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Enabling Web-Scale Knowledge Graphs Querying

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The Semantic Web: ESWC 2020 Satellite Events (ESWC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12124))

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Abstract

Knowledge Graphs (KGs) have become an asset for integrating and consuming data from heterogeneous sources. KGs have an influence on several domains such as health-care, manufacturing, transportation and energy. Over the years, the Web of Data has grown significantly. Today, answering complex queries on open KGs is practically impossible due to the SPARQL endpoints availability problem caused by well-known scalability and load balancing issues when hosting Web-size data for concurrent clients. To maintain reliable and responsive open Knowledge Graph query services, several solutions have been proposed: while SPARQL endpoints enforce restrictions on server usage such as imposing limited query execution time or providing partial query results, alternative solutions such as Triple Pattern Fragments (TPF) attempts to tackle the problem of availability by pushing query processing workload to the client-side but suffer from the unnecessary transfer of irrelevant data on complex queries as a result of the large intermediate results. The aim of our research is to develop a new generation of smart clients and servers to balance the load between servers and clients, with the best possible query execution performance, and at the same time reducing data transfer volume, by combining SPARQL endpoints, TPF and shipping compressed KG partitions. The proposed solution shall, on the server-side, offer a suitable query execution service according to the current status of the server workload. On the client-side, we plan research on novel client-side caching mechanisms on the basis of compressed and queryable KG partitions that can be distributed in a modular fashion. In addition, we plan to leverage query logs to optimize the number and the distribution of partitions as well as distributing the query load across a network of collaborative clients.

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Notes

  1. 1.

    https://developers.google.com/knowledge-graph.

  2. 2.

    https://www.microsoft.com/en-us/research/project/knowledge-mining-api/.

  3. 3.

    https://developers.facebook.com/docs/graph-api/.

  4. 4.

    https://www.stardog.com/.

  5. 5.

    https://wiki.dbpedia.org/public-sparql-endpoint.

  6. 6.

    https://ai.wu.ac.at/smartkg.

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Acknowledgments

This work has been supported by the European Union Horizon 2020 research and innovation programme under grant 731601 (SPECIAL) and by the Austrian Research Promotion Agency (FFG) grant no. 861213 (CitySPIN). I thank my doctoral supervisor Prof. Dr. Axel Polleres and my co-authors Dr. Javier D. Fernández and Dr. Maribel Acosta and Martin Beno for their helpful discussions, comments and feedback.

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Authors and Affiliations

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

    Amr Azzam

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  1. Amr Azzam
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Correspondence to Amr Azzam .

Editor information

Editors and Affiliations

  1. University of Erlangen-Nuremberg, Nuremberg, Germany

    Andreas Harth

  2. National Research Council, Rome, Italy

    Valentina Presutti

  3. Eurecom, Sophia Antipolis, France

    Raphaël Troncy

  4. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Maribel Acosta

  5. Institute for Information Business at WU Wien, Vienna, Austria

    Axel Polleres

  6. Institute for Information Business at WU Wien, Vienna, Austria

    Javier D. Fernández

  7. Siemens AG Österreich, Vienna, Austria

    Josiane Xavier Parreira

  8. Linköping University, Linköping, Sweden

    Olaf Hartig

  9. Aalborg University, Aalborg, Denmark

    Katja Hose

  10. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Michael Cochez

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Azzam, A. (2020). Enabling Web-Scale Knowledge Graphs Querying. In: Harth, A., et al. The Semantic Web: ESWC 2020 Satellite Events. ESWC 2020. Lecture Notes in Computer Science(), vol 12124. Springer, Cham. https://doi.org/10.1007/978-3-030-62327-2_38

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  • DOI: https://doi.org/10.1007/978-3-030-62327-2_38

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  • Online ISBN: 978-3-030-62327-2

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