research-article

Large-scale bisimulation of RDF graphs

Authors:

Alexander Schätzle,

Martin Przyjaciel-ZablockiAuthors Info & Claims

SWIM '13: Proceedings of the Fifth Workshop on Semantic Web Information Management

Article No.: 1, Pages 1 - 8

https://doi.org/10.1145/2484712.2484713

Published: 23 June 2013 Publication History

Abstract

RDF datasets with billions of triples are no longer unusual and continue to grow constantly (e.g. LOD cloud) driven by the inherent flexibility of RDF that allows to represent very diverse datasets, ranging from highly structured to unstructured data. Because of their size, understanding and processing RDF graphs is often a difficult task and methods to reduce the size while keeping as much of its structural information become attractive. In this paper we study bisimulation as a means to reduce the size of RDF graphs according to structural equivalence. We study two bisimulation algorithms, one for sequential execution using SQL and one for distributed execution using MapReduce. We demonstrate that the MapReduce-based implementation scales linearly with the number of the RDF triples, allowing to compute the bisimulation of very large RDF graphs within a time which is by far not possible for the sequential version. Experiments based on synthetic benchmark data and real data (DBPedia) exhibit a reduction of more than 90% of the size of the RDF graph in terms of the number of nodes to the number of blocks in the resulting bisimulation partition.

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Cited By

Javed HKhan KCheema MAlgarni APark J(2024)Instance-Based Lossless Summarization of Knowledge Graph With Optimized Triples and Corrections (IBA-OTC)IEEE Access10.1109/ACCESS.2023.334098412(5584-5604)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2023.3340984
Beldi ASassi SChbeir RJemai A(2024)HERSE: Handling and Enhancing RDF Summarization Through Blank Node EliminationFoundations of Intelligent Systems10.1007/978-3-031-62700-2_9(87-101)Online publication date: 17-Jun-2024
https://doi.org/10.1007/978-3-031-62700-2_9
Beldi ARicha JSassi SChbeir RJemai A(2023)A Novel Approach for Extracting Summarized RDF Graph from Heterogeneous Corpus2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA)10.1109/INISTA59065.2023.10310645(1-7)Online publication date: 20-Sep-2023
https://doi.org/10.1109/INISTA59065.2023.10310645
Show More Cited By

Index Terms

Large-scale bisimulation of RDF graphs
1. Information systems
  1. Information retrieval
    1. Document representation
  2. Information systems applications
2. Theory of computation
  1. Semantics and reasoning
    1. Program reasoning
      1. Abstraction

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Published In

cover image ACM Conferences

SWIM '13: Proceedings of the Fifth Workshop on Semantic Web Information Management

June 2013

50 pages

ISBN:9781450321945

DOI:10.1145/2484712

General Chairs:
Roberto De Virgilio
Universitá Roma Tre, Italy
,
Fausto Giunchiglia
Universitá di Trento, Italy
,
Letizia Tanca
Politecnico di Milano, Italy

Copyright © 2013 ACM.

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Publication History

Published: 23 June 2013

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SIGMOD/PODS'13: International Conference on Management of Data

June 23, 2013

New York, New York

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Cited By

Javed HKhan KCheema MAlgarni APark J(2024)Instance-Based Lossless Summarization of Knowledge Graph With Optimized Triples and Corrections (IBA-OTC)IEEE Access10.1109/ACCESS.2023.334098412(5584-5604)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2023.3340984
Beldi ASassi SChbeir RJemai A(2024)HERSE: Handling and Enhancing RDF Summarization Through Blank Node EliminationFoundations of Intelligent Systems10.1007/978-3-031-62700-2_9(87-101)Online publication date: 17-Jun-2024
https://doi.org/10.1007/978-3-031-62700-2_9
Beldi ARicha JSassi SChbeir RJemai A(2023)A Novel Approach for Extracting Summarized RDF Graph from Heterogeneous Corpus2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA)10.1109/INISTA59065.2023.10310645(1-7)Online publication date: 20-Sep-2023
https://doi.org/10.1109/INISTA59065.2023.10310645
Rau JRicherby DScherp A(2023)Computing k-Bisimulations for Large Graphs: A Comparison and Efficiency AnalysisGraph Transformation10.1007/978-3-031-36709-0_12(223-242)Online publication date: 12-Jul-2023
https://doi.org/10.1007/978-3-031-36709-0_12
Chen XLai LQin LLin XLiu B(2021)A Framework to Quantify Approximate Simulation on Graph Data2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00117(1308-1319)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00117
Blume TRicherby DScherp A(2021)FLUID: A common model for semantic structural graph summaries based on equivalence relationsTheoretical Computer Science10.1016/j.tcs.2020.12.019854(136-158)Online publication date: Jan-2021
https://doi.org/10.1016/j.tcs.2020.12.019
Kellou-Menouer KKardoulakis NTroullinou GKedad ZPlexousakis DKondylakis H(2021)A survey on semantic schema discoveryThe VLDB Journal10.1007/s00778-021-00717-x31:4(675-710)Online publication date: 27-Nov-2021
https://doi.org/10.1007/s00778-021-00717-x
Alva Principe RMaurino APalmonari MCiavotta MSpahiu B(2021)ABSTAT-HD: a scalable tool for profiling very large knowledge graphsThe VLDB Journal10.1007/s00778-021-00704-231:5(851-876)Online publication date: 29-Sep-2021
https://doi.org/10.1007/s00778-021-00704-2
Goasdoué FGuzewicz PManolescu I(2020)RDF graph summarization for first-sight structure discoveryThe VLDB Journal10.1007/s00778-020-00611-y29:5(1191-1218)Online publication date: 30-Apr-2020
https://doi.org/10.1007/s00778-020-00611-y
Zneika MVodislav DKotzinos DThakker DSchwabe DGarcía RKozaki KBrambilla MDimitrova V(2019)Quality metrics for RDF graph summarizationSemantic Web10.3233/SW-19034610:3(555-584)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.3233/SW-190346
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