A Semantically Guided and Domain Independent Evolutionary Model for Knowledge Discovery from Texts

J. Atkinson-Abutridy, Christopher Stuart Mellish, S. Aitken

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


We present a novel evolutionary model for knowledge discovery from texts (KDTs), which deals with issues concerning shallow text representation and processing for mining purposes in an integrated way. Its aims is to look for novel and interesting explanatory knowledge across text documents. The approach uses natural language technology and genetic algorithms to produce explanatory novel hypotheses. The proposed approach is interdisciplinary, involving concepts not only from evolutionary algorithms but also from many kinds of text mining methods. Accordingly, new kinds of genetic operations suitable for text mining are proposed. The principles behind the representation and a new proposal for using multiobjective evaluation at the semantic level are described. Some promising results and their assessment by human experts are also discussed which indicate the plausibility of the model for effective KDT.

Original languageEnglish
Pages (from-to)546-560
Number of pages14
JournalIEEE Transactions on Evolutionary Computation
Issue number6
Publication statusPublished - Dec 2003


  • data mining
  • genetic algorithms (GAs)
  • knowledge discovery from texts (KDTs)

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