ExpLOD: Summary-Based Exploration of Interlinking and RDF Usage in the Linked Open Data Cloud

Khatchadourian, Shahan; Consens, Mariano P.

doi:10.1007/978-3-642-13489-0_19

Cited by 65 publications

(54 citation statements)

References 23 publications

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“…For example, [9,23,34] propose a graph summarisation approach for optimizing query processing. The authors in [19,33] leverages a summary for data exploration. A summary is used in [3,14] to guide users writing graph queries.…”

Section: Introductionmentioning

confidence: 99%

Efficiency and precision trade-offs in graph summary algorithms

Campinas

Delbru

Tummarello

2013

Proceedings of the 17th International Database Engineering &Amp; Applications Symposium on - IDEAS '13

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In many applications, it is convenient to substitute a large data graph with a smaller homomorphic graph. This paper investigates approaches for summarising massive data graphs. In general, massive data graphs are processed using a shared-nothing infrastructure such as MapReduce. However, accurate graph summarisation algorithms are suboptimal for this kind of environment as they require multiple iterations over the data graph. We investigate approximate graph summarisation algorithms that are efficient to compute in a shared-nothing infrastructure. We define a quality assessment model of a summary with regards to a gold standard summary. We evaluate over several datasets the trade-offs between efficiency and precision of the algorithms. With regards to an application, experiments highlight the need to trade-off the precision and volume of a graph summary with the complexity of a summarisation technique.

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Section: Introductionmentioning

confidence: 99%

Efficiency and precision trade-offs in graph summary algorithms

Campinas

Delbru

Tummarello

2013

Proceedings of the 17th International Database Engineering &Amp; Applications Symposium on - IDEAS '13

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“…[14,15,20,22,25]) and automated schema extraction (e.g. [16,17,21]). Different from these contributions we are interested in largescale bisimulation.…”

Section: Introductionmentioning

confidence: 99%

Large-scale bisimulation of RDF graphs

Schätzle

Neu

Lausen

et al. 2013

Proceedings of the Fifth Workshop on Semantic Web Information Management

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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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“…Some of them use bisimulation and clustering techniques [3,19], whereas others such as [34,37] are based on statistics and topological features. These approaches differ from ours in their main objective: while they are focused on identifying the relevant parts of the dataset, we look for the parts more suited for MD analysis.…”

Section: Related Workmentioning

confidence: 99%

Statistically-driven generation of multidimensional analytical schemas from linked data

Nebot

Berlanga

2016

Knowledge-Based Systems

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The ever-increasing Linked Data (LD) initiative has given place to open, large amounts of semi-structured and rich data published on the Web. However, effective analytical tools that aid the user in his/her analysis and go beyond browsing and querying are still lacking. To address this issue, we propose the automatic generation of multidimensional analytical stars (MDAS). The success of the multidimensional (MD) model for data analysis has been in great part due to its simplicity. Therefore, in this paper we aim at automatically discovering MD conceptual patterns that summarize LD. These patterns resemble the MD star schema typical of relational data warehousing. The underlying foundations of our method is a statistical framework that takes into account both concept and instance data. We present an implementation that makes use of the statistical framework to generate the MDAS. We have performed several experiments that assess and validate the statistical approach with two well-known and large LD sets.

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ExpLOD: Summary-Based Exploration of Interlinking and RDF Usage in the Linked Open Data Cloud

Cited by 65 publications

References 23 publications

Efficiency and precision trade-offs in graph summary algorithms

Efficiency and precision trade-offs in graph summary algorithms

Large-scale bisimulation of RDF graphs

Statistically-driven generation of multidimensional analytical schemas from linked data

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