresponding SPARQL query. A common approach is to use query templates to generate SPARQL queries with slots that need to be filled. Using templates instead of running an extensive NLP pipeline or end-to-end model shifts the QA problem into a classification task, where the system needs to match the input question to the appropriate template. This paper presents an approach to automatically learn and classify natural language questions into corresponding templates using recursive neural networks. Our model was trained on 5000 questions and their respective SPARQL queries from the preexisting LC-QuAD dataset grounded in DBpedia, spanning 5042 entities and 615 predicates. The resulting model was evaluated using the FAIR GERBIL QA framework resulting in 0.419 macro f-measure on LC-QuAD and 0.417 macro f-measure on QALD-7.

KW - Question Answering

KW - Recursive Neural Network

KW - Informatics

KW - Business informatics

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U2 - 10.48550/arXiv.2004.13843

DO - 10.48550/arXiv.2004.13843

M3 - Article in conference proceedings

AN - SCOPUS:85102617112

SN - 978-1-7281-8900-0

T3 - Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021

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BT - Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th IEEE International Conference on Semantic Computing - ICSC 2021

Y2 - 27 January 2021 through 29 January 2021

ER -

Athreya RG, Bansal SK, Ngomo ACN, Usbeck R. Template-based Question Answering using Recursive Neural Networks. in Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. S. 195-198. 9364639. (Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021). doi: 10.48550/arXiv.2004.13843, 10.1109/ICSC50631.2021.00041

Template-based Question Answering using Recursive Neural Networks

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