Catálogo de publicaciones - libros

The emergence of the World-Wide Web has made available a multitude of autonomous data sources which can as a whole satisfy most of users information needs. However, it remains a tedious and long task for users to find the data sources that are relevant to their request, to interact with each of those sources in order to extract the useful pieces of information which then have to be combined for building the expected answer to the initial request.

An increasing number of services are appearing both within agent communities and as Web Services on the World Wide Web. As these services proliferate, humans and agents need to be able to find, select, understand and invoke these services. Today, services (e.g. travel services, book selling services, stock reporting services etc) are discovered and invoked manually by human users. In the near future, such service discovery and use will be mediated by agents acting on behalf of their human users. Such use of agent technology will be the next Web revolution. Instead of populated with human-readable documents, the Web will be populated with Agent-Mediated Services. For this to be accomplished, the Web must become agent-understandable, i.e. allow for of content.

Up to now, this vision has been conceived and pursued mainly in academia and research labs. However, recent industrial interest in such services, and the availability of tools to enable service automation (e.g. UDDI, WSDL, X-lang, WSFL, e-speak, .NET etc) holds the promise of fast progress in the automation in the Web Services area. Agent Mediated discovery, selection, execution and monitoring of Web Services will be a crucial test of Agent Technology. Agent Mediated Web Services is a confluence of Agent Technology and the Semantic Web.

In order to enable stable and scalable Agent Mediated Services, a widely used, widely accessible and extensible Multiagent (MAS) infrastructure is crucial. Part of this infrastructure should be languages for semantic annotation of content as well as for describing services, so that they can be discovered, invoked and composed. DAML-S is such a language for semantic descriptions of services. Another part of the MAS infrastructure should define communication and interoperability of agents. Various standards bodies (e.g. FIPA) are attempting to define standards for various aspects of MAS infrastructure, such as Agent Communications Languages.. However, there is no coherent account of what constitutes a MAS infrastructure, what functionality it supports, what characteristics it should have in order to enable various value-added abilities, such as Agent Based Mediation of Services, and what its possible relation with and requirements it may impose on the design and structure of single agents.

In this talk, we will present a model of MAS infrastructure, and our implemented RETSINA system that is an example of the general infrastructure model. In addition, we will show how RETSINA implements Agent Mediated Web Services through a variety of tools and mechanisms. Moreover, we will present DAML-S and illustrate its utility in the area of Agent Mediated Services.

It is common that a database contains noisy data. An important source of noise consists in mislabeled training instances. We present a new approach that deals with improving classification accuracies in such a case by using a preliminary filtering procedure. An example is suspect when in its neighborhood defined by a geometrical graph the proportion of examples of the same class is not significantly greater than in the whole database. Such suspect examples in the training data can be removed or relabeled. The filtered training set is then provided as input to learning algorithm. Our experiments on ten benchmarks of UCI Machine Learning Repository using 1-NN as the final algorithm show that removing give better results than relabeling. Removing allows maintaining the generalization error rate when we introduce from 0 to 20% of noise on the class, especially when classes are well separable.

Agents that learn on-line with partial instance memory reserve some of the previously encountered examples for use in future training episodes. We extend our previous work by combining our method for selecting extreme examples with two incremental learning algorithms, and . Using these new systems, and , and the task computer intrusion detection, we conducted a lesion study to analyze trade-offs in performance. Results showed that, although our partial-memory model decreased predictive accuracy by 2%, it also decreased memory requirements by 75%, learning time by 75%, and in some cases, concept complexity by 10%, an outcome consistent with earlier results using our partial-memory method and batch learning.

Automatic content extraction from multimedia files is a hot topic nowadays. Moving Picture Experts Group develops MPEG-7 standard, which aims to define a unified interface for multimedia content description, including audio data. Audio description in MPEG-7 comprises features that can be useful for any content-based search of sound files. In this paper, we investigate how to optimize sound representation in terms of musical instrument recognition purposes. We propose to trace trends in evolution of values of MPEG-7 descriptors in time, as well as their combinations. Described process is a typical example of KDD application, consisting of data preparation, feature extraction and decision model construction. Discussion of efficiency of applied classifiers illustrates capabilities of further progress in optimization of sound representation. We believe that further research in this area would provide background for automatic multimedia content description.

We describe a supervised learning approach to resolve difficulties in finding biologically significant local alignments. It was noticed that the () algorithm by Smith-Waterman, the prevalent tool for computing local sequence alignment, often outputs long, meaningless alignments while ignoring shorter, biologically significant ones. Arslan proposed an ( log ) algorithm which outputs a that maximizes the degree of similarity rather than the total similarity score. Given a properly selected normalization parameter, the algorithm can discover significant alignments that would be missed by the Smith-Waterman algorithm. Unfortunately, determining a proper normalization parameter requires repeated executions with different parameter values and expert feedback to determine the usefulness of the alignments. We propose a learning approach that uses existing biologically significant alignments to learn parameters for intelligently processing sub-optimal Smith-Waterman alignments. Our algorithm runs in () time and can discover biologically significant alignments without requiring expert feedback to produce meaningful results.

The aim of this paper is to illustrate how a probabilistic Description Logics, called , can be exploited for reasoning about information sources characterized by heterogeneous formats and structures. The paper first introduces syntax and semantics. Then, a -based approach is illustrated for inferring complex knowledge assertions from information sources characterized by heterogeneities in formats and representational structures. The thus obtained complex knowledge assertions can be exploited for constructing a user profile and for improving the quality of present Web search tools.

The paper investigates the roles of ontologies for Web intelligence, including issues on presentation, categories, languages, and automatic construction of ontologies. Three ontology categories are suggested, some of the research and development with respect to the three categories is presented, the major ontology languages are surveyed, and a multi-phase process of automatic construction of the ontologies is discussed.

Traditional query processing provides exact answers to queries. It usually requires that users fully understand the database structure and content to issue a query. Due to the complexity of the database applications, the so called global queries can be posed which traditional query answering systems can not handle. In this paper a query answering system based on distributed data mining is presented to rectify these problems. Task ontologies are used as a tool to handle semantic inconsistencies between sites.

This paper discusses an document discovery tool based on formal concept analysis. The program allows users to navigate email using a visual lattice metaphor rather than a tree. It implements a virtual file structure over email where files and entire directories can appear in multiple positions. The content and shape of the lattice formed by the conceptual ontology can assist in email discovery. The system described provides more flexibility in retrieving stored emails than what is normally available in email clients. The paper discusses how conceptual ontologies can leverage traditional document retrieval systems.