Klein, Ewan

Barker, Adam

Rovatsos, Michael

Pareti, Paolo

other

2018-03-27T12:32:54Z

2018-03-27T12:32:54Z

2018-07-02

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.

http://hdl.handle.net/1842/29010

en

The University of Edinburgh

Pareti, P., Klein, E., and Barker, A. (2016). Linking Data, Services and Human Know-How. In The Semantic Web. Latest Advances and New Domains, ESWC 2016, pages 505–520. Springer International Publishing

Pareti, P., Testu, B., Ichise, R., Klein, E., and Barker, A. (2014b). Integrating Know-How into the Linked Data Cloud. In Knowledge Engineering and Knowledge Management, volume 8876 of Lecture Notes in Computer Science, pages 385–396. Springer International Publishing

Pareti, P. (2016). Distributed Linked Data as a Framework for Human-Machine Collaboration. In Hartig, O., Sequeda, J., and Hogan, A., editors, Proceedings of the 7th International Workshop on Consuming Linked Data (COLD), number 1666 in CEUR Workshop Proceedings, Aachen

Pareti, P., Klein, E., and Barker, A. (2014a). A Semantic Web of Know-how: Linked Data for Community-centric Tasks. In Proceedings of the 23rd International Conference on World Wide Web Companion, pages 1011–1016

Pareti, P., Klein, E., and Barker, A. (2015). A Linked Data Scalability Challenge: Concept Reuse Leads to Semantic Decay. In Proceedings of the ACM Web Science Conference, WebSci ’15, pages 7:1–7:5, New York, NY, USA. ACM.

natural language

step-by-step instructions

analysis

wikiHow

machine learning

semantic framework

Representation and execution of human know-how on the Web

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy