Experiences with automated categorization in e-government information retrieval

High-precision search results are essential for supporting e-government employees’ information tasks. Prior studies have shown that existing features of e-government retrieval systems need improvement in terms of search facilities (e.g., Goh et al. 2008), navigation (e.g., de Jong and Lentz 2006) and metadata (e.g.,
Kopackova, Michalek and Cejna 2010). This paper investigates how automated categorization can enhance information organization and retrieval, and presents the results of a realistic evaluation that compared automated categorization with free text indexing of the government intranet used by Danish tax authorities. The evaluation demonstrates a potential for automated categorization in a government context. In terms of quantitative measures free text indexing performed at the same level or better than searching by categorization. However, the qualitative analysis revealed that categorized overviews were useful if the participant did not possess much knowledge of the task at hand. When task knowledge was present, categorization was used to support the assumptions of a correct search. Participants avoided automated categorization if high-precision documents
were among the top results or if few documents were retrieved. The findings emphasise the importance of simultaneous search options for e-government IR systems, and reveal that automated categorization is valuable in improving search facilities in e-government.