Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.738969
Title: Representation and execution of human know-how on the Web
Author: Pareti, Paolo
ISNI:       0000 0004 7225 0367
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2018
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Abstract:
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.
Supervisor: Klein, Ewan ; Barker, Adam ; Rovatsos, Michael Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.738969  DOI: Not available
Keywords: natural language ; step-by-step instructions ; analysis ; wikiHow ; machine learning ; semantic framework
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