Detect missing attributes for entities in knowledge bases via hierarchical clustering

Abstract

Automatically constructed knowledge bases often suffer from quality issues such as the lack of attributes for existing entities. Manually finding and filling missing attributes is time consuming and expensive since the volume of knowledge base is growing in an unforeseen speed. We, therefore, propose an automatic approach to suggest missing attributes for entities via hierarchical clustering based on the intuition that similar entities may share a similar group of attributes. We evaluate our method on a randomly sampled set of 20,000 entities from DBPedia. The experimental results show that our method can achieve a high precision and outperform existing methods.