In this paper we present a short survey of fuzzy and Semantic approaches to Knowledge Extraction. The goal of such approaches is to define flexible Knowledge Extraction Systems able to deal with the inherent vagueness and uncertainty of the Extraction process. It has long been recognised that interactivity improves the effectiveness of Knowledge Extraction systems. Novice user's queries is the most natural and interactive medium of communication and recent progress in recognition is making it possible to build systems that interact with the user. However, given the typical novice user's queries submitted to Knowledge Extraction systems, it is easy to imagine that the effects of goal recognition errors in novice user's queries must be severely destructive on the system's effectiveness. The experimental work reported in this paper shows that the use of classical Knowledge Extraction techniques for novice user's query processing is robust to considerably high levels of goal recognition errors. Moreover, both standard relevance feedback and pseudo relevance feedback can be effectively employed to improve the effectiveness of novice user's query processing.