Self-organizing maps ? en atlas över informationsrymden
The purpose of this thesis is to examine how semantic relations in a document collection can be visualized with a Kohonen self-organizing map. It can be seen as a map of the information space which can be used to support information retrieval. The study makes a comparison of the possible differences between a map that is based on morphologically unprocessed text and a map where the text has been lemmatized. The text that is being processed is the definitions of all the existing instances in WordNet of a random selection of indexing terms from the Times-collection. The purpose is to see if morphological processing somehow can show different semantic connections between term instances based on their definitions. Eventually some different cartographic and display methods are compared to examine their strengths and weaknesses when used as possible applications of information visualization. The results show only marginal advantage of visualization based on lemmatized text. The lemmatization brings together new instances of words but the semantic relations are far from unambiguous. The conclusion drawn from this study is that the authors didn't find any single visualization method that can show all aspects in a map. Different combinations of three and two dimensional methods might be required to get a better picture of an information space.