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RDF dataset profiling - a survey of features, methods, vocabularies and applications

Abstract

The Web of Data, and in particular Linked Data, has seen tremendous growth over the past years. However, reuse and take-up of these rich data sources is often limited and focused on a few well-known and established RDF datasets. This can be partially attributed to the lack of reliable and up-to-date information about the characteristics of available datasets. While RDF datasets vary heavily with respect to the features related to quality, coverage, dynamics and currency, reliable information about such features is essential to enable dataset discovery in tasks such as entity linking, distributed query, search or question answering. Even though there exists a wealth of works contributing to the problem of dataset profiling in general, these works are spread across a wide range of communities. In this survey, we provide a first comprehensive survey of the RDF dataset profile features, methods, tools and vocabularies. We organize these building blocks of dataset profiling in a taxonomy and emphasize the links between the dataset profiling and feature extraction approaches and several application domains. The survey is aimed towards data practitioners, data providers and scientists, spanning a large range of communities and drawing from different fields such as dataset profiling, assessment, summarization and characterization. Ultimately, this work is intended to facilitate the reader to identify and locate the relevant features for building a dataset profile for intended applications together with the tools capable of extracting these features from the data.

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Category:
Articles
Type:
artykuł w czasopiśmie wyróżnionym w JCR
Published in:
Semantic Web no. 9, edition 5, pages 677 - 705,
ISSN: 1570-0844
Language:
English
Publication year:
2018
Bibliographic description:
Ellefi M., Zohra B., Breslin J., Demidova E., Dietze S., Todorov K., Szymański J.: RDF dataset profiling - a survey of features, methods, vocabularies and applications// Semantic Web. -Vol. 9, iss. 5 (2018), s.677-705
DOI:
Digital Object Identifier (open in new tab) 10.3233/sw-180294
Verified by:
Gdańsk University of Technology

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