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Revealing Secrets in SPARQL Session Level

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The Semantic Web – ISWC 2020 (ISWC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12506))

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Abstract

Based on Semantic Web technologies, knowledge graphs help users to discover information of interest by using live SPARQL services. Answer-seekers often examine intermediate results iteratively and modify SPARQL queries repeatedly in a search session. In this context, understanding user behaviors is critical for effective intention prediction and query optimization. However, these behaviors have not yet been researched systematically at the SPARQL session level. This paper reveals secrets of session-level user search behaviors by conducting a comprehensive investigation over massive real-world SPARQL query logs. In particular, we thoroughly assess query changes made by users w.r.t. structural and data-driven features of SPARQL queries. To illustrate the potentiality of our findings, we employ an application example of how to use our findings, which might be valuable to devise efficient SPARQL caching, auto-completion, query suggestion, approximation, and relaxation techniques in the future (Code and data are available at: https://github.com/seu-kse/SparqlSession.).

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Notes

  1. 1.

    https://sparqles.ai.wu.ac.at/availability, accessed on 2020/10/24 17:39:51.

  2. 2.

    A query can be executed multiple times on the same dataset.

  3. 3.

    We remove the queries with parse errors and the contiguous same queries before the recognition. For example, \(q_1, q_2, q_2, q_3\) is processed into \(q_1, q_2, q_3\).

  4. 4.

    We use randomly selected sessions in DBpedia because the GED computation on such large-scale data is NP-hard and time-consuming.

  5. 5.

    We append a 1 to vectors to avoid all-zero vectors.

  6. 6.

    Features in this vector can also be extended to more dimensions or features.

  7. 7.

    The query representation method can be replaced by other distributed representations such as trained embedding. We do not use embedding here because training embeddings for 10 datasets is highly resource and time-consuming.

  8. 8.

    We eliminate the processing error state here.

  9. 9.

    https://www.w3.org/TR/sparql11-query/#scopeFilters.

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Acknowledgements

This work has been supported by by National Natural Science Foundation of China with Grant Nos. 61906037 and U1736204; the German Federal Ministry for Economic Affairs and Energy (BMWi) within the project RAKI under the grant no 01MD19012D, by the German Federal Ministry of Education and Research (BMBF) within the project DAIKIRI under the grant no 01IS19085B, and by the EU H2020 Marie Skłodowska-Curie project KnowGraphs under the grant agreement no 860801; National Key Research and Development Program of China with Grant Nos. 2018YFC0830201 and 2017YFB1002801; the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. Southeast University, Nanjing, China

    Xinyue Zhang, Meng Wang & Guilin Qi

  2. Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China

    Meng Wang & Guilin Qi

  3. AKSW, Leipzig University, Leipzig, Germany

    Muhammad Saleem

  4. University of Paderborn, Paderborn, Germany

    Axel-Cyrille Ngonga Ngomo

  5. Intelligent Big Data Visualization Lab, Tongji University, Shanghai, China

    Haofen Wang

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  1. Xinyue Zhang
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  2. Meng Wang
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  3. Muhammad Saleem
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  4. Axel-Cyrille Ngonga Ngomo
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  5. Guilin Qi
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  6. Haofen Wang
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Corresponding author

Correspondence to Meng Wang .

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Editors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Jeff Z. Pan

  2. University of Liverpool, Liverpool, UK

    Valentina Tamma

  3. University of Bari, Bari, Italy

    Claudia d’Amato

  4. University of California, Santa Barbara, Santa Barbara, CA, USA

    Krzysztof Janowicz

  5. California State University, Long Beach, Long Beach, CA, USA

    Bo Fu

  6. Vienna University of Economics and Business, Vienna, Austria

    Axel Polleres

  7. Rensselaer Polytechnic Institute, Troy, NY, USA

    Oshani Seneviratne

  8. Massachusetts Institute of Technology, Cambridge, MA, USA

    Lalana Kagal

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Zhang, X., Wang, M., Saleem, M., Ngomo, AC.N., Qi, G., Wang, H. (2020). Revealing Secrets in SPARQL Session Level. In: Pan, J.Z., et al. The Semantic Web – ISWC 2020. ISWC 2020. Lecture Notes in Computer Science(), vol 12506. Springer, Cham. https://doi.org/10.1007/978-3-030-62419-4_38

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  • DOI: https://doi.org/10.1007/978-3-030-62419-4_38

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