Context-sensitive text mining with fitness leveling Genetic Algorithm

Author

Huk, Maciej ; Kwiatkowski, Jan ; Konieczny, Dariusz ; Kedziora, Michal ; Mizera-Pietraszko, Jolanta

Author_Institution

Dept. of Comput. Sci., Wroclaw Univ. of Technol., Wroclaw, Poland

fYear

2015

fDate

24-26 June 2015

Firstpage

342

Lastpage

347

Abstract

Contextual processing is a great challenge for information retrieval study - the most approved techniques include scanning content of HTML web pages, user supported metadata analysis, automatic inference grounded on knowledge base, or content-oriented digital documents analysis. We propose a meta-heuristic by making use of Genetic Algorithms for Contextual Search (GACS) built on genetic programming (GP) and custom fitness leveling function to optimize contextual queries in exact search that represents unstructured phrases generated by the user. Our findings show that the queries built with GACS can significantly optimize the retrieval process.

Keywords

data mining; genetic algorithms; query processing; text analysis; GACS; HTML Web pages; automatic inference; content-oriented digital documents analysis; context-sensitive text mining; contextual processing; contextual queries; custom fitness leveling function; fitness leveling genetic algorithm; genetic algorithms for contextual search; genetic programming; information retrieval; knowledge base; meta-heuristic; retrieval process; user supported metadata analysis; Context; Convergence; Correlation; Data mining; Genetic algorithms; Sociology; Data mining; contextual search; fitness leveling; genetic programming; text retrieval;

fLanguage

English

Publisher

ieee

Conference_Titel

Cybernetics (CYBCONF), 2015 IEEE 2nd International Conference on

Conference_Location

Gdynia

Print_ISBN

978-1-4799-8320-9

Type

conf

DOI

10.1109/CYBConf.2015.7175957

Filename

7175957