Catálogo de publicaciones - libros

In this paper, we investigate the integration of virtual knowledge communities (VKC) into an ontology-driven uncertainty model (OntoBayes). The selected overall framework for OntoBayes is the multiagent paradigm. Agents modeled with OntoBayes have two parts: knowledge and decision making parts. The former is the ontology knowledge while the latter is based upon Bayesian Networks (BN). OntoBayes is thus designed in agreement with the Agent Oriented Abstraction (AOA) paradigm. Agents modeled with OntoBayes possess a common community layer that enables to define, describe and implement corporate knowledge. This layer consists of virtual knowledge communities.

This paper deals with the querying of possibilistic relational databases, by means of queries called generalized yes/no queries, whose general form is: “to what extent is it possible that the answer to Q satisfies property P”. Here, property P concerns the inclusion in the result, of a set of tuples specified by the user. A processing technique for such queries is proposed, which avoids computing the worlds attached to the possibilistic database.

This paper presents a semantics-driven web portal allowing browsing and flexible querying state-of-the-art data on organizations, national and international projects, current research topics, technologies and software solutions in the domain of the European Research in the FP6/IST program. The backbone of this portal consists of an ontology-based knowledge base structuring information about the above-described area. The portal incorporates advanced information retrieval methods, based on ontologies and on natural language processing.

A human face is a complex object with features that can vary over time. Face recognition systems have been investigated while developing biometrics technologies. This paper presents a face recognition system that uses eyes, nose and mouth approximations for training a neural network to recognize faces in different expressions such as natural, smiley, sad and surprised. The developed system is implemented using our face database and the ORL face database. A comparison will be drawn between our method and two other face recognition methods; namely PCA and LDA. Experimental results suggest that our method performs well and provides a fast, efficient system for recognizing faces with different expressions.

In this paper we present a methodology based on interoperability for building Semantic Web Mining systems. In particular we consider the still poorly investigated case of mining the Semantic Web layers of ontologies and rules. We argue that Inductive Logic Programming systems could serve the purpose if they were more compliant with the standards of representation for ontologies and rules in the Semantic Web and/or interoperable with well-established Ontological Engineering tools that support these standards.

Content-based filtering can reflect content information, and provide recommendations by comparing various feature based information regarding an item. However, this method suffers from the shortcomings of superficial content analysis, the special recommendation trend, and varying accuracy of predictions, which relies on the learning method. In order to resolve these problems, this paper presents content-based image filtering, seamlessly combining content-based filtering and image-based filtering for recommendation. Filtering techniques are combined in a weighted mix, in order to achieve excellent results. In order to evaluate the performance of the proposed method, this study uses the EachMovie dataset, and is compared with the performance of previous recommendation studies. The results have demonstrated that the proposed method significantly outperforms previous methods.

This work investigates on kernels that are applicable to semantic annotations expressed in Description Logics which are the theoretical counterpart of the standard representations for the Semantic Web. Namely, the focus is on the definition of a kernel for the logic, based both on the syntax and on the semantics of concept descriptions. The kernel is proved to be valid. Furthermore, semantic distance measures are induced from the kernel function.

The Resource Description Framework (RDF) is the basic standard for representing information in the Semantic Web. It is mainly designed to be machine-readable and -processable. This paper takes the opposite side of view: RDF is investigated as a logic system designed for the needs of humans. RDF is developed as a logic system based on mathematical graphs, i.e., as . As such, is has humanly-readable, diagrammatic representations. Moreover, a sound and complete calculus is provided. Its rules are suited to act on the diagrammatic representations. Finally, some normalforms for the graphs are introduced, and the calculus is modified to suit them.

Medical guidelines are clinical behaviour recommendations used to help and support physicians in the definition of the most appropriate diagnosis and/or therapy within determinate clinical circumstances. Due to the intrinsic complexity of such guidelines, their application is not a trivial task; hence it is important to verify if health-care workers behave in a conform manner w.r.t. the intended model, and to evaluate how much their behaviour differs.

In this paper we present the GPROVE framework that we are developing within a regional project to describe medical guidelines in a visual way and to automatically perform the conformance verification.

The aim of this paper consists in investigating the problem of managing inconsistent databases, i.e. databases violating integrity constraints. A flurry of research on this topic has shown that the presence of inconsistent data can be resolved by “repairing” the database, i.e. by providing a computational mechanism that ensures obtaining consistent “scenarios” of the information or by consistently answer to queries posed on an inconsistent set of data. This paper considers preferences among repairs and possible answers by introducing a partial order among them on the basis of some preference criteria. Moreover, the paper also extends the notion of preferred consistent answer by extracting from a set of preferred repaired database, the maximal consistent overlapping portion of the information, i.e. the information supported by each preferred repaired database.