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A Linked Data Approach to Know-How

  • Paolo Pareti
  • Benoit Testu
  • Ryutaro Ichise
  • Ewan Klein
  • Adam Barker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8982)

Abstract

The Web is one of the major repositories of human generated know-how, such as step-by-step videos and instructions. This knowledge can be potentially reused in a wide variety of applications, but it currently suffers from a lack of structure and isolation from related knowledge. To overcome these challenges we have developed a Linked Data framework which can automate the extraction of know-how from existing Web resources and generate links to related knowledge on the Linked Data Cloud. We have implemented our framework and used it to extract a Linked Data representation of two of the largest know-how repositories on the Web. We demonstrate two possible uses of the resulting dataset of real-world know-how. Firstly, we use this dataset within a Web application to offer an integrated visualization of distributed know-how resources. Lastly, we show the potential of this dataset for inferring common sense knowledge about tasks.

Keywords

Link Data Procedural Knowledge Related Knowledge Knowledge Extraction Integration Experiment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Paolo Pareti
    • 1
    • 2
  • Benoit Testu
    • 1
  • Ryutaro Ichise
    • 1
  • Ewan Klein
    • 2
  • Adam Barker
    • 3
  1. 1.National Institute of InformaticsTokyoJapan
  2. 2.University of EdinburghEdinburghUK
  3. 3.University of St. AndrewsSt. AndrewsUK

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