Representação de dados semânticos em agentes BDI

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2014A crescente necessidade de informação e consequente aumento no volume de comunicação tem conduzido a adoção de dados semânticos e resultando numa demanda por ferramentas que manipulam tais dados. Com o avanço de dados semânticos na Web, estamos atingindo um ponto onde ferramentas de software devem se adaptar a este novo formato. Este trabalho propõe um novo modelo para desenvolvimento de agentes inteligentes da IA baseados em um modelo de logica BDI, com o objetivo de permitir comunicação livre de ambiguidade e capaz de reutilizar dados semânticos já existentes na Web. O modelo propõe uma alternativa para a representação de dados semânticos no agente, como estes dados podem ser armazenados e utilizados para comunicação com triplestores da Web Semântica e também com outros agentes (semânticos e não semânticos). Com foco na representação destes dados semânticos, são exploradas maneiras de se integrar informação semântica a um agente, seus processos e estados e porque esta integração pode levar a melhores resultados quando acessando informação na Web. Além disso, e demonstrado o ganho que se pode obter ao reutilizar dados já existentes na Web Semântica, e como isto pode facilitar o desenvolvimento de novas aplicações baseadas em agentes. Finalmente, para avaliar o modelo proposto, e feita uma comparação qualitativa com outros trabalhos na área, levantando as diferenças, motivações e melhorias feitas neste trabalho. Uma implementação deste modelo e apresentada através de um arcabouço criado para demonstrar e validar as intenções deste trabalho. Este arcabouço chamado PySA e descrito expondo os principais pontos defendidos na proposta, testando em situações hipotéticas e exemplos reais a comunicação e aprendizado semanticamente rico que são os objetivos do trabalho.Abstract: Increasing needs for information and consequent increase in communication volume are leading to a widespread adoption of semantic data and demand for tools that manipulate such data. With the uprising of Semantic Web data, we are reaching a point where software tools must adapt to this new format. This work proposes a new model for developing intelligent agents based on a BDI reasoning model, with the goal of allowing ambiguity free communication and capable of reusing semantic data that already exists in the Web. The model proposes an alternative to representing semantic data in agents, and how this data can be stored and utilized to communicate with Semantic Web stores and also other agents (semantic and non-semantic). Focusing on the representation of this semantic data, this work explores ways to integrate semantic information to an agent, it's processes and states and why this integration can lead to better results when acessing information in the Web. On top of that, this work demonstrates what gain can be obtained from reutilizing data that already exists in the Semantic Web, and how this eases the development of new agent-based applications.Finally, to evaluate the proposed model, a qualitative comparison is made with similar work in the area, comparing the dierences, motivations and improvements made in this project. An implementation of this model is presented through a framework created to demonstrate and validate in practice the intentions of this project. This framework called PySA is described, exposing the main values defended in the proposal, testing in hypothetical situations and real examples the semantically rich communication and learning capabilities that are the main goal of this work

Web structure mining of dynamic pages

Web structure mining in static web contents decreases the accuracy of mined outcomes and affects the quality of decision making activity. By structure mining in web hidden data, the accuracy ratio of mined outcomes can be improved, thus enhancing the reliability and quality of decision making activity. Data Mining is an automated or semi automated exploration and analysis of large volume of data in order to reveal meaningful patterns. The term web mining is the discovery and analysis of useful information from World Wide Web that helps web search engines to find high quality web pages and enhances web click stream analysis. One branch of web mining is web structure mining. The goal of which is to generate structural summary about the Web site and Web pages. Web structure mining tries to discover the link structure of the hyperlinks at the inter-document level. In recent years, Web link structure mining has been widely used to infer important information about Web pages. But a major part of the web is in hidden form, also called Deep Web or Hidden Web that refers to documents on the Web that are dynamic and not accessible by general search engines; most search engine spiders can access only publicly index able Web (or the visible Web). Most documents in the hidden Web, including pages hidden behind search forms, specialized databases, and dynamically generated Web pages, are not accessible by general Web mining applications. Dynamic content generation is used in modern web pages and user forms are used to get information from a particular user and stored in a database. The link structure lying in these forms can not be accessed during conventional mining procedures. To access these links, user forms are filled automatically by using a rule based framework which has robust ability to read a web page containing dynamic contents as activeX controls like input boxes, command buttons, combo boxes, etc. After reading these controls dummy values are filled in the available fields and the doGet or doPost methods are automatically executed to acquire the link of next subsequent web page. The accuracy ratio of web page hierarchical structures can phenomenally be improved by including these hidden web pages in the process of Web structure mining. The designed system framework is adequately strong to process the dynamic Web pages along with static ones

An Agent System for Ontology Sharing on WWW

Semantic Web Services (SWS), a new generation WWW technology, will facilitate the automation of Web service tasks, including automated Web service discovery, execution, composition and mediation by using XML based metadata and ontology. There have been several e#orts to build knowledge representation languages for Web Services. However, only few attempts have so far been made to develop applications based on SWS. Especially, front-end agent systems for users are one of the urgent research areas. The purpose of this paper is to introduce our new integrated front-end agent system for ontology management and SWS management