As the ability to store and process massive amounts of user behavioral data increases, new approaches continue to arise for leveraging the wisdom of the crowds to gain insights that were previously very challenging to discover by text mining alone. For example, through collaborative filtering, we can learn previously hidden relationships between items based upon users' interactions with them, and we can also perform ontology mining to learn which keywords are semantically-related to other keywords based upon how they are used together by similar users as recorded in search engine query logs. The biggest challenge to this collaborative filtering approach is the variety of noise and outliers present in the underlying user behavioral data. In this paper we propose a novel approach to improve the quality of semantic relationships extracted from user behavioral data. Our approach utilizes millions of documents indexed into an inverted index in order to detect and remove noise and outliers.
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