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Horn-rule based compression technique for RDF data

Published: 13 April 2015 Publication History

Abstract

With the growing number of RDF datasets being published, RDF compression techniques have attracted a lot of research attention. A number of structural compression techniques exist that take into account the structural redundancies such as blank nodes. Few other works focus on providing a compact representation. In our work, we utilize the various semantic associations that can be learned from RDF graphs to compress them. We mine logical Horn rules from the RDF datasets and utilize them for achieving better compression. The technique employed is to store just the triples matching the antecedent part. We delete the triples that match the head part of the rules, as they can be inferred by applying the rules. The experimental evaluation of our approach shows that greater compression can be achieved compared to the existing technique.

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Cited By

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  • (2023)Graph pattern detection and structural redundancy reduction to compress named graphsInformation Sciences10.1016/j.ins.2023.119428(119428)Online publication date: Jul-2023
  • (2022)gRDF: An Efficient Compressor with Reduced Structural Regularities That Utilizes gRePairSensors10.3390/s2207254522:7(2545)Online publication date: 26-Mar-2022
  • (2022)Efficient rule mining and compression for RDF style KB based on Horn rulesThe Journal of Supercomputing10.1007/s11227-022-04519-y78:14(16553-16580)Online publication date: 8-May-2022
  • Show More Cited By

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  1. Horn-rule based compression technique for RDF data

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      cover image ACM Conferences
      SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing
      April 2015
      2418 pages
      ISBN:9781450331968
      DOI:10.1145/2695664
      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]

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      Publication History

      Published: 13 April 2015

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      Author Tags

      1. RDF compression
      2. horn-rule
      3. semantics

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      SAC 2015
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      SAC 2015: Symposium on Applied Computing
      April 13 - 17, 2015
      Salamanca, Spain

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      SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;
      Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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      Cited By

      View all
      • (2023)Graph pattern detection and structural redundancy reduction to compress named graphsInformation Sciences10.1016/j.ins.2023.119428(119428)Online publication date: Jul-2023
      • (2022)gRDF: An Efficient Compressor with Reduced Structural Regularities That Utilizes gRePairSensors10.3390/s2207254522:7(2545)Online publication date: 26-Mar-2022
      • (2022)Efficient rule mining and compression for RDF style KB based on Horn rulesThe Journal of Supercomputing10.1007/s11227-022-04519-y78:14(16553-16580)Online publication date: 8-May-2022
      • (2021)Applying Grammar-Based Compression to RDFThe Semantic Web10.1007/978-3-030-77385-4_6(93-108)Online publication date: 31-May-2021

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