Catálogo de publicaciones - libros

This paper proposes constructing an environment ontology to represent domain knowledge about Web services. The capability of a Web service is considered in terms of the effects it imposes on the environment during execution. Thus, more fundamental and precise semantic specification for service capability than conventional interface-based description language can be obtained. Basic concepts of the ontology include resources residing in the environment. For each environment resource, there is a corresponding hierarchical state machine specifying its dynamic characteristics. Thus, the influence of a machine on its environments can be modelled with the state machines of the environment resources. Rules and algorithms to construct an environment ontology on the basis of generic domain ontology are introduced. And then guidelines for specifying Web service capability semantically based on the constructed environment ontology are given.

The traditional rough set theory is a powerful tool to deal with complete information system, and its performance to process incomplete information system is weak, M.Kryszkiewcz has put forward the tolerance relation to handle the problem. however,the method may not be perfect on account of excessively many intersectional elements between classifications. This paper improves the tolerance relation proposed by M.Kryszkiewcz to obtain the – relation and – classes, presents rough set model for incomplete information system based on the – relation. The method of – relation is proved to be more superior to that of M.Kryszkiewcz’s tolerance relation. Finally, the conception of – reduction is defined, and the algorithm of – reduction is provided.

Methods based on resolution have been widely used for theorem proving since it was proposed. The extension rule (ER) method is a new method for theorem proving, which is potentially a complementary method to resolution-based methods. But the first-order ER approach is incomplete and not realized. This paper gives a complete first-order ER algorithm and describes the implementation of a theorem prover based on it and its application to solving some planning problems. We also report the preliminary computational results on first-order formulation of planning problems.

Workflow resource model describes all kinds of resources that support the execution of workflows. The meta-model for workflow resource model presents the constituents of workflow resource model. It is one of the three correlative sub-meta-models for workflow model. Based on the analysis of existed studies and real cases, an extended meta-model for workflow resource model was introduced by extending and modifying the meta-model for organizational model proposed by WfMC. The detail of entities and their relationships were described. The relationships between workflow resource model and process model were discussed. XML was used to describe the meta-model. In the end, a conclusion and proposals for future research directions were presented.

Rough set theory has been considered as a useful tool to model the vagueness, imprecision, and uncertainty, and has been applied successfully in many fields. Knowledge reduction is one of the most important problems in rough set theory. However, in real-world most of information systems are based on dominance relations in stead of the classical rough set because of various factors. To acquire brief decision rules from systems based on dominance relations, knowledge reductions are needed. The main aim of this paper is to study the problem. The basic concepts and properties of knowledge reduction based on evidence reasoning theory are discussed. Furthermore, the characterization and knowledge reduction approaches based on evidence reasoning theory are obtained with examples in several kinds of ordered information system, which is every useful in future research works of the ordered information systems.

A maximum distribution reduction is meant to preserve not only all deterministic information with respect to decision attributes but also the largest possible decision class for each object of an inconsistent decision table. Hence, it is useful to compute this type of reduction when mining decision tables with data inconsistency. This paper presents a novel algorithm for finding a maximum distribution reduct of an inconsistent decision table. Two functions of attribute sets are introduced to characterize a maximum distribution reduct in a new and simple way and then used as a heuristic in the algorithm to search for a reduction. Complexity analysis of the algorithm is also presented. As an application example, the presented algorithm was applied to mine a real surgery database and some interesting results were obtained.

Discretization is an important preprocessing technique in data mining tasks. Univariate Discretization is the most commonly used method. It discretizes only one single attribute of a dataset at a time, without considering the interaction information with other attributes. Since it is multi-attribute rather than one single attribute determines the targeted class attribute, the result of Univariate Discretization is not optimal. In this paper, a new Multivariate Discretization algorithm is proposed. It uses ICA (Independent Component Analysis) to transform the original attributes into an independent attribute space, and then apply Univariate Discretization to each attribute in the new space. Data mining tasks can be conducted in the new discretized dataset with independent attributes. The numerical experiment results show that our method improves the discretization performance, especially for the nongaussian datasets, and it is competent compared to PCA-based multivariate method.

This paper proposes and tests a new model that helps explain knowledge contribution in virtual communities. Grounded on a communication-based view, we examined key drivers of user intention to share knowledge in virtual communities from three aspects: the knowledge to be shared, the individual self and the environment. In particular, a self-concept-based motivation model was employed to investigate individuals’ motivational factors. An empirical study of 363 virtual community users demonstrated the salient and dominant influences of enhanced knowledge self-efficacy and self-image on knowledge contribution intention. Enjoyment in helping others, trust and system usability were also found to be important motivations for knowledge sharing. Implications for both researchers and practitioners are discussed.

Knowledge transfer as an important aspect of knowledge management has been considered as an effective way to promote the knowledge ability and the core competence of an organization. In this paper, a method to evaluate knowledge transfer ability in organization is proposed. Firstly, the main factors which affect the knowledge transfer ability to be found out through the analysis of the relevant research of domestic and international knowledge transfer, then, an index system is set up to evaluate knowledge transfer ability using the method of questionnaire investigation and statistical analysis as knowledge transmission ability, knowledge receptive ability, interactive ability and organizational supporting ability, etc.. According to the index system and the characteristics of linguistic assessment information provided by experts, a multi-index linguistic decision-making method based on linguistic assessment information is proposed using LWD operator and LOWA operator developed in recent years. Finally, an example is given to explain the method.

This paper proposes the web information extraction system that extracts the pre-defined information automatically from web documents (i.e. HTML documents) and integrates the extracted information. The system recognizes entities without labels by the probabilistic based entity recognition method and extends the existing domain knowledge semiautomatically by using the extracted data. Moreover, the system extracts the sub-linked information linked to the basic page and integrates the similar results extracted from heterogeneous sources. The experimental result shows that the global precision of seven domain sites is 93.5%. The system using the sub-linked information and the probabilistic based entity recognition enhances the precision significantly against the system using only the domain knowledge. Moreover, the presented system can extract the more various information precisely due to applying the system with flexibility according to domains. Thus, the system can increase the degree of user satisfaction at its maximum and contribute the revitalization of e-business.