Integrating Know-How into the Linked Data Cloud

Paolo Pareti, Benoit Testu, Ryutaro Ichise, Ewan Klein, Adam Barker

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationKnowledge Engineering and Knowledge Management
EditorsKrzysztof Janowicz, Stefan Schlobach, Patrick Lambrix, Eero Hyvönen
PublisherSpringer International Publishing
Number of pages12
ISBN (Electronic)978-3-319-13704-9
ISBN (Print)978-3-319-13703-2
Publication statusPublished - 2014

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


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