Integrating Know-How into the Linked Data Cloud
Paolo Pareti, Benoit Testu, Ryutaro Ichise, Ewan Klein · 2016
Abstract. This paper presents the first framework for integrating pro-cedural 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 knowl-edge 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 automati-cally acquiring this representation from existing resources on the Web. This system also allows the automatic generation of links between differ-ent 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. 1