Knowledge Graph Question Answering Using Graph-Pattern Isomorphism
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Further with Knowledge Graphs - Proceedings of the 17th International Conference on Semantic Systems, SEMANTiCS 2017, Amsterdam, The Netherlands, September 6-9, 2021. ed. / Mehwish Alam; Paul Groth; Victor de Boer; Tassilo Pellegrini; Harshvardhan J. Pandit; Elena Montiel-Ponsoda; Víctor Rodríguez-Doncel; Barbara McGillivray; Albert Meroño-Peñuela. Vol. 53 Netherlands: IOS Press BV, 2021. p. 103-117 (Studies on the Semantic Web; Vol. 53).
Research output: Contributions to collected editions/works › Chapter › peer-review
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TY - CHAP
T1 - Knowledge Graph Question Answering Using Graph-Pattern Isomorphism
AU - Vollmers, Daniel
AU - Jalota, Rricha
AU - Moussallem, Diego
AU - Topiwala, Hardik
AU - Ngomo, Axel-Cyrille Ngonga
AU - Usbeck, Ricardo
PY - 2021
Y1 - 2021
N2 - Knowledge Graph Question Answering (KGQA) systems are often based on machine learning algorithms, requiring thousands of question-answer pairs as training examples or natural language processing pipelines that need module fine-tuning. In this paper, we present a novel QA approach, dubbed TeBaQA. Our approach learns to answer questions based on graph isomorphisms from basic graph patterns of SPARQL queries. Learning basic graph patterns is efficient due to the small number of possible patterns. This novel paradigm reduces the amount of training data necessary to achieve state-of-the-art performance. TeBaQA also speeds up the domain adaption process by transforming the QA system development task into a much smaller and easier data compilation task. In our evaluation, TeBaQA achieves state-of-the-art performance on QALD-8 and delivers comparable results on QALD-9 and LC-QuAD v1. Additionally, we performed a fine-grained evaluation on complex queries that deal with aggregation and superlative questions as well as an ablation study, highlighting future research challenges.
AB - Knowledge Graph Question Answering (KGQA) systems are often based on machine learning algorithms, requiring thousands of question-answer pairs as training examples or natural language processing pipelines that need module fine-tuning. In this paper, we present a novel QA approach, dubbed TeBaQA. Our approach learns to answer questions based on graph isomorphisms from basic graph patterns of SPARQL queries. Learning basic graph patterns is efficient due to the small number of possible patterns. This novel paradigm reduces the amount of training data necessary to achieve state-of-the-art performance. TeBaQA also speeds up the domain adaption process by transforming the QA system development task into a much smaller and easier data compilation task. In our evaluation, TeBaQA achieves state-of-the-art performance on QALD-8 and delivers comparable results on QALD-9 and LC-QuAD v1. Additionally, we performed a fine-grained evaluation on complex queries that deal with aggregation and superlative questions as well as an ablation study, highlighting future research challenges.
KW - Informatics
KW - question answering
KW - Basic Graph Pattern
KW - isomorphism
KW - QUALD
UR - https://www.mendeley.com/catalogue/378e5114-69cf-31c9-b426-a8790999cd12/
U2 - 10.48550/arXiv.2103.06752
DO - 10.48550/arXiv.2103.06752
M3 - Chapter
SN - 978-1-64368-200-6
VL - 53
T3 - Studies on the Semantic Web
SP - 103
EP - 117
BT - Further with Knowledge Graphs - Proceedings of the 17th International Conference on Semantic Systems, SEMANTiCS 2017, Amsterdam, The Netherlands, September 6-9, 2021
A2 - Alam, Mehwish
A2 - Groth, Paul
A2 - Boer, Victor de
A2 - Pellegrini, Tassilo
A2 - Pandit, Harshvardhan J.
A2 - Montiel-Ponsoda, Elena
A2 - Rodríguez-Doncel, Víctor
A2 - McGillivray, Barbara
A2 - Meroño-Peñuela, Albert
PB - IOS Press BV
CY - Netherlands
ER -