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Jones, G.; Robertson, A.M.; Willett, P.: ¬An introduction to genetic algorithms and to their use in information retrieval (1994)
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- Abstract
- This paper provides an introduction to genetic algorithms, a new approach to the investigation of computationally-intensive problems that may be insoluble using conventional, deterministic approaches. A genetic algorithm takes an initial set of possible starting solutions and then iteratively improves theses solutions using operators that are analogous to those involved in Darwinian evolution. The approach is illusrated by reference to several problems in information retrieval
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Willett, P.: Best-match text retrieval (1993)
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- Abstract
- Provides an introduction to the computational techniques that underlie best match searching retrieval systems. Discusses: problems of traditional Boolean systems; characteristics of best-match searching; automatic indexing; term conflation; matching of documents and queries (dealing with similarity measures, initial weights, relevance weights, and the matching algorithm); and describes operational best-match systems
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Robertson, A.M.; Willett, P.: Use of genetic algorithms in information retrieval (1995)
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- Abstract
- Reviews the basic techniques involving genetic algorithms and their application to 2 problems in information retrieval: the generation of equifrequent groups of index terms; and the identification of optimal query and term weights. The algorithm developed for the generation of equifrequent groupings proved to be effective in operation, achieving results comparable with those obtained using a good deterministic algorithm. The algorithm developed for the identification of optimal query and term weighting involves fitness function that is based on full relevance information
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Li, X.; Cox, A.; Ford, N.; Creaser, C.; Fry, J.; Willett, P.: Knowledge construction by users : a content analysis framework and a knowledge construction process model for virtual product user communities (2017)
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- Abstract
- Purpose The purpose of this paper is to develop a content analysis framework and from that derive a process model of knowledge construction in the context of virtual product user communities, organization sponsored online forums where product users collaboratively construct knowledge to solve their technical problems. Design/methodology/approach The study is based on a deductive and qualitative content analysis of discussion threads about solving technical problems selected from a series of virtual product user communities. Data are complemented with thematic analysis of interviews with forum members. Findings The research develops a content analysis framework for knowledge construction. It is based on a combination of existing codes derived from frameworks developed for computer-supported collaborative learning and new categories identified from the data. Analysis using this framework allows the authors to propose a knowledge construction process model showing how these elements are organized around a typical "trial and error" knowledge construction strategy. Practical implications The research makes suggestions about organizations' management of knowledge activities in virtual product user communities, including moderators' roles in facilitation. Originality/value The paper outlines a new framework for analysing knowledge activities where there is a low level of critical thinking and a model of knowledge construction by trial and error. The new framework and model can be applied in other similar contexts.
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Robertson, A.M.; Willett, P.: Generation of equifrequent groups of words using a genetic algorithm (1994)
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- Abstract
- Genetic algorithms are a class of non-deterministic algorithms that derive from Darwinian evolution and that provide good, though not necessarily optimal, solutions to combinatorial problems. We describe their application to the identification of characteristics that occur approximately equifrequently in a database, using two different methods for the creation of the chromosome data structures that lie at the heart of a genetic algortihm. Experiments with files of English and Turkish text suggest that the genetic algorithm developed here can produce results superior to those produced by existing non-deterministic algorithms; however, the results are inferior to those produced by an existing deterministic algorithm
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Artymiuk, P.J.; Spriggs, R.V.; Willett, P.: Graph theoretic methods for the analysis of structural relationships in biological macromolecules (2005)
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- Date
- 22. 7.2006 14:40:10
