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

Abstract

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 document searching in them.

Practical implications

With self‐organising maps the distribution of documents can be visualised and relevant documents found in document collections of limited size.

Originality/value

The paper reports on an approach that can be especially used to group documents and also for information search. So far self‐organising maps have rarely been studied for information retrieval. Instead, they have been applied to document grouping tasks.

Citation

Saarikoski, J., Laurikkala, J., Järvelin, K. and Juhola, M. (2009), "A study of the use of self‐organising maps in information retrieval", Journal of Documentation, Vol. 65 No. 2, pp. 304-322. https://doi.org/10.1108/00220410910937633