Knowledge Graph Question Answering Using Graph-Pattern Isomorphism

Publikation: Beiträge in SammelwerkenKapitelbegutachtet

Authors

  • Daniel Vollmers
  • Rricha Jalota
  • Diego Moussallem
  • Hardik Topiwala
  • Axel-Cyrille Ngonga Ngomo
  • Ricardo Usbeck
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.
OriginalspracheEnglisch
TitelFurther with Knowledge Graphs - Proceedings of the 17th International Conference on Semantic Systems, SEMANTiCS 2017, Amsterdam, The Netherlands, September 6-9, 2021
HerausgeberMehwish Alam, Paul Groth, Victor de Boer, Tassilo Pellegrini, Harshvardhan J. Pandit, Elena Montiel-Ponsoda, Víctor Rodríguez-Doncel, Barbara McGillivray, Albert Meroño-Peñuela
Anzahl der Seiten15
Band53
ErscheinungsortNetherlands
VerlagIOS Press BV
Erscheinungsdatum2021
Seiten103-117
ISBN (Print)978-1-64368-200-6
ISBN (elektronisch)978-1-64368-201-3
DOIs
PublikationsstatusErschienen - 2021
Extern publiziertJa

DOI

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