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Title: Semantically-guided evolutionary knowledge discovery from texts
Author: Atkinson-Abutridy, John A.
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2003
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This 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. This 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 semi-structured Latent Semantic Analysis process is also captured. The discovery process is modelled 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 is 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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available