Deep Query Ranking for Question Answering over Knowledge Bases

  • Hamid ZafarEmail author
  • Giulio Napolitano
  • Jens Lehmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11053)


We study question answering systems over knowledge graphs which map an input natural language question into candidate formal queries. Often, a ranking mechanism is used to discern the queries with higher similarity to the given question. Considering the intrinsic complexity of the natural language, finding the most accurate formal counter-part is a challenging task. In our recent paper [1], we leveraged Tree-LSTM to exploit the syntactical structure of input question as well as the candidate formal queries to compute the similarities. An empirical study shows that taking the structural information of the input question and candidate query into account enhances the performance, when compared to the baseline system. Code related to this paper is available at:



This research was supported by EU H2020 grants for the projects HOBBIT (GA no. 688227), WDAqua (GA no. 642795) as well as by German Federal Ministry of Education and Research (BMBF) for the project SOLIDE (no. 13N14456).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Science InstituteUniversity of BonnBonnGermany
  2. 2.Fraunhofer IAISSankt AugustinGermany

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