An Empirical Evaluation of Techniques for Ranking Semantic Associations (Extended Abstract)
Gong Cheng, Fei Shao, Yuzhong Qu · 2018
Searching for associations between entities is needed in many domains. It has been facilitated by the emergence of graph-structured semantic data on the Web, which offers structured semantic associations more explicit than those hiding in unstructured text for computers to discover. The increasing volume of semantic data requires ranking techniques to identify the more important semantic associations for users. Considering a lack of comprehensive empirical evaluation of existing techniques, we carry out an extensive evaluation of eight techniques including two novel ones we propose. The practical effectiveness of these techniques is assessed based on 1,200 ground-truth rankings created by 30 human experts for real-life semantic associations and on the explanations given by the experts.