A study of the use of self-organising maps in information retrieval

Purpose – The aim of this paper is to explore the possibility of retrieving information with Kohonen self‐organising maps, which are known to be effective to group objects according to their similarity or dissimilarity.Design/methodology/approach – After conventional preprocessing, such as transforming into vector space, documents from a German document collection were trained for a neural network of Kohonen self‐organising map type. Such an unsupervised network forms a document map from which relevant objects can be found according to queries.Findings – Self‐organising maps ordered documents to groups from which it was possible to find relevant targets.Research limitations/implications – The number of documents used was moderate due to the limited number of documents associated to test topics. The training of self‐organising maps entails rather long running times, which is their practical limitation. In future, the aim will be to build larger networks by compressing document matrices, and to develop docu...

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