Abstract
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.
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Acknowledgement
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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Wen, Y., Jin, Y., Yuan, X. (2018). KAT: Keywords-to-SPARQL Translation Over RDF Graphs. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_51
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DOI: https://doi.org/10.1007/978-3-319-91452-7_51
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