skip to main content
10.1145/3357384.3358147acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
short-paper

Datalog Reasoning over Compressed RDF Knowledge Bases

Authors Info & Claims
Published:03 November 2019Publication History

ABSTRACT

Materialisation is often used in RDF systems as a preprocessing step to derive all facts implied by given RDF triples and rules. Although widely used, materialisation considers all possible rule applications and can use a lot of memory for storing the derived facts, which can hinder performance. We present a novel materialisation technique that compresses the RDF triples so that the rules can sometimes be applied to multiple facts at once, and the derived facts can be represented using structure sharing. Our technique can thus require less space, as well as skip certain rule applications. Our experiments show that our technique can be very effective: when the rules are relatively simple, our system is both faster and requires less memory than prominent state-of-the-art RDF systems.

References

  1. Daniel J. Abadi. 2008. Query Execution in Column-oriented Database Systems. Ph.D. Dissertation. MIT, Cambridge, MA, USA. AAI0820132.Google ScholarGoogle Scholar
  2. D. J. Abadi, S. Madden, and M. Ferreira. 2006. Integrating Compression and Execution in Column-Oriented Database Systems. In Proc. SIGMOD. 671--682.Google ScholarGoogle Scholar
  3. D. J. Abadi, A. Marcus, S. Madden, and K. Hollenbach. 2009. SW-Store: a vertically partitioned DBMS for Semantic Web data management . VLDB Journal , Vol. 18, 2 (2009), 385--406.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. S. Abiteboul, R. Hull, and V. Vianu. 1995. Foundations of Databases. Addison Wesley.Google ScholarGoogle Scholar
  5. B. Bishop, A. Kiryakov, D. Ognyanoff, I. Peikov, Z. Tashev, and R. Velkov. 2011. OWLIM: A family of scalable semantic repositories . Semantic Web , Vol. 2, 1 (2011), 33--42.Google ScholarGoogle ScholarCross RefCross Ref
  6. D. Croft, A.F. Mundo, R. Haw, M. Milacic, J. Weiser, G. Wu, M. Caudy, P. Garapati, M. Gillespie, M.R. Kamdar, et almbox. 2013. The Reactome pathway knowledgebase. Nucleic acids research , Vol. 42, D1 (2013), D472--D477.Google ScholarGoogle Scholar
  7. B. N. Grosof, I. Horrocks, R. Volz, and S. Decker. 2003. Description Logic Programs: Combining Logic Programs with Description Logic. In Proc. WWW . 48--57.Google ScholarGoogle Scholar
  8. Y. Guo, Z. Pan, and J. Heflin. 2005. LUBM: A benchmark for OWL knowledge base systems . Journal of Web Semantics , Vol. 3, 2--3 (2005), 158--182.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Pan Hu, Jacopo Urbani, Boris Motik, and Ian Horrocks. 2019. Datalog Reasoning over Compressed RDF Knowledge Bases . CoRR , Vol. abs/1908.10177 (2019).Google ScholarGoogle Scholar
  10. S. Idreos, F. Groffen, N. Nes, S. Manegold, K. S. Mullender, and M. L. Kersten. 2012. MonetDB: Two Decades of Research in Column-oriented Database Architectures . IEEE Data Engineering Bulletin , Vol. 35, 1 (2012), 40--45.Google ScholarGoogle Scholar
  11. Graham Klyne, Jeremy J. Carroll, and Brian McBride. 2014. RDF 1.1: Concepts and Abstract Syntax . W3C Recommendation.Google ScholarGoogle Scholar
  12. A. Lamb, M. Fuller, R. Varadarajan, N. Tran, B. Vandier, L. Doshi, and C. Bear. 2012. The Vertica Analytic Database: C-Store 7 Years Later . PVLDB , Vol. 5, 12 (2012), 1790--1801.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Stefan Manegold, Peter A. Boncz, and Niels Nes. 2004. Cache-Conscious Radix-Decluster Projections. In Proc. VLDB. 684--695.Google ScholarGoogle ScholarCross RefCross Ref
  14. B. Motik, Y. Nenov, R. Piro, and I. Horrocks. 2015. Incremental update of datalog materialisation: the backward/forward algorithm. In Proc. AAAI . 1560--1568.Google ScholarGoogle Scholar
  15. B. Motik, Y. Nenov, R. Piro, I. Horrocks, and D. Olteanu. 2014. Parallel Materialisation of Datalog Programs in Centralised, Main-Memory RDF Systems. In Proc. AAAI. 129--137.Google ScholarGoogle Scholar
  16. Sebastian Rahtz, Alexander Dutton, Donna Kurtz, Graham Klyne, Andrew Zisserman, and Relja Arandjelovic. 2011. CLAROS--Collaborating on Delivering the Future of the Past. In Proc. DH . 355--357.Google ScholarGoogle Scholar
  17. Jacopo Urbani, Ceriel J. H. Jacobs, and Markus Krö tzsch. 2016. Column-Oriented Datalog Materialization for Large Knowledge Graphs. In Proc. AAAI . 258--264.Google ScholarGoogle Scholar
  18. Zhe Wu, George Eadon, Souripriya Das, Eugene Inseok Chong, Vladimir Kolovski, Melliyal Annamalai, and Jagannathan Srinivasan. 2008. Implementing an inference engine for RDFS/OWL constructs and user-defined rules in Oracle. In Proc. ICDE. 1239--1248.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Datalog Reasoning over Compressed RDF Knowledge Bases

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management
      November 2019
      3373 pages
      ISBN:9781450369763
      DOI:10.1145/3357384

      Copyright © 2019 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 3 November 2019

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • short-paper

      Acceptance Rates

      CIKM '19 Paper Acceptance Rate202of1,031submissions,20%Overall Acceptance Rate1,861of8,427submissions,22%

      Upcoming Conference

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader