Integrating Know-How into the Linked Data Cloud

  • Paolo Pareti
  • Benoit Testu
  • Ryutaro Ichise
  • Ewan Klein
  • Adam Barker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8876)


This paper presents the first framework for integrating procedural knowledge, or “know-how”, into the Linked Data Cloud. Know-how available on the Web, such as step-by-step instructions, is largely unstructured and isolated from other sources of online knowledge. To overcome these limitations, we propose extending to procedural knowledge the benefits that Linked Data has already brought to representing, retrieving and reusing declarative knowledge. We describe a framework for representing generic know-how as Linked Data and for automatically acquiring this representation from existing resources on the Web. This system also allows the automatic generation of links between different know-how resources, and between those resources and other online knowledge bases, such as DBpedia. We discuss the results of applying this framework to a real-world scenario and we show how it outperforms existing manual community-driven integration efforts.


Link Data Procedural Knowledge Declarative Knowledge Knowledge Extraction Candidate Entity 
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 2014

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 EdinburghEdinburghUnited Kingdom
  3. 3.University of St AndrewsSt AndrewsUnited Kingdom

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