Optimization of Keyword Grouping in Biomedical Information Retrieval Using Evolutionary Algorithms
V. Dorfer, S. M. Winkler, T. Kern, G. Petz, P. Faschang - Optimization of Keyword Grouping in Biomedical Information Retrieval Using Evolutionary Algorithms - 22nd European Modeling and Simulation Symposium EMSS 2010, Fes, Marokko, 2010, pp. 25-30
The amount of data available in the field of life sciences is growing exponentially; therefore, intelligent information search strategies are required to find relevant information as fast and correctly as possible. In this paper we propose a document keyword clustering approach: On the basis of a given set of documents, we identify groups of keywords found in the given documents. Having developed those clusters, the complexity of the data base can be handled much easier: Future user queries can be extended with terms found in the same clusters as those originally defined by the user. In this paper we present a framework for representing and evaluating keyword clusters on a given data basis as well as a simple evolutionary algorithm (based on an evolution strategy) that shall find optimal keyword clusters. In the empirical section of this paper we document first results obtained using a data set published at the TREC-9 conference.
- FH-Prof. Mag. Dr. Gerald Petz
- DI (FH) Viktoria Dorfer MSc
- FH-Prof. DI Dr. Stephan Winkler
- DI (FH) Thomas Kern
- Patrizia Faschang B.A.
- Fakultät für Informatik, Kommunikation und Medien, Hagenberg
- Fakultät für Management, Steyr
- Research Center Hagenberg
- Research Center Steyr
- Research Group Bioinformatik
- Research Group Heuristic and Evolutionary Algorithms Laboratory