  The Web has become a potentially infinite information resource, turning into
an essential tool for many daily activities. This resulted in an increase in
the amount of information available in users' contexts that is not taken into
account by current information retrieval systems. This thesis proposes a
semisupervised information retrieval technique that helps users to recover
context relevant information. The objective of the proposed technique is to
reduce the vocabulary gap existing between the knowledge a user has about a
specific topic and the relevant documents available in the Web. This thesis
presents a method for learning novel terms associated with a thematic context.
This is achieved by identifying those terms that are good descriptors and good
discriminators of the user's current thematic context. In order to evaluate the
proposed method, a theoretical framework for the evaluation of search
mechanisms was developed. This served as a guide for the implementation of an
evaluation framework that allowed to compare the techniques proposed in this
thesis with other techniques existing in the literature. The experimental
evidence indicates that the methods proposed in this thesis present significant
improvements over previously published techniques. In addition, the evaluation
framework was equipped with novel evaluation metrics that favor the exploration
of novel material and incorporates a semantic relationship metric between
documents. The algorithms developed in this thesis evolve high quality queries,
which have the capability of retrieving results that are relevant to the user
context. These results have a positive impact on the way users interact with
available resources.
