The Role of Automated Categorisation in e-Government Information Retrieval

High-precision search results are essential for helping e-government employees complete work-based tasks. Prior studies have shown that existing features of e-government systems need improvement in terms of search facilities, navigation, and metadata adoption. This paper investigates how automated categorization can enhance information organization and retrieval and presents the results of a controlled evaluation that compared automated categorization and free text indexing of the government intranet used by Danish tax authorities. Thirty-two individuals participated in the evaluation, conducting simulated searches and genuine search tasks. Searching behaviour and search outcome was documented by search logs, relevance assessments, and post search interviews.

The evaluation demonstrates a high potential for automated categorization in a government context. Overall, the categorized organization generated more reformulations and less query success. Session success was found to be fairly even between the two systems. Qualitative data revealed that categorized overviews of the search results were useful if the participating employee did not possess extensive knowledge of the task at hand. When task knowledge was present, categorization was used to support the assumptions of a correct search. On the other hand, however, test participants avoided using automated categorization if high-precision documents were among the top results or if few documents were retrieved. The findings emphasize the importance of simultaneously providing different search options for e-government IR systems and reveal that automated categorization is a valuable candidate for improving search facilities within this domain.