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dc.contributor.authorAtkinson-Abutridy, John Anthonyen
dc.date.accessioned2018-01-31T11:41:33Z
dc.date.available2018-01-31T11:41:33Z
dc.date.issued2003en
dc.identifier.urihttp://hdl.handle.net/1842/27800
dc.description.abstracten
dc.description.abstractThis thesis proposes a new approach for structured knowledge discovery from texts which considers both the mining process itself, the evaluation of this knowledge by the model, and the human assessment of the quality of the outcome.en
dc.description.abstractThis is achieved by integrating Natural-Language technology and Genetic Algorithms to produce explanatory novel hypotheses. Natural-Language techniques are specifically used to extract genre-based information from text documents. Additional semantic and rhetorical information for generating training data and for feeding a semistructured Latent Semantic Analysis process is also captured.en
dc.description.abstractThe discovery process is modeled by a semantically-guided Genetic Algorithm which uses training data to guide the search and optimization process. A number of novel criteria to evaluate the quality of the new knowledge are proposed. Consequently, new genetic operations suitable for text mining are designed, and techniques for Evolutionary Multi-Objective Optimization are adapted for the model to trade off between different criteria in the hypotheses.en
dc.description.abstractDomain experts were used in an experiment to assess the quality of the hypotheses produced by the model so as to establish their effectiveness in terms of novel and interesting knowledge. The assessment showed encouraging results for the discovered knowledge and for the correlation between the model and the human opinions.en
dc.publisherThe University of Edinburghen
dc.relation.isreferencedbyen
dc.subjectAnnexe Thesis Digitisation Project 2017 Block 16en
dc.titleSemantically-guided evolutionary knowledge discovery from textsen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelen
dc.type.qualificationnamePhD Doctor of Philosophyen


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