KAT: Keywords-to-SPARQL Translation Over RDF Graphs

  • Yanlong Wen
  • Yudong Jin
  • Xiaojie Yuan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)


In this paper, we focus on the problem of translating keywords into SPARQL query effectively and propose a novel approach called KAT. KAT takes into account the context of each input keyword and reduces the ambiguity of input keywords by building a keyword index which contains the class information of keywords in RDF data. To explore RDF data graph efficiently, KAT builds a graph index as well. Moreover, a context aware ranking method is proposed to find the most relevant SPARQL query. Extensive experiments are conducted to show that KAT is both effective and efficient.


Keywords-to-SPARQL Two-facet index Context aware 



This work is supported by National Natural Science Foundation of China (grant No. 61772289) and National 863 Program of China (grant No. 2015AA015401).


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer and Control EngineeringNankai UniversityNankaiPeople’s Republic of China

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