Rankbox: An adaptive ranking system for mining complex semantic relationships using user feedback
Na Chen, Viktor K. Prasanna · 2012
This paper presents Rankbox, an adaptive ranking system for mining complex relationships on the Semantic Web. Our objective is to provide an effective ranking method for complex relationship mining, which can 1) automatically personalize ranking results according to user preferences, 2) be continuously improved to more precisely capture user preferences, and 3) hide as many technical details from end users as possible. We observe that a user's opinions on search results carry important information regarding his interests and search intentions. Based on this observation, our system supports each user to give simple feedback about the current search results, and employs a machine-learning based ranking algorithm to learn the user's preferences from his feedback. A personalized ranking function is then generated and used to sort results of the user's subsequent queries. The user can keep teaching the system his preferences by giving feedback through several iterations until he is satisfied with the search results. Our system is implemented and deployed on a web server that can be easily accessed through web browsers. We evaluate our system on a large RDF knowledge base created from the Freebase linked-open-data. The experimental results demonstrate the effectiveness of our method compared to the state-of-the-art.