Template-based Question Answering using Recursive Neural Networks

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages195-198
Number of pages4
ISBN (Electronic)9781728188997
DOIs
StatePublished - Jan 2021
Externally publishedYes
Event15th IEEE International Conference on Semantic Computing, ICSC 2021 - Virtual, Laguna Hills, United States
Duration: Jan 27 2021Jan 29 2021

Publication series

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

Conference

Conference15th IEEE International Conference on Semantic Computing, ICSC 2021
CountryUnited States
CityVirtual, Laguna Hills
Period1/27/211/29/21

Keywords

  • Question Answering
  • Recursive Neural Network

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Decision Sciences (miscellaneous)

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