A Linked Data Approach to Know-How

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

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


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.
Original languageEnglish
Title of host publicationKnowledge Engineering and Knowledge Management
Subtitle of host publicationEKAW 2014 Satellite Events, VISUAL, EKM1, and ARCOE-Logic, Linköping, Sweden, November 24-28, 2014. Revised Selected Papers.
EditorsPatrick Lambrix, Eero Hyvönen, Eva Blomqvist, Valentina Presutti, Guilin Qi, Uli Sattler, Ying Ding, Chiara Ghidini
PublisherSpringer International Publishing
Number of pages4
ISBN (Electronic)978-3-319-17966-7
ISBN (Print)978-3-319-17965-0
Publication statusPublished - 2015

Publication series

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


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