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

Challenges in distributed information search in a semantic digital library

Nowadays an enormous quantity of heterogeneous and distributed information is stored in the current digital libraries. Access to these collections poses a serious challenge, however, because present search techniques based on manually annotated metadata and linear replay of material selected by the user do not scale effectively or efficiently to large collections. The artificial intelligent and semantic Web provides a common framework that allows knowledge to be shared and reused. In this paper we propose a comprehensive approach for discovering information objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We suggest a conceptual architecture for a semantic and intelligent search engine. OntoFAMA is a collaborative effort that proposes a new form of interaction between people and Digital Library, where the latter is adapted to individuals and their surroundings. We have used Case Based-Reasoning methodology to develop a prototype for supporting efficient retrieval knowledge from digital library of Seville University

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

Sistema de apoio à solução de não-conformidades: um estudo de caso na extrusão de alumínio

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Mecânica, Florianópolis, 2014Esta tese investiga o uso da abordagem de sistemas multiagentes (SMA) buscando compartilhar e recuperar conhecimentos decorrentes da solução de problemas prévios de não-conformidades e da aplicação do método de análise de modos de falha e efeitos em processos de manufatura (PFMEA) e raciocínio baseado em casos. Neste sentido, propõe-se um modelo de multiagentes em apoio à solução de problemas de não-conformidades em processos de fabricação, com o intuito de superar não somente as dificuldades relacionadas à natureza do conhecimento, mas também quanto à sua distribuição. A distribuição adotada no modelo considera tanto o aspecto geográfico das fontes quanto à fragmentação relacionada aos processos existentes na cadeia produtiva. Nesta ótica, são considerados agentes computacionais cujo comportamento inclui o uso de raciocínio baseado em casos e métodos de recuperação baseado em ontologias. Por fim, um protótipo computacional foi desenvolvido para permitir a verificação e a validação do modelo proposto, sendo que as bases de conhecimento manipuladas pelo sistema são instanciadas com conhecimentos no domínio do processo de extrusão de alumínio obtidos a partir da literatura e de pesquisas de campo em uma empresa que fabrica peças por extrusão de alumínio, com ênfase na liga 60xx.Abstract: This thesis investigates the use of the multi-agent systems (MAS) approach seeking to share and retrieve knowledge from previous solutions of nonconformance problems and the application of the method of failure modes and effects analysis in manufacturing processes (PFMEA) and case-based reasoning (CBR). In this sense, it is proposed a MAS-based model to support the solution of nonconformance problems in manufacturing processes in order to overcome the difficulties related to both the nature of knowledge and on its distribution. The distribution adopted in the model considers both the geographical aspect of the sources and the fragmentation related to existing processes in the production chain. From this perspective, agents were developed whose behavior includes the use of case-based reasoning and retrieval methods based on ontologies. Finally, a software prototype was developed to allow the verification and validation of the proposed model, and the foundations of knowledge manipulated by the system are instantiated with knowledge in the field of the aluminum extrusion process obtained from the literature and from a company that manufactures parts via aluminum extrusion, with emphasis on the 60xx alloy

Case-based decision support system for breast cancer management

Breast cancer is identified as the most common type of cancer in women worldwide with 1.6 million women around the world diagnosed every year. This prompts many active areas of research in identifying better ways to prevent, detect, and treat breast cancer. DESIREE is a European Union funded project, which aims at developing a web-based software ecosystem for the multidisciplinary management of primary breast cancer. The development of an intelligent clinical decision support system offering various modalities of decision support is one of the key objectives of the project. This paper explores case-based reasoning as a problem solving paradigm and discusses the use of an explicit domain knowledge ontology in the development of a knowledge-intensive case-based decision support system for breast cancer management

COBRA : Une plate-forme de RàPC basée sur des ontologies

International audienceCet article présente un projet en cours qui a pour objectif de développer une plate-forme de RàPC pour le diagnostic basée sur des ontologies, appelée COBRA. Cette plate-forme est constituée de deux parties principales : les modèles de connaissances décrits par des ontologies, et les processus de raisonnement. Nous travaillons actuellement sur la défaillance des barrières de sécurité installées sur des sites industriels. Cependant, notre objectif est de rendre la plate-forme générique et indépendante du domaine d'application. Nous affirmons que, pour mieux exploiter les avantages des ontologies dans les systèmes de RàPC, il est important de pouvoir utiliser n'importe quel concept dans la description des cas. Ainsi, COBRA permet de définir les attributs de chaque cas dynamiquement au moment de l'exécution, ce qui conduit à une base de cas hétérogène. Dans cet article, nous présentons l'architecture de la plate-forme, les modèles de connaissances, les processus principaux, ainsi que les problèmes rencontrés en travaillant avec des cas hétérogènes

An Intelligent Help-Desk Framework for Effective Troubleshooting

Nowadays, technological infrastructure requires an intelligent virtual environment based on decision processes. These processes allow the coordination of individual elements and the tasks that connect them. Thus, incident resolution must be efficient and effective to achieve maximum productivity. In this paper, we present the design and implementation of an intelligent decision-support system applied in technology infrastructure at the University of Seville (Spain). We have used a Case Based Reasoning (CBR) methodology and an ontology to develop an intelligent system for supporting expert diagnosis and intelligent management of incidents. This is an innovative and interdisciplinary approach to knowledge management in problem-solving processes that are related to environmental issues. Our system provides an automatic semantic indexing for the generating of question/answer pairs, a case based reasoning technique for finding similar questions, and an integration of external information sources via ontologies. A real ontology-based question/answer platform named ExpertSOS is presented as a proof of concept. The intelligent diagnosis platform is able to identify and isolate the most likely cause of infrastructure failure in case of a faulty operation

Integrating case based reasoning and geographic information systems in a planing support system: Çeşme Peninsula study

Thesis (Doctoral)--Izmir Institute of Technology, City and Regional Planning, Izmir, 2009Includes bibliographical references (leaves: 110-121)Text in English; Abstract: Turkish and Englishxii, 140 leavesUrban and regional planning is experiencing fundamental changes on the use of of computer-based models in planning practice and education. However, with this increased use, .Geographic Information Systems. (GIS) or .Computer Aided Design.(CAD) alone cannot serve all of the needs of planning. Computational approaches should be modified to deal better with the imperatives of contemporary planning by using artificial intelligence techniques in city planning process.The main aim of this study is to develop an integrated .Planning Support System. (PSS) tool for supporting the planning process. In this research, .Case Based Reasoning. (CBR) .an artificial intelligence technique- and .Geographic Information Systems. (GIS) .geographic analysis, data management and visualization techniqueare used as a major PSS tools to build a .Case Based System. (CBS) for knowledge representation on an operational study. Other targets of the research are to discuss the benefits of CBR method in city planning domain and to demonstrate the feasibility and usefulness of this technique in a PSS. .Çeşme Peninsula. case study which applied under the desired methodology is presented as an experimental and operational stage of the thesis.This dissertation tried to find out whether an integrated model which employing CBR&GIS could support human decision making in a city planning task. While the CBS model met many of predefined goals of the thesis, both advantages and limitations have been realized from findings when applied to the complex domain such as city planning