Catálogo de publicaciones - libros

A Self-Explaining Document (SED) is a document enriched with annotations keeping track of all possible interpretations with respect to a given grammar and dictionary, as well as disambiguating choices. If disambiguation is complete and has been done by the author himself, a SED conveys “the author’s intention”. The availability of SEDs might considerably reduce misunderstanding between authors and readers, and perhaps lead to the assignment of a “meaning certification level” to any part of a document. We present ways to integrate these annotations into an arbitrary XML document (SED-XML), and to make them visible and usable to readers for accessing the “true content” of a document. We also show that, under several constraints, a SED, once translated into a target language L, might be transformed into an SED in L with no human interaction. Hence, the SED structure might be used in multilingual as well as in monolingual contexts, without addition of human work.

In this paper, we present a mechanism to deal with short text structures in relational databases. Text fields are transformed into a special knowledge representation named AP-structure based on the Apriori algorithm of the mining area. Once the abstract data type is obtained, the text fields can be summarized, mined, and queried in a easy way. The operations to query these fields are the main aim of this paper.

In the past few years, several studies have emphasized the use of ontologies as an alternative to information organization. The notion of ontology has become popular in fields such as intelligent information integration, information retrieval on the Internet, and knowledge management. Different groups use different approaches to develop and verify de effectiveness of ontologies [1] [2] [3]. This diversity can be a factor that makes it difficult the formularization of formal methodologies of evaluation. This paper seeks to provide a way to identify the effectiveness of the knowledge representation based on ontology that was developed trough Knowledge Based System tools. The reason for that is because all processing and storage of gathered information and knowledge base organization is done using this structure. Our evaluation is based on case studies in the Ontoweb system [4], involving real world ontology for money laundry domain. Our results indicate that modification of ontology structure can effectively reveal faults, as long as they adversely affect the program state.

Effective usage of multimedia digital libraries has to deal with the problem of building efficient content annotation and retrieval tools. In particular in video domain, different techniques for manual and automatic annotation and retrieval have been proposed. Despite the existence of well-defined and extensive standards for video content description, such as MPEG-7, these languages are not explicitly designed for automatic annotation and retrieval purpose. Usage of linguistic ontologies for video annotation and retrieval is a common practice to classify video elements by establishing relationships between video contents and linguistic terms that specify domain concepts at different abstraction levels. The main issue related to the use of description languages such as MPEG-7 or linguistic ontologies is due to the fact that linguistic terms are appropriate to distinguish event and object categories but they are inadequate when they must describe specific or complex patterns of events or video entities. In this paper we propose the usage of knowledge representation languages to define ontologies enriched with visual information that can be used effectively for video annotation and retrieval. Difference between content description languages and knowledge representation languages are shown, the advantages of using enriched ontologies both for the annotation and the retrieval process are presented in terms of enhanced user experience in browsing and querying video digital libraries.

A temporal question answering system must be able to deduce which qualitative temporal relation holds between two events, a reasoning task that is complicated by the fact that historical events tend to have a gradual beginning and ending. In this paper, we introduce an algebra of temporal relations that is well–suited to represent the qualitative temporal information we have at our disposal. We provide a practical algorithm for deducing new temporal knowledge, and show how this can be used to answer questions that require several pieces of qualitative and quantitative temporal information to be combined. Finally, we propose a heuristic technique to cope with inconsistencies that may arise when integrating qualitative and quantitative information.

Access to complex information bases through multidimensional, dynamic taxonomies (also improperly known as faceted classification systems) is rapidly becoming pervasive in industry, especially in e-commerce. In this paper, the major shortcomings of conventional, monodimensional taxonomic approaches, such as the independence of different branches of the taxonomy and insufficient scalability, are discussed. The dynamic taxonomy approach, the first and most complete model for multidimensional taxonomic access to date, is reviewed and compared to conventional taxonomies. We analyze the reducing power of dynamic taxonomies and conventional taxonomies and report experimental results on real data, which confirm that monodimensional taxonomies are not useful for browsing/retrieval on large databases, whereas dynamic taxonomies can effectively manage very large databases and exhibit a very fast convergence.

This paper describes an efficient audio flow control method in the point of quantitative performance using audio-mixing, compared to existing P2P(Peer To Peer) method. In comparison with existing P2P method, using central mixing and distributed terminal mixing method, we achieved advance at the point of global network usage and each terminal’s CPU load, and additionally we expect more session, more terminal can be served by same amount of network bandwidth and computers. By using P2P method in audio communication, speaker and listener must connect to each other. So it has the critical defect that as the participants grows more and more, the network bandwidth usage, each terminal’s CPU load will grows rapidly. So the number of participants in same session will be extremely restricted. In comparison with P2P method, the central mixing method has the great advantage at the points of network usage and terminals CPU load. Regardless of the number of speakers and listeners, all the participants can speak and listen with all other participants by using just one stream’s amount of data size and CPU load. But all the network usages and CPU loads of "Audio decompression->Buffering->Mixing->Audio Compression" are concentrated on central server. So the number of sessions and terminals can be participated in one server will be highly restricted. This study solves the problems of server’s CPU load and network load by using the distributed terminal mixing method.

In this work, T-DiCoR is presented (hree mensional nceptual epresentation) as a tool for representing the fuzzy relations among the most representative concepts of a domain. Using this tool in a Metasearcher, the user may observe what other concepts are related to the searched concept, and what the connection forces are (fuzzy relations between concepts). This knowledge can be useful for making new queries with words conceptually related in a specific domain with the original ones.

It is presented several connections between Fuzzy Logic, the Semantic Web, and its components (Ontologies, Description Logics). It is then introduced and illustrated by an example (“Ontology of Art”) a Fuzzy Ontology structure, Lexicon and Knowledge Base.

We consider the Peer-To-Peer (P2P) database systems with RDF ontologies and with the semantic characterization of P2P mappings based on logical views over local peer’s ontology. Such kind of virtual-predicate based mappings needs an embedding of RDF ontologies into a predicate first-order logic, or at some of its sublanguages as, for example, logic programs for deductive databases. We consider a peer as a epistemic logic system with its own belief based on RDF tuples, independent from other peers and their own beliefs. This motivates the need of a semantic characterization of P2P mappings based not on the extension but on the of concepts used in the mappings, that is, based on intensional logic. We show that it adequately models robust weakly-coupled framework of RDF ontologies and supports decidable query answering for the union of conjunctive queries.