A social bookmarking system to support cluster driven archival arrangement

Cultural heritage materials are increasingly being made available through standard search facilities. However, it is challenging to automatically organize these materials in a way that is well aligned with users' specific interests. We report on the development of a social bookmaking system to collect human annotations that are used to measure the performance of three different clustering algorithms. We find that there is a discrepancy between the latent structure present in the data and the clusters annotated by humans. However, it is difficult to detect such discrepancies explicitly.