G-Diff: A Grouping Algorithm for RDF Change Detection on MapReduce

Ahn, Jinhyun; Im, Dong-Hyuk; Eom, Jae-Hong; Zong, Nansu; Kim, Hong-Gee

doi:10.1007/978-3-319-15615-6_17

Cited by 7 publications

(8 citation statements)

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“…Various directions are worth further research, including the identification of special cases where optimality is guaranteed, and methods for parallelizing the task of bnode matching in the context of a parallel matching or differential algorithm [1,15]. , where b is the adjacent node of b through the triple t i (line 6) and l b (t i ) is the label of t i in terms of b, as defined in Definition 5.…”

Section: Discussionmentioning

confidence: 99%

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Radius-aware approximate blank node matching using signatures

et al. 2016

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In the linked open data cloud, the biggest open data graph that currently exists, a remarkable percentage of data are unnamed resources, also called blank nodes. Several fundamental tasks, such as graph isomorphism checking and RDF data versioning, require computing a map between the sets of blank nodes of two graphs. This map aims at minimizing the delta size, i.e. the number of change operations that are required to make the graphs isomorphic. Computing the optimal map is NP-Hard in the general case, and various approximation algorithms have been proposed. In this work, we propose a novel radius-aware signature-based algorithm that is not restricted to the direct neighborhood of the compared blank nodes. Contrary to the older algorithms, the proposed algorithm manages to decrease the deviation from the optimal solution even for graphs that contain connected blank nodes in large and dense structures. The conducted experiments over real and synthetically generated datasets (including datasets from the Billion Triple Challenge 2012 and 2014) show the significantly smaller deltas. For isomorphism checking (simple RDF equivalence), with a wise configuration of radius, the proposed algorithm achieves optimality for 100 % of the datasets, while in non-isomorphic datasets the deltas are on average 50-75 % smaller than those of the previous algorithms. Finally, the trade-off between radius, deviation from the optimum and time efficiency is analyzed.

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Section: Discussionmentioning

confidence: 99%

“…Without loss of generality, let us assume that the values of these parameters are common for both G 1 …”

Section: Complexitymentioning

confidence: 99%

Radius-aware approximate blank node matching using signatures

et al. 2016

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“…Furthermore, RDF data changes over time. Handling updated RDF data will be considered in terms of change detection [32]- [34] and version management framework [35]. In this paper, we only consider subclassof and instanceof predicates.…”

Section: Discussionmentioning

confidence: 99%

Efficient Access Control of Large Scale RDF Data Using Prefix-Based Labeling

Ahn

2020

IEEE Access

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A massive amount of resource description framework (RDF) data are available on the web. An RDF data publisher may want to prevent a few users from accessing a certain part of the RDF data. Various approaches have been proposed to reject a given SPARQL query that is intended to access instances or classes that are required to be protected. The problem of such access control management can be cast to processing ancestor/descendant relationship query over class hierarchy. The prefix-based labeling scheme has been applied to the fast processing of ancestor/descendant relationship queries. However, we observed that the existing approaches are ineffective in dealing with massive amounts of RDF data because the adopted labeling schemes produce labels of large sizes. Hence, we adopted the state-of-the art MapReducebased algorithm for prefix-based labeling to reduce the label size based on the structural information of RDF data. Experiments with real-world RDF datasets showed that the proposed approach is more efficient than the conventional methods.

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“…This is straightforward for IRIs and literals, but hard for blank nodes, as they are only characterized through the RDF triples they participate in and do not have identifiers across RDF graphs. In general, finding a mapping between the blank nodes of two RDF graphs that minimizes the set difference is NP-hard [34,18,1,13]. For Wikidated 1.0, we circumvent this issue (see Sect.…”

Section: Rdfmentioning

confidence: 99%

Wikidated 1.0: An Evolving Knowledge Graph Dataset of Wikidata's Revision History

Schmelzeisen¹,

Dima²,

Staab³

2021

Preprint

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Wikidata is the largest general-interest knowledge base that is openly available. It is collaboratively edited by thousands of volunteer editors and has thus evolved considerably since its inception in 2012. In this paper, we present Wikidated 1.0 1 , a dataset of Wikidata's full revision history, which encodes changes between Wikidata revisions as sets of deletions and additions of RDF triples. To the best of our knowledge, it constitutes the first large dataset of an evolving knowledge graph, a recently emerging research subject in the Semantic Web community. We introduce the methodology for generating Wikidated 1.0 from dumps of Wikidata, discuss its implementation and limitations, and present statistical characteristics of the dataset.

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G-Diff: A Grouping Algorithm for RDF Change Detection on MapReduce

Cited by 7 publications

References 3 publications

Radius-aware approximate blank node matching using signatures

Radius-aware approximate blank node matching using signatures

Efficient Access Control of Large Scale RDF Data Using Prefix-Based Labeling

Wikidated 1.0: An Evolving Knowledge Graph Dataset of Wikidata's Revision History

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