The Role of Ontology Engineering in Linked Data Publishing and Management

Luczak-Rösch, Markus; Simperl, Elena; Stadtmüller, Steffen; Käfer, Tobias

doi:10.4018/ijswis.2014070103

Cited by 2 publications

(2 citation statements)

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“…The authors discussed activities, which theoretically could be subject to crowdsourcing, but did not discuss such aspects explicitly. Similarly, Luczak-Rösch et al [33] mapped ontology engineering methodologies to Linked Data practices, drawing on insights from interviews with practitioners and quantitative analysis. A more focused account of the use of human and crowd intelligence in Linked Data management is offered in the work by Siorpaes and Simperl [49]; the authors investigated several technically oriented scenarios in order to identify lower-level tasks and analyze the extent to which they can be feasibly automated.…”

Section: Using Crowdsourcing In Linked Data Managementmentioning

confidence: 99%

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Detecting Linked Data quality issues via crowdsourcing: A DBpedia study

Acosta

Zaveri²,

Simperl

et al. 2018

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In this paper we examine the use of crowdsourcing as a means to detect Linked Data quality problems that are difficult to uncover automatically. We base our approach on the analysis of the most common errors encountered in the DBpedia dataset, and a classification of these errors according to the extent to which they are likely to be amenable to crowdsourcing. We then propose and study different crowdsourcing approaches to identify these Linked Data quality issues, employing DBpedia as our use case: (i) a contest targeting the Linked Data expert community, and (ii) paid microtasks published on Amazon Mechanical Turk. We secondly focus on adapting the Find-Fix-Verify crowdsourcing pattern to exploit the strengths of experts and lay workers. By testing two distinct Find-Verify workflows (lay users only and experts verified by lay users) we reveal how to best combine different crowds' complementary aptitudes in Linked Data quality issue detection. Empirical results show that a combination of the two styles of crowdsourcing is likely to achieve more effective results than each of them used in isolation, and that human computation is a promising and affordable way to enhance the quality of DBpedia.

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Section: Using Crowdsourcing In Linked Data Managementmentioning

confidence: 99%

“…http://rdfunit.aksw.org33 Schema prefixes as used as defined in Linked Open Vocabularies (http://lov.okfn.org).…”

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Detecting Linked Data quality issues via crowdsourcing: A DBpedia study

Acosta

Zaveri²,

Simperl

et al. 2018

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N-Dimensional Matrix-Based Ontology

Kardan

Jafarpour

2018

International Journal on Semantic Web and Information Systems

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This article describes how the assessment of semantic similarities between word pairs is an important component of understanding text which enables processing, classifying and structuring of textual resources. For this purpose, an ontology is a powerful technique when applied to compute similarity. In this article, the authors propose a novel model to represent an ontology in which an N-dimensional matrix is applied, called an N-dimensional matrix-based ontology. This matrix-based ontology attempts to decrease the time complexity of computation. Second, a new semantic similarity measure is introduced and is performed on the N-dimensional matrix-based ontology. Third, the validation of the result of the N-dimensional matrix-based ontology is compared with related studies comparing two well-known benchmarks. The results reveal that in an N-dimensional matrix-based ontology with increasing N, the accuracy of the proposed semantic similarity measure is increased. Moreover, a matrix-based ontology decreases the time complexity when compared to a graph-based ontology.

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The Role of Ontology Engineering in Linked Data Publishing and Management

Cited by 2 publications

References 26 publications

Detecting Linked Data quality issues via crowdsourcing: A DBpedia study

Detecting Linked Data quality issues via crowdsourcing: A DBpedia study

N-Dimensional Matrix-Based Ontology

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