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

Embedding defeasible argumentation in the semantic web: an ontology-based approach

The SemanticWeb is a project intended to create a universal medium for information exchange by giving semantics to the content of documents on the Web by means of ontology definitions. Ontologies intended for knowledge representation in intelligent agents rely on common-sense reasoning formalizations. Defeasible argumentation has emerged as a successful approach to model common-sense reasoning. Recent research has linked argumentation with belief revision in order to model the dynamics of knowledge. This paper outlines an approach which combines ontologies, argumentation and belief revision by defining an ontology algebra. We suggest how different aspects of ontology integration can be defined in terms of defeasible argumentation and belief revision.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Corporate Smart Content Evaluation

Nowadays, a wide range of information sources are available due to the evolution of web and collection of data. Plenty of these information are consumable and usable by humans but not understandable and processable by machines. Some data may be directly accessible in web pages or via data feeds, but most of the meaningful existing data is hidden within deep web databases and enterprise information systems. Besides the inability to access a wide range of data, manual processing by humans is effortful, error-prone and not contemporary any more. Semantic web technologies deliver capabilities for machine-readable, exchangeable content and metadata for automatic processing of content. The enrichment of heterogeneous data with background knowledge described in ontologies induces re-usability and supports automatic processing of data. The establishment of “Corporate Smart Content” (CSC) - semantically enriched data with high information content with sufficient benefits in economic areas - is the main focus of this study. We describe three actual research areas in the field of CSC concerning scenarios and datasets applicable for corporate applications, algorithms and research. Aspect- oriented Ontology Development advances modular ontology development and partial reuse of existing ontological knowledge. Complex Entity Recognition enhances traditional entity recognition techniques to recognize clusters of related textual information about entities. Semantic Pattern Mining combines semantic web technologies with pattern learning to mine for complex models by attaching background knowledge. This study introduces the afore-mentioned topics by analyzing applicable scenarios with economic and industrial focus, as well as research emphasis. Furthermore, a collection of existing datasets for the given areas of interest is presented and evaluated. The target audience includes researchers and developers of CSC technologies - people interested in semantic web features, ontology development, automation, extracting and mining valuable information in corporate environments. The aim of this study is to provide a comprehensive and broad overview over the three topics, give assistance for decision making in interesting scenarios and choosing practical datasets for evaluating custom problem statements. Detailed descriptions about attributes and metadata of the datasets should serve as starting point for individual ideas and approaches

A model-driven approach to the conceptual modeling of situations : from specification to validation

A modelagem de situações para aplicações sensíveis ao contexto, também chamadas de aplicações sensíveis a situações, é, por um lado, uma tarefa chave para o funcionamento adequado dessas aplicações. Por outro lado, essa também é uma tafera árdua graças à complexidade e à vasta gama de tipos de situações possíveis. Com o intuito de facilitar a representação desses tipos de situações em tempo de projeto, foi criada a Linguagem de Modelagem de Situações (Situation Modeling Language - SML), a qual se baseia parcialmente em ricas teorias ontológicas de modelagem conceitual, além de fornecer uma plataforma de detecção de situação em tempo de execução. Apesar do benefício da existência dessa infraestrutura, a tarefa de definir tipos de situação é ainda não-trivial, podendo carregar problemas que dificilmente são detectados por modeladores via inspeções manuais. Esta dissertação tem o propósito de melhorar e facilitar ainda mais a definição de tipos de situação em SML propondo: (i) uma maior integração da linguagem com as teorias ontológicas de modelagem conceitual pelo uso da linguagem OntoUML, visando aumentar a expressividade dos modelos de situação; e (ii) uma abordagem para validação de tipos de situação usando um método formal, visando garantir que os modelos criados correspondam à intenção do modelador. Tanto a integração quanto a validação são implementadas em uma ferramenta para especificação, verificação e validação de tipos de situação ontologicamente enriquecidos.The modeling of situation types for context-aware applications, also called situationaware applications, is, on the one hand, a key task to the proper functioning of those applications. On the other hand, it is also a hard task given the complexity and the wide range of possible situation types. Aiming at facilitating the representation of those types of situations at design-time, the Situation Modeling Language (SML) was created. This language is based partially on rich ontological theories of conceptual modeling and is accompanied by a platform for situation-detection at runtime. Despite the benefits of the availability of this suitable infrastructure, the definition of situation types, being a non-trivial task, can still pose problems that are hardly detected by modelers by manual model inspection. This thesis aims at improving and facilitating the definition of situation types in SML by proposing: (i) the integration between the language and the ontological theories of conceptual modeling by using the OntoUML language, with the purpose of increasing the expressivity of situation type models; and (ii) an approach for the validation of situation type models using a lightweight formal method, aiming at increasing the correspondence between the created models’ instances and the modeler’s intentions. Both the integration and the validation are implemented in a tool for specification, verification and validation of ontologically-enriched situation types.CAPE