Catálogo de publicaciones - libros

The Scone knowledge-base system, currently being developed at Carnegie Mellon University, implements search and inference operations using a set of marker-passing algorithms. These were originally designed for a massively parallel hardware architecture but now are implemented completely in software. The algorithms are fast, relatively simple, and they support efficient implementation of the most heavily used KB features. This paper describes these marker-passing algorithms, their strengths and limitations, and how they are used in Scone.

The first hyper tableau suffers from blind guessing in instancing the clauses, and evolves into the unification-driven style, the second version. However, we found a counterexample of it. We modify the calculus and a new hyper tableau is represented.

It is believed that the structure of a group is not stable which changes along with the time, the completion of goals and other random factors. After a thorough study over different kinds of group-awareness theories in recent years, and combined with the important concept Service, a new group-awareness model is proposed which is services-oriented, is called Service-Oriented Group Awareness Model (SOGAM). The awareness need of applications in heterogeneity environment and representation of the dynamic property in the group structure can be resolved by this model. The formalization to describe the awareness model, implementation of a Web-based architecture using Web Service related standards as communication model to share awareness information are given in this paper. Finally problems that need further study are pointed out.

The aim of this paper is to specify the ontological commitments of the theory of document genres proposed by J. Yates and W. Orlikowski. To this end, I construct a formal ontology of documents and genres in which to define the notions presupposed in the genre discourse. For the sake of decreasing ambiguity and confusion, I briefly describe the primitive terms in which this ontology is formulated.

Access control is an important issue related to the security on the Semantic Web. Role-Based Access Control (RBAC) is commonly considered as a flexible and efficient model in practice. In this paper, we provide an OWL-based approach for RBAC in the Semantic Web context. First we present an extended model of RBAC with negative authorization, providing detailed analysis of conflicts. Then we use OWL to formalize the extended model. Additionally, we show how to use an OWL-DL reasoner to detect the potential conflicts in the extended model.

Ontology matching is an essential operation in many application domains, such as the Semantic Web, ontology merging or integration. So far, quite a few ontology matching approaches or matchers have been proposed. It has been observed that combining the results of multiple matchers is a promising technique to get better results than just using one matcher at a time. Many aggregation operators, such as , , and , have been developed. The limitations of these operators are studied. To overcome the limitations and provide a semantic interpretation for each aggregation operator, in this paper, we propose a linguistic combination system (LCS), where a linguistic aggregation operator (LAO), based on the ordered weighted averaging (OWA) operator, is used for the aggregation. A weight here is not associated with a specific matcher but a particular ordered position. A large number of LAOs can be developed for different uses, and the existing aggregation operators , and are the special cases in LAOs. For each LAO, there is a corresponding semantic interpretation. The experiments show the strength of our system.

We propose a novel system for collaborative knowledge sharing and recommendation based on taxonomic partial reputations on web-based personal knowledge directories. And we developed a prototype of the proposed system as a web-based user interface for personal knowledge management. This system presents a personal knowledge directory to a registered user. Such a directory has a personal ontology to integrate and classify the knowledge collected by a user from the Web. And the knowledge sharing activities among registered users generate partial reputation factors of knowledge items, their domain nodes, users and groups. Then new users can obtain the knowledge items proper to their needs, by referring such reputation values of those elements. In addition, users can also take the that is a set of common knowledge items over the domains designated by them. Thus proposed system can prevent cold-start problem because our knowledge recommendation mechanisms depend on the results of the collaborative knowledge sharing activities among users.

This paper presents a series of text-mining algorithms for managing knowledge directory, which is one of the most crucial problems in constructing knowledge management systems today. In future systems, the constructed directory, in which knowledge objects are automatically classified, should evolve so as to provide a good indexing service, as the knowledge collection grows or its usage changes. One challenging issue is how to combine manual and automatic organization facilities that enable a user to flexibly organize obtained knowledge by the hierarchical structure over time. To this end, I propose three algorithms that utilize text mining technologies: semi-supervised classification, semi-supervised clustering, and automatic directory building. Through experiments using controlled document collections, the proposed approach is shown to significantly support hierarchical organization of large electronic knowledge base with minimal human effort.

It is relatively easy to detect exact matches in Web documents; however, detecting similar content in distinct Web documents with different words and sentence structures is a much more difficult task. A reliable tool for determining the degree of similarity between any two Web documents could help filter or retain Web documents with similar content. Most methods for detecting similarity between documents rely on some kind of textual fingerprinting or a process of looking for exactly matched substrings. This may not be sufficient as changing the sentence structure or replacing words with synonyms can cause sentences with similar/same content to be treated as different. In this paper, we develop a sentence-based Fuzzy Set Information Retrieval (IR) approach, using word clusters that capture the similarity between different words for discovering similar documents. Our approach has the advantages of detecting documents with similar, but not necessarily the same, sentences based on fuzzy-word sets. The three different fuzzy-word clustering techniques that we have considered include the correlation cluster, the association cluster, and the metric cluster, which generate the word-to-word correlation values. Experimental results show that by adopting the metric cluster, our similarity detection approach has high accurate rate in detecting similar documents and improves previous Fuzzy Set IR approaches based solely on the correlation cluster.

Formal concepts and concept lattices are two central notions of formal concept analysis. This paper investigates the problem of determining formal concepts based on the congruences on semilattices. The properties of congruences corresponding to formal contexts are discussed. The relationship between the closed sets generated by congruences and the elements of indiscernibility matrices is examined. Consequently, a new approach of determining concept lattices is derived.