Ranking complex relationships on the semantic Web

Industry and academia are both focusing their attention on information retrieval over semantic metadata extracted from the Web, and it is increasingly possible to analyze such metadata to discover interesting relationships. However, just as document ranking is a critical component in today's search engines, the ranking of complex relationships would be an important component in tomorrow's semantic Web engines. This article presents a flexible ranking approach to identify interesting and relevant relationships in the semantic Web. The authors demonstrate the scheme's effectiveness through an empirical evaluation over a real-world data set.