A hierarchical co-clustering approach for entity exploration over Linked Data

Abstract

With the increasing amount of Linked Data on the Web, large numbers of linked entities often make it difficult for users to find the entities of interest quickly for further exploration. Clustering as a fundamental approach, has been adopted to organize entities into meaningful groups. In general, link and entity class are semantically labelled and can be used to group linked entities. However, entities are usually associated with many links and classes. To avoid information overload, we propose a novel hierarchical co-clustering approach to simultaneously group links and entity classes. In our approach, we define a measure of intra-link similarity and intra-class similarity respectively, and then incorporate them into co-clustering. Our proposed approach is implemented in a Linked Data browser called CoClus. We compare it with other three browsers by conducting a task-based user study and the experimental results show that our approach provides useful support for entity exploration. We also compare our algorithm with three baseline co-clustering algorithms and the experimental results indicate that it outperforms baselines in terms of the Clustering Index score.