1,607 research outputs found
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
Integration of decision support systems to improve decision support performance
Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes
Knowledge formalization in experience feedback processes : an ontology-based approach
Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable
Knowledge modelling with the open source tool myCBR
Building knowledge intensive Case-Based Reasoning applications requires tools that support this on-going process between domain experts and knowledge engineers. In this paper we will introduce how the open source tool myCBR 3 allows for flexible knowledge elicitation and formalisation form CBR and non CBR experts. We detail on myCBR 3 's versatile approach to similarity modelling and will give an overview of the Knowledge Engineering workbench, providing the tools for the modelling
process. We underline our presentation with three case studies of knowledge modelling for technical diagnosis and recommendation systems
using myCBR 3
Intelligent Knowledge Retrieval from Industrial Repositories
Actually, a large amount of information is stored in the industrial repositories. Accessing this information is complicated, and the techniques currently used in metadata and the material chosen by the user do not scale efficiently in large collections. The semantic Web provides a frame of reference that allows sharing and reusing knowledge efficiently. In our work, we present a focus for discovering information in digital repositories based on the application of expert system technologies, and we show a conceptual architecture for a semantic search engine. We used case-based reasoning methodology to create a prototype that supports efficient retrieval knowledge from digital repositories. OntoEnter is a collaborative effort that proposes a new form of interaction between users and digital enterprise repositories, where the latter are adapted to users and their surroundings
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
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
An Intelligent Methodology for Modeling Semantic Knowledge in Industrial Networks
Networks has been involved in Industrial and IoT Applications for decades, creating new
opportunities for more personalized services, improved security, greater automation and operational efficiency.
Industry and businesses who prioritize and modernize their analytics strategy and technology to monetize their
data will lead and succeed in our data-driven world. The network now provides even more detailed information
through units and equipment databases, which provide details about the installed equipment, including models,
designed capacity, performance and start / stop dates of the switches, routers, etc. repositories, digital files and
business websites. Access to these collections is a serious challenge. Artificial intelligence and the Semantic
Web provide a common framework for sharing and reusing knowledge in an efficient way. This article explores
the architecture of intelligent agents to make the argument of an intelligent solution as opposed to traditional
methods. We propose a new paradigm in which the intelligent management of the network is integrated into the
conceptual repository of management information. This study focuses on an intelligent framework and
language to formalize knowledge management descriptions and combine them with the existing SNMP
management model. Based on the present proposal and the Internet management model, we describe the design
and implementation of an integrated intelligent management platform called OntoNetwork
An Integrated Content and Metadata based Retrieval System for Art
In this paper we describe aspects of the Artiste project to develop a distributed content and metadata based analysis, retrieval and navigation system for a number of major European Museums. In particular, after a brief overview of the complete system, we describe the design and evaluation of some of the image analysis algorithms developed to meet the specific requirements of the users from the museums. These include a method for retrievals based on sub images, retrievals based on very low quality images and retrieval using craquelure type
Recommended from our members
An intelligent framework for dynamic web services composition in the semantic web
As Web services are being increasingly adopted as the distributed computing technology of choice to securely publish application services beyond the firewall, the importance of composing them to create new, value-added service, is increasing. Thus far, the most successful practical approach to Web services composition, largely endorsed by the industry falls under the static composition category where the service selection and flow management are done a priori and manually. The second approach to web-services composition aspires to achieve more dynamic composition by semantically describing the process model of Web services and thus making it comprehensible to reasoning engines or software agents. The practical implementation of the dynamic composition approach is still in its infancy and many complex problems need to be resolved before it can be adopted outside the research communities.
The investigation of automatic discovery and composition of Web services in this thesis resulted in the development of the eXtended Semantic Case Based Reasoner (XSCBR), which utilizes semantic web and AI methodology of Case Based Reasoning (CBR). Our framework uses OWL semantic descriptions extensively for implementing both the matchmaking profiles of the Web services and the components of the CBR engine.
In this research, we have introduced the concept of runtime behaviour of services and consideration of that in Web services selection. The runtime behaviour of a service is a result of service execution and how the service will behave under different circumstances, which is difficult to presume prior to service execution. Moreover, we demonstrate that the accuracy of automatic matchmaking of Web services can be further improved by taking into account the adequacy of past matchmaking experiences for the requested task. Our XSCBR framework allows annotating such runtime experiences in terms of storing execution values of non-functional Web services parameters such as availability and response time into a case library. The XSCBR algorithm for matchmaking and discovery considers such stored Web services execution experiences to determine the adequacy of services for a particular task.
We further extended our fundamental discovery and matchmaking algorithm to cater for web services composition. An intensive knowledge-based substitution approach was proposed to adapt the candidate service experiences to the requested solution before suggesting more complex and computationally taxing AI-based planning-based transformations. The inconsistency problem that occurs while adapting existing service composition solutions is addressed with a novel methodology based on Constraint Satisfaction Problem (CSP).
From the outset, we adopted a pragmatic approach that focused on delivering an automated Web services discovery and composition solution with the minimum possible involvement of all composition participants: the service provider, the requestor and the service composer. The qualitative evaluation of the framework and the composition tools, together with the performance study of the XSCBR framework has verified that we were successful in achieving our goal
- …