Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Sharing Linked Open Data over Peer-to-Peer Distributed File Systems: The Case of IPFS Read chapter Read first chapter

2016 | OriginalPaper | Chapter

DSCrank: A Method for Selection and Ranking of Datasets

Authors : Yasmmin Cortes Martins, Fábio Faria da Mota, Maria Cláudia Cavalcanti

Published in: Metadata and Semantics Research

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Considerable efforts have been made to build the Web of Data. One of the main challenges has to do with how to identify the most related datasets to connect to. Another challenge is to publish a local dataset into the Web of Data, following the Linked Data principles. The present work is based on the idea that a set of activities should guide the user on the publication of a new dataset into the Web of Data. It presents the specification and implementation of two initial activities, which correspond to the crawling and ranking of a selected set of existing published datasets. The proposed implementation is based on the focused crawling approach, adapting it to address the Linked Data principles. Moreover, the dataset ranking is based on a quick glimpse into the content of the selected datasets. Additionally, the paper presents a case study in the Biomedical area to validate the implemented approach, and it shows promising results with respect to scalability and performance.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter The Metadata Ecosystem of DataID
next chapter WDFed: Exploiting Cloud Databases Using Metadata and RESTful APIs
Footnotes
14
Ratio between relevant datasets retrieved and the number of top ranked datasets.
 
Literature
1.
Bizer, C., Heath, T., Berners-Lee, T.: Linked data - the story so far. Int. J. Semant. Web Inf. Syst. 5(3), 1–22 (2009)CrossRef
2.
Caliskan, K., Ozcan, R.: Comparing classification methods for link context based focused crawlers. In: 2013 International Conference on Electronics, Computer and Computation (ICECCO), pp. 143–146, November 2013
3.
Hausenblas, M.: Exploiting linked data to build web applications. IEEE Internet Comput. 13(4), 68–73 (2009). Accessed 01 May 2016MathSciNetCrossRef
4.
Hull, D.A.: Stemming algorithms: a case study for detailed evaluation. J. Am. Soc. Inf. Sci. (JASIS) 47(1), 70–84 (1996)CrossRef
5.
Leme, L.A.P.P., Lopes, G.R., Nunes, B.P., Casanova, M.A., Dietze, S.: Identifying candidate datasets for data interlinking. In: Daniel, F., Dolog, P., Li, Q. (eds.) ICWE 2013. LNCS, vol. 7977, pp. 354–366. Springer, Heidelberg (2013). doi:10.​1007/​978-3-642-39200-9_​29 CrossRef
6.
Manning, C.D., Raghavan, P., Schtze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)CrossRefMATH
7.
Nikolov, A., d’Aquin, M., Motta, E.: What should i link to? identifying relevant sources and classes for data linking. In: Pan, J.Z., Chen, H., Kim, H.-G., Li, J., Horrocks, I., Mizoguchi, R., Wu, Z., Wu, Z. (eds.) JIST 2011. LNCS, vol. 7185, pp. 284–299. Springer, Heidelberg (2012). doi:10.​1007/​978-3-642-29923-0_​19 CrossRef
8.
de Oliveira, H.R., Tavares, A.T., Lóscio, B.F.: Feedback-based data set recommendation for building linked data applications. In: International Conference on Semantic Systems, I-SEMANTICS 2012, pp. 49–55. ACM, New York (2012)
9.
Raman, S., Chaurasiya, V., Venkatesan, S.: Performance comparison of various information retrieval models used in search engines. In: International Conference on Communication, Information Computing Technology (ICCICT), pp. 1–4 (2012)
10.
Salvadores, M., Alexander, P.R., Musen, M.A., Noy, N.F.: Bioportal as a dataset of linked biomedical ontologies and terminologies in RDF. Semant. Web 4(3), 277–284 (2013)
11.
Studer, R., Benjamins, V.R., Fensel, D.: Knowledge engineering: principles and methods. Data Knowl. Eng. 25(1–2), 161–197 (1998)CrossRefMATH
12.
Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, pp. 173–180. Association for Computational Linguistics, Stroudsburg (2003)
13.
Zaveri, A., Rula, A., Maurino, A., Pietrobon, R., Lehmann, J., Auer, S.: Quality assessment for linked data: a survey. Semant. Web 7(1), 63–93 (2016)CrossRef
Metadata
Title
DSCrank: A Method for Selection and Ranking of Datasets
Authors
Yasmmin Cortes Martins
Fábio Faria da Mota
Maria Cláudia Cavalcanti
Copyright Year
2016
Book
Metadata and Semantics Research

Print ISBN: 978-3-319-49156-1

Electronic ISBN: 978-3-319-49157-8

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-49157-8_29