The detection of topics from large textual data volumes is currently a research area, which has many applications in the development of computational systems. A proposed solution for the detection of topics in data mining is the application of clustering methods. This paper presents the application of a new ontology-based methodology for the automatic topic detection without any previous information based on the use of hierarchical clustering algorithms and a multilingual knowledge base. The approach also includes lexical resources that allow us to enrich the semantics of the analyzed texts. The novelty of this approach consists of the dimensionality reduction of the terms present in the texts by using ontology and the introduction of a method for the creation of a term weight matrix for use in clustering algorithms. With this approach, it is possible to improve automatic topic detection in documents. The proposed methodology was assessed with four datasets (two of them in English and two in Spanish).

In this paper, a new approach to deal with textual attributes in databases is presented. The basic idea is to give a new representation for these attributes, so that it allows us to treat them together with and in the same way than the other database attributes in querying, data warehousing, data mining processes, etc. Additionally, the transformation process of the textual attribute implies the obtaining of a global representation, which includes a great part of the attribute meaning. Moreover, this representation can be improved by using an ontology during the querying process, which enables the semantic queries. The formal representation model is presented in the paper, as well as the mathematical bases for dealing with it. The whole implementation process is also described by using a medical database to show the experimental results. Finally, a set of semantic query examples are offered to explain the advantages of this new approach.

The algebraic structures known as bags were introduced by R. Yager as set-like algebraic structures where elements are allowed to be repeated. Since the original papers by Yager, different definitions of the concept of fuzzy bag, and the corresponding operators, are available in the literature, as well as some extensions of the union, intersection and difference operators of sets, and new algebraic operators. In general, the current definitions of bag pose very interesting issues related to the ontological aspects and practical use of bags. In this paper, we introduce a characterization of bags viewing them as the result of a count operation on the basis of a mathematical correspondence. We also discuss on the extension of our alternative characterization of bags to the fuzzy case. On these basis, we introduce some operators on bags and fuzzy bags, and we compare them to existing approaches. Finally, we deal with the case where no information about the correspondence is available, and only bounds can be provided for the count of elements of the result of algebraic operators. For this purpose, the notion of IC-bag (Chakrabarty, in: Proc. Int. Conf. On Computational Intelligence for Modelling, Control and Automation—CIMCA'2001, Vol. 25, 2001) is employed, and new operators for these structures are proposed. © 2009 Wiley Periodicals, Inc.

We investigate extensions of the classical measurement of effectiveness in information retrieval systems, precision and recall, to situations where the answer is modeled by a fuzzy set, such as in cases where each object in the answer is measured by its relevance to the query. The most used fuzzy extension of the classical precision-recall measure based on Zadeh’s relative cardinality appears to be counter-intuitive in some situations. We propose a new approach to the measurement of effectiveness, based on the evaluation of quantified sentences. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

We present an approach to a Genetic Information Retrieval Agent Filter (GIRAF) for documents from the Internet using a genetic algorithm (GA) with fuzzy set genes to learn the user's information needs. The population of chromosomes with fixed length represents such user's preferences. Each chromosome is associated with a fitness that may be considered the system's belief in the hypothesis that the chromosome, as a query, represents the user's information needs. In a chromosome, every gene characterizes documents by a keyword and an associated occurrence frequency, represented by a certain type of a fuzzy subset of the set of positive integers. Based on the user's evaluation of the documents retrieved by the chromosome, compared to the scores computed by the system, the fitness of the chromosomes is adjusted. A prototype of GIRAF has been developed and tested. The results of the test are discussed, and some directions for further works are pointed out.