Retrieving precise information from large collections of documents or from the web is an important task in our world. The specification of the information needed is done in form of a sequence of terms or query, which is frequently too short or unspecific to allow selecting a set of relevant documents small enough to be inspected by the user. This problem can be alleviated by expanding the query with other terms that make it more specific. The selection of these possible expansion terms is the problem addressed in this work. We have developed a classifier which has been trained for distinguishing good expansion terms. The identification of good terms to train the classifier has been achieved with a genetic algorithm whose fitness function is based on users' relevance judgements on a set of documents. Results show that the training performed by the genetic algorithm is able to improve the quality of the query expansion results.

This paper presents a new collection based on DBpedia and INEX for evaluating semantic search performance. The proposed corpus is used to calculate the impact of considering document's structure on the retrieval performance of the Lucene and BM25 ranking functions. Results show that BM25 outperforms Lucene in all the considered metrics and that there is room for future improvements, which may be obtained using a hybrid approach combining both semantic technology and information retrieval ranking functions.

A new scenario has raised into the IR field with the increase in the use of mark-up languages. This new scenario has been defined as structured IR and is focused on documents with structure. The classic IR models have been extended in order to be applied to this document type. Generally these adaptations have been carried out by weighting the fields that form the document structure, and making the assumption of statistics independence between fields. This assumption force to an estimation of the different weights applied to each field. In this paper a new ranking function for structured IR is proposed. This new function is based on Fuzzy Logic, and its main aim is to model through heuristics and expert knowledge the relations between fields within a document.

With the increase in the use of mark-up languages, the classic IR models have been extended in order to be applied to structured documents. Generally these adaptations have been carried out by weighting the fields that form the document structure, and making the assumption of statistics independence between fields, forcing to an estimation of the different weights applied to each field. In this paper a new ranking function for structured IR and based on Fuzzy Logic is proposed, where its main aim is to model through heuristics and expert knowledge the relations between fields.