Abstract
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 language | English |
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Title of host publication | Knowledge Engineering and Knowledge Management |
Editors | Krzysztof Janowicz, Stefan Schlobach, Patrick Lambrix, Eero Hyvönen |
Publisher | Springer International Publishing |
Pages | 385-396 |
Number of pages | 12 |
ISBN (Electronic) | 978-3-319-13704-9 |
ISBN (Print) | 978-3-319-13703-2 |
DOIs | |
Publication status | Published - 2014 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer International Publishing |
Volume | 8876 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
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Dive into the research topics of 'Integrating Know-How into the Linked Data Cloud'. Together they form a unique fingerprint.Datasets
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The Human Know-How Dataset
Pareti, P. (Creator) & Klein, E. (Creator), Edinburgh DataShare, 29 Apr 2016
DOI: 10.7488/ds/1394, https://datahub.io/dataset/human-activities-and-instructions and one more link, http://homepages.inf.ed.ac.uk/s1054760/prohow/index.htm (show fewer)
Dataset