A Linked Data Approach to Know-How

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 cur-rently suffers from a lack of structure and isolation from related knowl-edge. To overcome these challenges we have developed a Linked Data framework which can automate the extraction of know-how from exist-ing 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 reposi-tories 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.