An Empirical Evaluation of Techniques for Ranking Semantic Associations

Searching for associations between entities is needed in many domains like national security and bioinformatics. In recent years, 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 often produces excessively many semantic associations, and requires ranking techniques to identify the more important ones for users. Despite the fruitful theoretical research on innovative ranking techniques, there is a lack of comprehensive empirical evaluation of these techniques. In this article, we carry out an extensive evaluation of eight techniques for ranking semantic associations, 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 the explanations given by the experts. Our findings also suggest a number of directions in improving existing techniques and developing novel techniques for future work.