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

CCBR-Driven Business Process Evolution

Process-aware information systems (PAIS) allow coordinating the execution of business processes by providing the right tasks to the right people at the right time. In order to support a broad spectrum of business processes, PAIS must be flexible at run-time. Ad-hoc deviations from the predefined process schema as well as the quick adaptation of the process schema itself due to changes of the underlying business processes must be supported. This paper presents an integrated approach combining the concepts and methods provided by the process management systems ADEPT and CBRFlow. Integrating these two systems enables ad-hoc modifications of single process instances, the memorization of these modifications using conversational case-based reasoning, and their reuse in similar future situations. In addition, potential process type changes can be derived from cases when similar ad-hoc modifications at the process instance level occur frequently

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

A viewpoint-based case-based reasoning approach utilising an enterprise architecture ontology for experience management

The accessibility of project knowledge obtained from experiences is an important and crucial issue in enterprises. This information need about project knowledge can be different from one person to another depending on the different roles he or she has. Therefore, a new ontology-based case-based reasoning (OBCBR) approach that utilises an enterprise ontology is introduced in this article to improve the accessibility of this project knowledge. Utilising an enterprise ontology improves the case-based reasoning (CBR) system through the systematic inclusion of enterprisespecific knowledge. This enterprise-specific knowledge is captured using the overall structure given by the enterprise ontology named ArchiMEO, which is a partial ontological realisation of the enterprise architecture framework (EAF) ArchiMate. This ontological representation, containing historical cases and specific enterprise domain knowledge, is applied in a new OBCBR approach. To support the different information needs of different stakeholders, this OBCBR approach has been built in such a way that different views, viewpoints, concerns and stakeholders can be considered. This is realised using a case viewpoint model derived from the ISO/IEC/IEEE 42010 standard. The introduced approach was implemented as a demonstrator and evaluated using an application case that has been elicited from a business partner in the Swiss research project.This work was supported in part by the Commission for Technology and Innovation (CTI) of the Swiss Confederation under Grant 14575.1 PFES-ES and the ELO Digital Office CH AG.http://www.tandfonline.com/loi/teis202018-04-30hb2017Information Scienc

Reutilization of diagnostic cases by adaptation of knowledge models.

International audienceThis paper deals with design of knowledge oriented diagnostic system. Two challenges are addressed. The first one concerns the elicitation of expert practice and the proposition of a methodology for developing four knowledge containers of case based reasoning system. The second one concerns the proposition of a general adaptation phase to reuse case solving diagnostic problems in a different context. In most cases, adaptation methods are application-specific and the challenge in this work is to make a general adaptation method for the field of industrial diagnostics applications. This paper is a contribution to fill this gap in the field of fault diagnostic and repair assistance of equipment. The proposed adaptation algorithm relies on hierarchy descriptors, an implied context model and dependencies between problems and solutions of the source cases. In addition, one can note that the first retrieved case is not necessarily the most adaptable case, and to take into account this report, an adaptation-guided retrieval step based on a similarity measure associated with an adaptation measure is realized on the diagnostic problem. These two measures allow selecting the most adaptable case among the retrieved cases. The two retrieval and adaptation phases are applied on real industrial system called Supervised industrial system of Transfer of pallets (SISTRE)

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

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