Diversity-Aware Entity Exploration on Knowledge Graph

Knowledge graphs are graph-structured knowledge bases containing abundant entities and relations among the entities. Entity exploration can help users understand the overall structure of knowledge graphs, as well as find the entities of interest in an exploratory manner. Being different from typical entity-centric search whose goal is to retrieve the most related entities for a user’s specific need, the related entities that present diverse aspects are preferred for user’s ambiguous needs in entity exploration. In this paper, we propose a novel diversity-aware entity exploration approach based on random walk model, which naturally mimics human conceptual exploration by surfing a class association graph. This model leverages the diversity, representativeness and relatedness of entity classes to rank these classes in a unified way. Furthermore, for each top ranked class, the associated entities are ranked by combining their diversity and popularity. We compare our algorithm with four baseline algorithms and the experimental results indicate that it outperforms baselines. Furthermore, we conduct a task-based user study to evaluate our approach and the experimental results show that our work provides effective support for entity exploration.