Automatic Creation and Analysis of a Linked Data Cloud Diagram

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• 关键词：Linked data cloud analysis ; Automatic clustering ; Domain identification ; Community detection algorithms
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：10041
• 期：1
• 页码：417-432
• 全文大小：519 KB
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• 作者单位：Alexander Arturo Mera Caraballo (19)
  Bernardo Pereira Nunes (19) (22)
  Giseli Rabello Lopes (20)
  Luiz André Portes Paes Leme (21)
  Marco Antonio Casanova (19)
  
  19. Department of Informatics, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil
  22. Federal University of the State of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil
  20. Federal University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil
  21. Fluminense Federal University, Niterói, Rio de Janeiro, Brazil
  
• 丛书名：Web Information Systems Engineering ¨C WISE 2016
• ISBN：978-3-319-48740-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：10041

文摘

Datasets published on the Web and following the Linked Open Data (LOD) practices have the potential to enrich other LOD datasets in multiple domains. However, the lack of descriptive information, combined with the large number of available LOD datasets, inhibits their interlinking and consumption. Aiming at facilitating such tasks, this paper proposes an automated clustering process for the LOD datasets that, thereby, provide an up-to-date description of the LOD cloud. The process combines metadata inspection and extraction strategies, community detection methods and dataset profiling techniques. The clustering process is evaluated using the LOD diagram as ground truth. The results show the ability of the proposed process to replicate the LOD diagram and to identify new LOD dataset clusters. Finally, experiments conducted by LOD experts indicate that the clustering process generates dataset clusters that tend to be more descriptive than those manually defined in the LOD diagram.