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A Linked Data Approach to Know-How

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Knowledge Engineering and Knowledge Management (EKAW 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8982))

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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.

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References

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Author information

Authors and Affiliations

  1. National Institute of Informatics, Tokyo, Japan

    Paolo Pareti, Benoit Testu & Ryutaro Ichise

  2. University of Edinburgh, Edinburgh, UK

    Paolo Pareti & Ewan Klein

  3. University of St. Andrews, St. Andrews, UK

    Adam Barker

Authors
  1. Paolo Pareti
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  2. Benoit Testu
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  3. Ryutaro Ichise
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  4. Ewan Klein
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  5. Adam Barker
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Corresponding author

Correspondence to Paolo Pareti .

Editor information

Editors and Affiliations

  1. Linköping University, Linköping, Sweden

    Patrick Lambrix

  2. Aalto University, Espoo, Finland

    Eero Hyvönen

  3. Linköping University, Linköping, Sweden

    Eva Blomqvist

  4. STLab ISTC-CNR, Rome, Italy

    Valentina Presutti

  5. Southeast University, Nanjing, Jiangsu, China

    Guilin Qi

  6. University of Manchester, Manchester, United Kingdom

    Uli Sattler

  7. Indiana University Bloomington, Bloomington, Indiana, USA

    Ying Ding

  8. Fondazione Bruno Kessler, Trento, Italy

    Chiara Ghidini

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© 2015 Springer International Publishing Switzerland

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Pareti, P., Testu, B., Ichise, R., Klein, E., Barker, A. (2015). A Linked Data Approach to Know-How. In: Lambrix, P., et al. Knowledge Engineering and Knowledge Management. EKAW 2014. Lecture Notes in Computer Science(), vol 8982. Springer, Cham. https://doi.org/10.1007/978-3-319-17966-7_24

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  • DOI: https://doi.org/10.1007/978-3-319-17966-7_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17965-0

  • Online ISBN: 978-3-319-17966-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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