Catálogo de publicaciones - libros

This document shows a preliminary framework for editing in the context of the NeOn project. The goal is to manage, in a collaborative way, multiple networked ontologies for large-scale semantic applications. This paper shows the main concepts on the editorial workflow and several lifecycle use cases. The ontologies produced with this framework will be used by the Food and Agriculture Organization of the United Nations (FAO) in many different large applications such the [4]. Therefore a major goal for FAO is to have a strong and reliable ontology management system for editing the networked ontologies that applications will use as a basis. This framework for editing networked ontologies is being developed in the context of the NeOn Project. What we present here is a brief summary of the activities carried out in this project regarding user requirements and subsequent use case analysis.

This paper continues the research line on the multimodal logic of qualitative reasoning; specifically, it deals with the introduction of the notions non-closeness and distance. These concepts allow us to consider qualitative sum of medium and large numbers. We present a sound and complete axiomatization for this logic, together with some of its advantages by means of an example.

The objective of this work is to apply artificial ant colonies to solve the Rural Postman Problem on undirected graphs. In order to do so, we will transform this problem into a Traveling Salesman Problem, applying to this new problem algorithms based on artificial ant colonies, which have been applied at great length to the same, obtaining good results.

This article reports on a study on olive-fly infestation prediction using machine learning techniques. . The purpose of the work was, on the one hand, to make accurate predictions and, on the other, to verify whether the Bayesian network techniques are competitive with respect to classification trees. We have applied the techniques to a dataset and, in addition, performed a previous phase of variables selection to simplify the complexity of the classifiers. The results of the experiments show that Bayesians networks produce valid predictors, although improved definition of dependencies and refinement of the variables selection methods are required.

In this paper an incremental version of the ANOVA and Functional Networks Feature Selection (AFN-FS) method is presented. This new wrapper method (IAFN-FS) is based on an incremental functional decomposition, thus eliminating the main drawback of the basic method: the exponential complexity of the functional decomposition. This complexity limited its scope of applicability, being only applicable to datasets with a relatively small number of features. The performance of the incremental version of the method was tested against several real data sets. The results show that IAFN-FS outperforms the accuracy obtained by other standard and novel feature selection methods, using a small set of features.

When primitive data representation yields attribute interactions, learning requires feature construction. MFE2/GA, a GA-based feature construction has been shown to learn more accurately than others when there exist several complex attribute interactions. A new fitness function, based on the principle of Minimum Description Length (MDL), is proposed and implemented as part of the MFE3/GA system. Since the individuals of the GA population are collections of new features constructed to change the representation of data, an MDL-based fitness considers not only the part of data left unexplained by the constructed features (errors), but also the complexity of the constructed features as a new representation (theory). An empirical study shows the advantage of the new fitness over other fitness not based on MDL, and both are compared to the performance baselines provided by relevant systems.

A proposal is presented for integration between a graphical model, such as conceptual maps, and ontologies codified in the OWL language. Conceptual maps are a flexible form of knowledge representation, very useful in education-related collaborative environments; OWL is a language of knowledge representation oriented to semantic analysis and processing carried out by machines. Integration consists of a set of formal transformation applied to conceptual maps and the semantic analysis of the relations linking concepts. The proposed method is based on a concept sense-disambiguation procedure, also defined by the authors, and in the WordNet lexical database. It applies to conceptual maps of shallow domains with labels in the Spanish language.

Mapping thesauri is the task of identifying correspondences between entities in different thesauri. Discovering these matches is intrinsically problematic to automate. Earlier research has proposed solutions based on using lexical matching techniques and then, manually revising the resulting lexical mappings with the help of graphical user interfaces. Nevertheless, these solutions cannot guarantee the validity, accuracy and quality of the vocabulary mappings, as human capacity is limited. In this paper, we propose a method to automatically evaluate the quality of the results of a lexical technique. Our method combines structural constraints and annotations with part-of-speech tags to identifying error patterns from the results of lexical matches, differentiating between those leading to fall in precision and those producing decrease in recall.