Improving Knowledge Retrieval in Digital Libraries Applying Intelligent Techniques

Nowadays an enormous quantity of heterogeneous and distributed information is stored in the digital University. Exploring online collections to find knowledge relevant to a user’s interests is a challenging work. The artificial intelligence and Semantic Web provide a common framework that allows knowledge to be shared and reused in an efficient way. In this work we propose a comprehensive approach for discovering E-learning objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We have used Case Based-Reasoning methodology to develop a prototype for supporting efficient retrieval knowledge from online repositories. We suggest a conceptual architecture for a semantic search engine. OntoUS is a collaborative effort that proposes a new form of interaction between users and digital libraries, where the latter are adapted to users and their surroundings

Intelligent Integrated Management for Telecommunication Networks

As the size of communication networks keeps on growing, faster connections, cooperating technologies and the divergence of equipment and data communications, the management of the resulting networks gets additional important and time-critical. More advanced tools are needed to support this activity. In this article we describe the design and implementation of a management platform using Artificial Intelligent reasoning technique. For this goal we make use of an expert system. This study focuses on an intelligent framework and a language for formalizing knowledge management descriptions and combining them with existing OSI management model. We propose a new paradigm where the intelligent network management is integrated into the conceptual repository of management information called Managed Information Base (MIB). This paper outlines the development of an expert system prototype based in our propose GDMO+ standard and describes the most important facets, advantages and drawbacks that were found after prototyping our proposal

The Evolution of OSI Network Management by Integrated the Expert Knowledge

The management of modern telecommunications networks must satisfy ever-increasing operational demands. Operation and quality service requirements imposed by the users are also an important aspect to consider. In this paper we have carried out a study for the improvement of intelligent administration techniques in telecommunications networks. This task is achieved by integrating knowledge base of expert system within the management information used to manage a network. For this purpose, an extension of OSI management framework specifications language has been added and investigated in this study. A new property named RULE has also been added, which gathers important aspects of the facts and the knowledge base of the embedded expert system. Networks can be managed easily by using this proposed integration

Intelligent information processing in a digital library using semantic web

With the explosive growth of information, it is becoming increasingly difficult to retrieve the relevant documents with current search engine only. The information is treated as an ordinary database that manages the contents and positions. To the individual user, there is a great deal of useless information in addition to the substantial amount of useful information. This begets new challenges to docent community and motivates researchers to look for intelligent information retrieval approach and ontologies that search and/or filter information automatically based on some higher level of understanding are required. We study improving the efficiency of search methods and classify the search patrons into several models based on the profiles of agent based on ontology. We have proposed a method to efficiently search for the target information on a Digital Library network with multiple independent information sources. This paper outlines the development of an expert prototype system based in an ontology for retrieval information of the Digital Library University of Seville. The results of this study demonstrate that by improving representation by incorporating more metadata from within the information and the ontology into the retrieval process, the effectiveness of the information retrieval is enhanced. We used Jcolibri and Prótége for developing the ontology and creation the expert system respectively

Expert knowledge management based on ontology in a digital library

The architecture of the future Digital Libraries should be able to allow any users to access available knowledge resources from anywhere and at any time and efficient manner. Moreover to the individual user, there is a great deal of useless information in addition to the substantial amount of useful information. The goal is to investigate how to best combine Artificial Intelligent and Semantic Web technologies for semantic searching across largely distributed and heterogeneous digital libraries. The Artificial Intelligent and Semantic Web have provided both new possibilities and challenges to automatic information processing in search engine process. The major research tasks involved are to apply appropriate infrastructure for specific digital library system construction, to enrich metadata records with ontologies and enable semantic searching upon such intelligent system infrastructure. We study improving the efficiency of search methods to search a distributed data space like a Digital Library. This paper outlines the development of a CaseBased Reasoning prototype system based in an ontology for retrieval information of the Digital Library University of Seville. The results demonstrate that the used of expert system and the ontology into the retrieval process, the effectiveness of the information retrieval is enhanced

Intelligent Techniques for Knowledge Recovery in University Education

El desarrollo de sistemas de búsqueda que faciliten la gestión del conocimiento académico en un espacio distribuido como son las Bibliotecas digitales universitarias, es uno de los principales objetivos de instituciones y proveedores de información. Estos nuevos retos motivan a los investigadores y a la comunidad docente a buscar nuevos enfoques en la recuperación eficiente de la información. El presente estudio supone un esfuerzo en innovación educativa, y propone un enfoque pragmático en la aplicación de la recuperación del conocimiento en las bibliotecas digitales. Para ello utilizamos un enfoque ontológico y técnicas de la inteligencia artificial.The main goal of the academic institutions and information providers is to development a search engine to retrieval information in a super distributed data space like digital university libraries. This begets new challenges to docent community and motivates researchers to look for intelligent information retrieval approach that search and/or filter information automatically. We make an effort in innovation education in this direction and we propose a semantic method for efficient information search. This paper suggests a pragmatic approach to the implementation of intelligent techniques and ontologies for efficient knowledge retrieval in the academic digital libraries

Teaching Innovation in Order to Integrate Self-Learning and Self-Evaluation in the Webct Platform

El concepto de docencia universitaria tradicional se ha visto modificado por los principios definidos en el Espacio Europeo de Educación Superior. Un cambio metodológico que motiva el concepto de autoevaluación y promueve todas aquellas actividades académicas que faciliten el autoaprendizaje. En este escenario, es fundamental que los estudiantes adquieran nuevos hábitos autoformativos. Nuestro trabajo presenta un método de enseñanza-aprendizaje para que ésta pase a ser más activa y participativa. Este proyecto permite al alumno disponer de un conjunto de recursos, que favorecen la autoevaluación y autoformación, a la vez que facilita su trabajo personal y en equipo.In the European Higher Education Area (EHEA) the traditional teaching university concept has changed. EHEA has introduced a methodological change in order to motivate selfevaluation and to promote self learning academic activities. In this way students must acquire new self learning practices. Our work presents a method to make more active and participatory the teaching-learning process. This project provides students different tools in order to promote the self-learning and self-evaluation. A set of teaching resources are presented to facilitate both individual and collective students' work

Increasing the Efficiency of Rule-Based Expert Systems Applied on Heterogeneous Data Sources

Nowadays, the proliferation of heterogeneous data sources provided by different research and innovation projects and initiatives is proliferating more and more and presents huge opportunities. These developments create an increase in the number of different data sources, which could be involved in the process of decisionmaking for a specific purpose, but this huge heterogeneity makes this task difficult. Traditionally, the expert systems try to integrate all information into a main database, but, sometimes, this information is not easily available, or its integration with other databases is very problematic. In this case, it is essential to establish procedures that make a metadata distributed integration for them. This process provides a “mapping” of available information, but it is only at logic level. Thus, on a physical level, the data is still distributed into several resources. In this sense, this chapter proposes a distributed rule engine extension (DREE) based on edge computing that makes an integration of metadata provided by different heterogeneous data sources, applying then a mathematical decomposition over the antecedent of rules. The use of the proposed rule engine increases the efficiency and the capability of rule-based expert systems, providing the possibility of applying these rules over distributed and heterogeneous data sources, increasing the size of data sets that could be involved in the decision-making process

An intelligent alternative approach to the efficient network management

Due to the increasing complexity and heterogeneity of networks and services, many efforts have been made to develop intelligent techniques for management. Network intelligent management is a key technology for operating large heterogeneous data transmission networks. This paper presents a proposal for an architecture that integrates management object specifications and the knowledge of expert systems. We present a new approach named Integrated Expert Management, for learning objects based on expert management rules and describe the design and implementation of an integrated intelligent management platform based on OSI and Internet management models. The main contributions of our approach is the integration of both expert system and managed models, so we can make use of them to construct more flexible intelligent management network. The prototype SONAP (Software for Network Assistant and Performance) is accuracy-aware since it can control and manage a network. We have tested our system on real data to the fault diagnostic in a telecommunication system of a power utility. The results validate the model and show a significant improvement with respect to the number of rules and the error rate in others systems

Integración de inteligencia en la MIB del Modelo OSI para la gestión de redes de telecomunicaciones

La Gestión de red se define como el conjunto de actividades dedicadas al control y vigilancia de los recursos existentes en las redes de telecomunicaciones. En los complejos sistemas actuales, es necesario realizar una gestión de la red asistida por un software avanzado. La Inteligencia Artificial se incorpora a la gestión de las redes, con el fin de facilitar labores de administración y control de toda la información que proviene de los recursos gestionados, dando origen a la Gestión Inteligente de las Redes. Este nuevo paradigma, proporciona a los sistemas de gestión de un mayor grado de cohesión con las tecnologías de comunicaciones actuales, a la vez de disponer de todas las posibilidades y ventajas aportadas por la Inteligencia Artificial. Nuestro estudio tiene como objetivo perfeccionar las técnicas actuales de gestión. Para ello se establecen mecanismos que permiten una mayor correlación entre las especificaciones de la red y las aplicaciones que efectúan el tratamiento de la información de gestión. Presentamos una nueva concepción denominada “Gestión Inteligente Integrada” y una extensión del modelo de gestión OSI, que contempla la inclusión del conocimiento de gestión, en las propias especificaciones de los objetos gestionados. Este modelo consigue reunir conceptos que actualmente pertenecen a distintos ámbitos de estudio, la Inteligencia Artificial y la Información de Gestión del sistema. De esta forma se obtiene una solución global, que permite a los administradores de redes utilizar la potencia aportada por la Inteligencia Artificial, en particular de los Sistemas Expertos, de una forma sencilla y transparente