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Erschienen in:
2015 | OriginalPaper | Buchkapitel
A Linked Data Approach to Know-How
verfasst von : Paolo Pareti, Benoit Testu, Ryutaro Ichise, Ewan Klein, Adam Barker
Erschienen in: Knowledge Engineering and Knowledge Management
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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.