An empirical study on property clustering in linked data

Abstract

Properties are used to describe entities, a part of which are likely to be clustered together to constitute an aspect. For example, first name, middle name and last name are usually gathered to describe a person’s name. However, existing automated approaches to property clustering remain far from satisfactory for an open domain like Linked Data. In this paper, we firstly investigated the relatedness between properties using five different measures. Then, we employed three clustering algorithms and two combination methods for property clustering. Based on a moderate-sized sample of Linked Data, we empirically studied the property clustering in Linked Data and found that a proper combination of different measures gave rise to the best result. Additionally, we showed how the property clustering can improve user experience in our entity browsing system.