Author(s): Xiang Zhang, Siyao Pi
Abstract: Many Linked Data are incomplete on the type information of entities, the lack of which is a barrier to the success of many Semantic Web tasks. Type inference can retrieves missing types by means of reasoning, but this approach may become invalid in noisy data. Data-driven type prediction became prevalent these years, which utilized features of massive typed entities to revise untyped entities. In this paper, we propose our approach of type prediction based on collective classification on untyped entities. We investigate three structural features of entities on their type indicativeness, including attributive features, neighboring features and latent features. We also study the effectiveness of prediction by a mash-up of various types of features in collective classification. Experiments on real-world Linked Data demonstrate that our approach is considerably effective in finding missing types.
Keywords: Linked Data; type prediction; collective classification