  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.
