A Knowledge Representation for, and an Application to Requirements Elicitation of, Rhetorical Figures of Perfect Lexical Repetition

dc.contributor.authorWang, Yetian
dc.date.accessioned2026-01-22T15:28:57Z
dc.date.available2026-01-22T15:28:57Z
dc.date.issued2026-01-22
dc.date.submitted2026-01-16
dc.description.abstractRhetorical figures, such as rhyme and metaphor, affect human discourse by providing essential semantic and pragmatic information that generate a set of attentional effects such as salience, aesthetic pleasure, and memorability, that enhance the receiver’s attention. Ploke is one kind of rhetorical figure, that of perfect lexical repetition, which is a word or phrase that repeats with the same form and meaning in a passage. Rhetorical figures, including plokes, are largely ignored in natural language processing (NLP) and artificial intelligence (AI). This thesis aims to take two steps towards AIs’ being able to handle plokes as they occur in natural language. It first develops a knowledge representation model of the general concept of Ploke in the form of an ontology that represents the classification of Ploke, the forms of plokes, and the neurocognitive affinities that affect attention. This ontology will help AIs to understand and generate plokes. The ontology proposed in this thesis is able to represent the related knowledge of ploke and its subtypes. The ontology is able also to represent the neurocognitive affinities of ploke and its subtypes by representing their relations to various types of perfect lexical repetition characterized by the positions in which the repetitions occur. After observing that rhetorical figures are used to enhance persuasive discourse, the thesis hypothesizes that a requirements elicitation interview that uses plokes is more effective than one that does not. It then describes a test of this hypothesis in which the interviews were conducted by a simulated AI elicitation bot, which used some plokes in half of its interviews and avoided plokes entirely in the other half of its interviews. The experiment showed that the interview questions and statements conveyed by the simulated AI elicitation bot in its ploke-using requirements elicitation interviews were easier to recognize by the interviewees and were more memorable to them than those in its ploke-avoiding requirements elicitation interviews.
dc.identifier.urihttps://hdl.handle.net/10012/22881
dc.language.isoen
dc.pendingfalse
dc.publisherUniversity of Waterlooen
dc.subjectArtificial Intelligence
dc.subjectknowledge representation
dc.subjectrhetoric
dc.subjectrhetorical figure
dc.subjectrequirements engineering
dc.subjectTECHNOLOGY::Information technology::Computer science
dc.subjectHUMANITIES and RELIGION::Languages and linguistics::Linguistic subjects::Computational linguistics
dc.subjectOntology
dc.titleA Knowledge Representation for, and an Application to Requirements Elicitation of, Rhetorical Figures of Perfect Lexical Repetition
dc.typeDoctoral Thesis
uws-etd.degreeDoctor of Philosophy
uws-etd.degree.departmentDavid R. Cheriton School of Computer Science
uws-etd.degree.disciplineComputer Science
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.embargo.terms0
uws.contributor.advisorBerry, Daniel
uws.contributor.advisorWeddell, Grant
uws.contributor.affiliation1Faculty of Mathematics
uws.peerReviewStatusUnrevieweden
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.scholarLevelGraduateen
uws.typeOfResourceTexten

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