Collective intelligence as a source for machine learning self-supervision
Proceedings of the 4th International Workshop on Web Intelligence & Communities
Autonomously reviewing and validating the knowledge base of a never-ending learning system
Proceedings of the 22nd international conference on World Wide Web companion
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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.