Prophet -- A Link-Predictor to Learn New Rules on NELL

Abstract

Link prediction is a task that in graph-based data models, as well as, in complex networks not only to predict edges that will appear in a near future but also to find missing edges. NELL is a never ending language learner system that has the ability to continuously learn to extract structured information from unstructured text (fetched from web pages) and map this information to a continuously growing knowledge base. NELL's knowledge base can be seen as a complex network, allowing us to apply graph mining techniques to extract new knowledge and enhance the system performance. In this paper we present Prophet, a link prediction component that can be connected to NELL allowing the it to infer new rules and misplaced connections among nodes, thus, helping the never-ending system to learn more and better each day. We also show that Prophet can extract new knowledge that cannot be obtained using traditional first order rule extraction procedures.