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Answering why-not questions on SPARQL queries

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Abstract

SPARQL, the W3C standard for RDF query languages, has gained significant popularity in recent years. An increasing amount of effort is currently being exerted to improve the functionality and usability of SPARQL-based search engines. However, explaining missing items in the results of SPARQL queries or the so-called why-not question has not received sufficient attention. In this study, we first formalize why-not questions on SPARQL queries and then propose a novel explanation model, called answering why-not questions on SPARQL (ANNA) to answer why-not questions using a divide-and-conquer strategy. ANNA adopts a graph-based approach and an operator-based approach to generate logical explanations at the triple pattern level and the query operator level, respectively, which helps users refine their initial queries. Extensive experimental results on two real-world RDF datasets show that the proposed model and algorithms can provide high-quality explanations in terms of both effectiveness and efficiency.

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Notes

  1. http://linkeddata.org.

  2. http://wiki.dbpedia.org.

  3. Batman was released in 1989. The Nightmare Before Christmas was written and produced by Tim Burton, but its director was Henry Selick.

  4. A demo system of ANNA has been presented at ISWC 2015.

  5. http://queens.db.toronto.edu/~oktie/linkedmdb/.

  6. http://wiki.dbpedia.org/Datasets, released in September, 2015.

  7. http://www.w3.org/TR/rdf-schema/.

  8. http://kfm.skyclass.net/anna/queryset.html.

  9. http://jena.apache.org.

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Acknowledgements

This work is sponsored by the Fundamental Theory and Applications of Big Data with Knowledge Engineering under the National Key Research and Development Program of China with Grant No. 2016YFB1000903; National Science Foundation of China under Grant Nos. 61721002, 61672419, 61672418, 61532004 and 61532015; MOE Research Center for Online Education Funds under Grant No.2016YB165; Ministry of Education Innovation Research Team No. IRT17R86.

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Authors and Affiliations

  1. MOEKLINNS Lab, Xi’an Jiaotong University, Xi’an, China

    Meng Wang, Jun Liu, Bifan Wei, Siyu Yao, Hongwei Zeng & Lei Shi

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  1. Meng Wang
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  2. Jun Liu
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  3. Bifan Wei
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  4. Siyu Yao
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Correspondence to Meng Wang.

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Wang, M., Liu, J., Wei, B. et al. Answering why-not questions on SPARQL queries. Knowl Inf Syst 58, 169–208 (2019). https://doi.org/10.1007/s10115-018-1155-4

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