Integrating Know-How into the Linked Data Cloud
This work addresses the challenge of connecting isolated procedural knowledge for users of the Linked Data Cloud, though it is incremental as it extends existing Linked Data benefits to a new type of knowledge.
The paper tackles the problem of integrating unstructured procedural knowledge (know-how) into the Linked Data Cloud by proposing a framework for representing it as Linked Data and automatically acquiring it from web resources, showing that it outperforms manual integration efforts in a real-world scenario.
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.