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BSBM+: Extending BSBM to Evaluate Annotated RDF Features on Graph Databases

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Knowledge Graph and Semantic Computing. Language, Knowledge, and Intelligence (CCKS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 784))

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Abstract

Nowadays, more and more knowledge are published in form of RDF triples enriched with numerous types of annotations such as provenance, temporal and geospatial information. Due to the popularity of the ever-growing annotations, graph databases have proposed various storage engine to store these data and extended their query engine to support queries with these annotation constraints. The developers may be curious about the performance of different engines. Regarding the lack of such a benchmark, we develop the first benchmark for this purpose by extending BSBM (one of the most widely used graph database benchmark). We formalize the annotated RDF into a data model with well-defined categories of annotations and their corresponding operators to be supported. Then we extend the data set of BSBM to allow some triples to be annotated with one or more annotations. We further extend the query set to include annotation constraints in a given query, which can be seen as an extension of SPARQL query. We finally select several popular graph databases for benchmark. The experiment results show for each engine, it performs similarly when queried with different type of annotation constraints. No general database designs a special storage or query plan for a specific type of annotations.

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Notes

  1. 1.

    http://wiki.dbpedia.org/.

  2. 2.

    https://www.freebase.com/.

  3. 3.

    www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/ yago-naga/yago/.

  4. 4.

    We call SPARQL and graph engines which not support aRDF as general SPARQL (gSPARQL) and general graph engines (gGEs) respectively. In the contrast, we call those which support aRDF as extended SPARQL (eSPARQL) and extended graph engines (eGEs). Especially, we summarize these extensions as annotation features.

  5. 5.

    www.strabon.di.uoa.gr.

  6. 6.

    http://franz.com/agraph/allegrograph/.

  7. 7.

    www.opengeospatial.org/.

  8. 8.

    https://jena.apache.org/.

  9. 9.

    https://wiki.blazegraph.com/.

  10. 10.

    www.ecustnlplab.com/papers/BSBM+.pdf.

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Acknowledgements

This work was partially supported by the National Science Foundation of China (No: 61402173) and Open Funding Project of Tianjin Key Laboratory of Cognitive Computing and Application.

Author information

Authors and Affiliations

  1. East China University of Science and Technology, Shanghai, 200237, China

    Le Zhang, Tong Ruan, Yuhang Xia & Qi Wang

  2. Shenzhen Gowild Robotics Co. Ltd., Shenzhen, China

    Haofen Wang

  3. Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, China

    Haofen Wang

  4. North China Sea Marine Technical Support Center, SOA, Qingdao, 266033, China

    Dong Xu

Authors
  1. Le Zhang
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  2. Tong Ruan
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  3. Haofen Wang
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  4. Yuhang Xia
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  5. Qi Wang
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  6. Dong Xu
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Corresponding author

Correspondence to Haofen Wang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Juanzi Li

  2. Beijing Xigema Center, Beijing, China

    Ming Zhou

  3. School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, China

    Guilin Qi

  4. Google, Mountain View, California, USA

    Ni Lao

  5. East China University of Science and Technology, Shanghai, China

    Tong Ruan

  6. Guangdong University of Foreign Studies, Guangzhou, China

    Jianfeng Du

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Zhang, L., Ruan, T., Wang, H., Xia, Y., Wang, Q., Xu, D. (2017). BSBM+: Extending BSBM to Evaluate Annotated RDF Features on Graph Databases. In: Li, J., Zhou, M., Qi, G., Lao, N., Ruan, T., Du, J. (eds) Knowledge Graph and Semantic Computing. Language, Knowledge, and Intelligence. CCKS 2017. Communications in Computer and Information Science, vol 784. Springer, Singapore. https://doi.org/10.1007/978-981-10-7359-5_13

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  • DOI: https://doi.org/10.1007/978-981-10-7359-5_13

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