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

Service discovery and composition : PreDiCtS approach

The proliferation of Web Services is fostering the need for service-discovery and composition tools to provide more personalisation during the service retrieval process. In this paper, we describe the motivating details behind PreDiCtS, a framework for personalised service-retrieval. In our approach we consider that similar service composition problems can be tackled in a similar manner by reusing and adapting past composition best practices or templates. The proposed retrieval process uses a mixed- initiative technique based on Conversational Case-Based Reasoning (CCBR), that provides i) for a clearer identification of the user’s service requirements and ii) based on these requirements, finds suitable service templates that satisfy the user’s goal. We discuss how retrieval can vary through the use of different CCBR algorithms and how adaptation can be performed over the retrieved templates thus providing the personalisation feature in PreDiCtS.peer-reviewe

Retrieval, reuse, revision and retention in case-based reasoning

El original está disponible en www.journals.cambridge.orgCase-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision, and retention.Peer reviewe

Plan recommendation for well engineering.

Good project planning provides the basis for successful offshore well drilling projects. In this domain, planning occurs in two phases: an onshore phase develops a project plan; and an offshore phase implements the plan and tracks progress. The Performance Tracker applies a case-based reasoning approach to support the reuse of project plans. Cases comprise problem parts that store project initiation data, and solution parts that record the tasks and subtasks of actual plans. An initial evaluation shows that nearest neighbour retrieval identifies projects in which the retrieved tasks and subtasks are relevant for the new project. The Performance Tracker can be viewed as a recommender system in which recommendations are plans. Thus the data that is routinely captured as part of the performance tracking during offshore implementation is utilised as experiences. This conference was held in Syracuse, NY

Carbon Footprint Calculator for Malaysian Construction Industry with Case-Based Reasoning

Malaysia's carbon emission level has reached an alarming point. Cnrrently, the country is ranked 30th for the highest amount of carbon emitted by a country. Construction industry alone contributes 24% of the nation's carbon emission. There are various ways of reducing the amount of C02 in the said industry. One of which is through usage of low impact materials in buildings. However, cnrrently there is no existing system such as Carbon Footprint Calculator for the Construction Industry tailoring towards the local needs. This project creates a calculator that can determine the carbon footprint of materials in construction projects embedded with a CaseBased Reasoning (CBR) functionality. The project aims to reduce carbon emission levels for construction activity through better selection and combination of material resulting in lower carbon emission per square feet of a building. Construction firms can ouly make informed and better decision with substantial amount of information such as the total amount of carbon emission per square feet which the system calculates. Furthermore, users of the calculator also have the option to use similar past cases (projects)' s material selection as a solution to a new project to be implemented. Following the prototyping system development life cycle methodology, the software is further refined and tested until the prototype software is fully stable and functioning. The calculator's accuracy is unquestionable and very much reliable through the various test cases conducted on it

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

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

An Ontology-Driven Methodology To Derive Cases From Structured And Unstructured Sources

The problem-solving capability of a Case-Based Reasoning (CBR) system largely depends on the richness of its knowledge stored in the form of cases, i.e. the CaseBase (CB). Populating and subsequently maintaining a critical mass of cases in a CB is a tedious manual activity demanding vast human and operational resources. The need for human involvement in populating a CB can be drastically reduced as case-like knowledge already exists in the form of databases and documents and harnessed and transformed into cases that can be operationalized. Nevertheless, the transformation process poses many hurdles due to the disparate structure and the heterogeneous coding standards used. The featured work aims to address knowledge creation from heterogeneous sources and structures. To meet this end, this thesis presents a Multi-Source Case Acquisition and Transformation Info-Structure (MUSCATI). MUSCATI has been implemented as a multi-layer architecture using state-of-the-practice tools and can be perceived as a functional extension to traditional CBR-systems. In principle, MUSCATI can be applied in any domain but in this thesis healthcare was chosen. Thus, Electronic Medical Records (EMRs) were used as the source to generate the knowledge. The results from the experiments showed that the volume and diversity of cases improves the reasoning outcome of the CBR engine. The experiments showed that knowledge found in medical records (regardless of structure) can be leveraged and standardized to enhance the (medical) knowledge of traditional medical CBR systems. Subsequently, the Google search engine proved to be very critical in “fixing” and enriching the domain ontology on-the-fly

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998