Linking Data, Services and Human Know-How

An increasing number of everyday tasks involve a mixture of human actions and machine computation. This paper presents the first framework that allows non-programmer users to create and execute workflows where each task can be completed by a human or a machine. In this framework, humans and machines interact through a shared knowledge base which is both human and machine understandable. This knowledge base is based on the PROHOW Linked Data vocabulary that can represent human instructions and link them to machine functionalities. Our hypothesis is that non-programmer users can describe how to achievecertain tasks at a level of abstraction which is both human and machine understandable. This paper presents the PROHOW vocabulary and describes its usage within the proposed framework. We substantiate our claim with a concrete implementation of our framework and by experimental evidence.Postprin

Representation and execution of human know-how on the Web

Structured data has been a major component of web resources since the very beginning of the web. Metadata that was originally mostly meant for display purposes gradually expanded to incorporate the semantic content of a page. Until now semantic data on the web has mostly focused on factual knowledge, namely trying to capture “what humans know”. This thesis instead focuses on procedural knowledge, or in other words, “how humans do things”, and in particular on step-by-step instructions. I will present a semantic framework to capture the meaning of sets of instructions with respect to their potential execution. This framework is based on a logical model which I evaluated in terms of its expressiveness and it compatibility with existing languages. I will show how this type of procedural knowledge can be automatically acquired from human-generated instructions on the web, while at the same time bridging the semantic gap, from unstructured to structured, by mapping these resources into a formal process description language. I will demonstrate how procedural and factual data on the web can be integrated automatically using Linked Data, and how this integration results in an overall richer semantic representation. To validate these claims I have conducted large scale knowledge acquisition and integration experiments on two prominent instructional websites and evaluated the results against a human benchmark. Finally, I will demonstrate how existing web technologies allow for this data to seamlessly enrich existing web resources and to be used on the web without the need for centralisation. I have explored the potential uses of formalised instructions by the implementation and testing of concrete prototypes which enable human users to explore know-how and collaborate with machines in novel ways

Linking data, services and human know-how

An increasing number of everyday tasks involve a mixture of human actions and machine computation. This paper presents the first framework that allows non-programmer users to create and execute workflows where each task can be completed by a human or a machine. In this framework, humans and machines interact through a shared knowledge base which is both human and machine understandable. This knowledge base is based on the PROHOW Linked Data vocabulary that can represent human instructions and link them to machine functionalities. Our hypothesis is that non-programmer users can describe how to achievecertain tasks at a level of abstraction which is both human and machine understandable. This paper presents the PROHOW vocabulary and describes its usage within the proposed framework. We substantiate our claim with a concrete implementation of our framework and by experimental evidence