SWA-KMDLS: An Enhanced e-Learning Management System Using Semantic Web and Knowledge Management Technology

In this era of knowledge economy in which knowledge have become the most precious resource, surveys have shown that e-Learning has been on the increasing trend in various organizations including, among others, education and corporate. The use of e-Learning is not only aim to acquire knowledge but also to maintain competitiveness and advantages for individuals or organizations. However, the early promise of e-Learning has yet to be fully realized, as it has been no more than a handout being published online, coupled with simple multiple-choice quizzes. The emerging of e-Learning 2.0 that is empowered by Web 2.0 technology still hardly overcome common problem such as information overload and poor content aggregation in a highly increasing number of learning objects in an e-Learning Management System (LMS) environment. The aim of this research study is to exploit the Semantic Web (SW) and Knowledge Management (KM) technology; the two emerging and promising technology to enhance the existing LMS. The proposed system is named as Semantic Web Aware-Knowledge Management Driven e-Learning System (SWA-KMDLS). An Ontology approach that is the backbone of SW and KM is introduced for managing knowledge especially from learning object and developing automated question answering system (Aquas) with expert locator in SWA-KMDLS. The METHONTOLOGY methodology is selected to develop the Ontology in this research work. The potential of SW and KM technology is identified in this research finding which will benefit e-Learning developer to develop e-Learning system especially with social constructivist pedagogical approach from the point of view of KM framework and SW environment. The (semi-) automatic ontological knowledge base construction system (SAOKBCS) has contributed to knowledge extraction from learning object semiautomatically whilst the Aquas with expert locator has facilitated knowledge retrieval that encourages knowledge sharing in e-Learning environment. The experiment conducted has shown that the SAOKBCS can extract concept that is the main component of Ontology from text learning object with precision of 86.67%, thus saving the expert time and effort to build Ontology manually. Additionally the experiment on Aquas has shown that more than 80% of users are satisfied with answers provided by the system. The expert locator framework can also improve the performance of Aquas in the future usage. Keywords: semantic web aware – knowledge e-Learning Management System (SWAKMDLS), semi-automatic ontological knowledge base construction system (SAOKBCS), automated question answering system (Aquas), Ontology, expert locator

Customisable e-training programmes based on trainees profiles

Dissertation presented at Faculdade de Ciências e Tecnologia of Universidade Nova de Lisboa to obtain the Master degree in Electrical and Computer EngineeringOnline training (e-training) is a major driver to promote the development of competencies and knowledge in enterprises. A lack of customizable e-training programmes based on trainees‟ profiles and of continuous maintenance of the training materials prevents the sustainability of industrial training deployment. This dissertation presents a training strategy and a methodology for building training courses with the purpose to provide a trainee oriented industrial training development. The training strategy intends to facilitate the management of all the training components and tasks to be able to build a training structure focused in a specific planned objective. The methodology for building e-training courses proposes to create customizable training materials in an easier way, enabling various organizations to participate actively on its production. Additionally a customisable training programme framework is presented. It is supported by a compliant ontology-based model able to support adaptable training contents, orchestration service, facilitating the efficiency and acceptance of the e-training programmes delivery

Model driven validation approach for enterprise architecture and motivation extensions

As the endorsement of Enterprise Architecture (EA) modelling continues to grow in diversity and complexity, management of its schema, artefacts, semantics and relationships has become an important business concern. To maintain agility and flexibility within competitive markets, organizations have also been compelled to explore ways of adjusting proactively to innovations, changes and complex events also by use of EA concepts to model business processes and strategies. Thus the need to ensure appropriate validation of EA taxonomies has been considered severally as an essential requirement for these processes in order to exert business motivation; relate information systems to technological infrastructure. However, since many taxonomies deployed today use widespread and disparate modelling methodologies, the possibility to adopt a generic validation approach remains a challenge. The proliferation of EA methodologies and perspectives has also led to intricacies in the formalization and validation of EA constructs as models often times have variant schematic interpretations. Thus, disparate implementations and inconsistent simulation of alignment between business architectures and heterogeneous application systems is common within the EA domain (Jonkers et al., 2003). In this research, the Model Driven Validation Approach (MDVA) is introduced. MDVA allows modelling of EA with validation attributes, formalization of the validation concepts and transformation of model artefacts to ontologies. The transformation simplifies querying based on motivation and constraints. As the extended methodology is grounded on the semiotics of existing tools, validation is executed using ubiquitous query language. The major contributions of this work are the extension of a metamodel of Business Layer of an EAF with Validation Element and the development of EAF model to ontology transformation Approach. With this innovation, domain-driven design and object-oriented analysis concepts are applied to achieve EAF model’s validation using ontology querying methodology. Additionally, the MDVA facilitates the traceability of EA artefacts using ontology graph patterns

Intentional dialogues in multi-agent systems based on ontologies and argumentation

Some areas of application, for example, healthcare, are known to resist the replacement of human operators by fully autonomous systems. It is typically not transparent to users how artificial intelligence systems make decisions or obtain information, making it difficult for users to trust them. To address this issue, we investigate how argumentation theory and ontology techniques can be used together with reasoning about intentions to build complex natural language dialogues to support human decision-making. Based on such an investigation, we propose MAIDS, a framework for developing multi-agent intentional dialogue systems, which can be used in different domains. Our framework is modular so that it can be used in its entirety or just the modules that fulfil the requirements of each system to be developed. Our work also includes the formalisation of a novel dialogue-subdialogue structure with which we can address ontological or theory-of-mind issues and later return to the main subject. As a case study, we have developed a multi-agent system using the MAIDS framework to support healthcare professionals in making decisions on hospital bed allocations. Furthermore, we evaluated this multi-agent system with domain experts using real data from a hospital. The specialists who evaluated our system strongly agree or agree that the dialogues in which they participated fulfil Cohen’s desiderata for task-oriented dialogue systems. Our agents have the ability to explain to the user how they arrived at certain conclusions. Moreover, they have semantic representations as well as representations of the mental state of the dialogue participants, allowing the formulation of coherent justifications expressed in natural language, therefore, easy for human participants to understand. This indicates the potential of the framework introduced in this thesis for the practical development of explainable intelligent systems as well as systems supporting hybrid intelligence

An approach for joint estimation of physical and logical security by semantic modelling

Key activities in critical systems are the monitoring, observation and comprehension of different phenomena, aimed at providing an updated and meaningful description of the monitored scenario, as well as its possible evolutions, to enable proper decisions and countermeasures for the protection and safety of people and things. The threats coming from many different sources, internally and externally. The diffusion of new technologies have made more accessible the assets of a system. In this thesis we demonstrate that the use of a semantic model for the information management it is suitable in order to meet these issues. In particular, thesis proposes and implement a methodology and approach for the early situation awareness recognizing a threat situation on time, for decision support to automatically activate recovery strategies. The threat on which the thesis focus on are regarded the logical and physical security. In particular for the logical security estimation will be presented a an approach guided by metrics. Then will be presented some results and example of real application

Knowledge Components and Methods for Policy Propagation in Data Flows

Data-oriented systems and applications are at the centre of current developments of the World Wide Web (WWW). On the Web of Data (WoD), information sources can be accessed and processed for many purposes. Users need to be aware of any licences or terms of use, which are associated with the data sources they want to use. Conversely, publishers need support in assigning the appropriate policies alongside the data they distribute. In this work, we tackle the problem of policy propagation in data flows - an expression that refers to the way data is consumed, manipulated and produced within processes. We pose the question of what kind of components are required, and how they can be acquired, managed, and deployed, to support users on deciding what policies propagate to the output of a data-intensive system from the ones associated with its input. We observe three scenarios: applications of the Semantic Web, workflow reuse in Open Science, and the exploitation of urban data in City Data Hubs. Starting from the analysis of Semantic Web applications, we propose a data-centric approach to semantically describe processes as data flows: the Datanode ontology, which comprises a hierarchy of the possible relations between data objects. By means of Policy Propagation Rules, it is possible to link data flow steps and policies derivable from semantic descriptions of data licences. We show how these components can be designed, how they can be effectively managed, and how to reason efficiently with them. In a second phase, the developed components are verified using a Smart City Data Hub as a case study, where we developed an end-to-end solution for policy propagation. Finally, we evaluate our approach and report on a user study aimed at assessing both the quality and the value of the proposed solution

Un Intergiciel de Gestion du Contexte basé Multi-Agent pour les Applications d'Intelligence Ambiante

The complexity and magnitude of Ambient Intelligence scenarios imply that attributes such as modeling expressiveness, flexibility of representation and deployment, as well as ease of configuration and development become central features for context management systems.However, existing works in the literature seem to explore these development-oriented attributes at a low degree.Our goal is to create a flexible and well configurable context management middleware, able to respond to different scenarios. To this end, our solution is built on the basis of principles and techniques of the Semantic Web and Multi-Agent Systems.We use the Semantic Web to provide a new context meta-model, allowing for an expressive and extensible modeling of content, meta-properties (e.g. temporal validity, quality parameters) and dependencies (e.g. integrity constraints).In addition, we develop a middleware architecture that relies on Multi-Agent Systems and a service component based design. Each agent of the system encapsulates a functional aspect of the context provisioning processes (acquisition, coordination, distribution, use).We introduce a new way to structure the deployment of agents depending on the multi-dimensionality aspects of the application's context model. Furthermore, we develop declarative policies governing the adaptation behavior of the agents managing the provisioning of context information.Simulations of an intelligent university scenario show that appropriate tooling built around our middleware can provide significant advantages in the engineering of context-aware applications.La complexité et l'ampleur des scénarios de l'Intelligence Ambiante impliquent que des attributs tels que l'expressivité de modelisation, la flexibilité de representation et de deploiement et la facilité de configuration et de developpement deviennent des caracteristiques centrales pour les systèmes de gestion de contexte. Cependant, les ouvrages existants semblent explorer ces attributs orientés-developpement a un faible degré.Notre objectif est de créer un intergiciel de gestion de contexte flexible et bien configurable, capable de répondre aux différents scenarios. A cette fin, notre solution est construite a base de techniques et principes du Web Semantique (WS) et des systèmes multi-agents (SMA).Nous utilisons le WS pour proposer un noveau meta-modèle de contexte, permettant une modelisation expressive et extensible du contenu, des meta-proprietés (e.g. validité temporelle, parametres de qualité) et des dépendances (e.g. les contraintes d'integrité) du contexte.De plus, une architecture a base de SMA et des composants logiciels, ou chaque agent encapsule un aspect fonctionnel du processus de gestion de contexte (acquisition, coordination, diffusion, utilisation) est developpée.Nous introduisons un nouveau moyen de structurer le deploiement d'agents selon les dimensions du modèle de contexte de l'application et nous elaborons des politiques déclaratives gouvernant le comportement d'adaptation du provisionnement contextuel des agents. Des simulations d'un scenario d'université intelligente montrent que un bon outillage construit autour de notre intergiciel peut apporter des avantages significatifs dans la génie des applications sensibles au contexte

SWA-KMDLS: An Enhanced e-Learning Management System Using Semantic Web and Knowledge Management Technology

In this era of knowledge economy in which knowledge have become the most precious resource, surveys have shown that e-Learning has been on the increasing trend in various organizations including, among others, education and corporate. The use of e-Learning is not only aim to acquire knowledge but also to maintain competitiveness and advantages for individuals or organizations. However, the early promise of e-Learning has yet to be fully realized, as it has been no more than a handout being published online, coupled with simple multiple-choice quizzes. The emerging of e-Learning 2.0 that is empowered by Web 2.0 technology still hardly overcome common problem such as information overload and poor content aggregation in a highly increasing number of learning objects in an e-Learning Management System (LMS) environment. The aim of this research study is to exploit the Semantic Web (SW) and Knowledge Management (KM) technology; the two emerging and promising technology to enhance the existing LMS. The proposed system is named as Semantic Web Aware-Knowledge Management Driven e-Learning System (SWA-KMDLS). An Ontology approach that is the backbone of SW and KM is introduced for managing knowledge especially from learning object and developing automated question answering system (Aquas) with expert locator in SWA-KMDLS. The METHONTOLOGY methodology is selected to develop the Ontology in this research work. The potential of SW and KM technology is identified in this research finding which will benefit e-Learning developer to develop e-Learning system especially with social constructivist pedagogical approach from the point of view of KM framework and SW environment. The (semi-) automatic ontological knowledge base construction system (SAOKBCS) has contributed to knowledge extraction from learning object semiautomatically whilst the Aquas with expert locator has facilitated knowledge retrieval that encourages knowledge sharing in e-Learning environment. The experiment conducted has shown that the SAOKBCS can extract concept that is the main component of Ontology from text learning object with precision of 86.67%, thus saving the expert time and effort to build Ontology manually. Additionally the experiment on Aquas has shown that more than 80% of users are satisfied with answers provided by the system. The expert locator framework can also improve the performance of Aquas in the future usage. Keywords: semantic web aware – knowledge e-Learning Management System (SWAKMDLS), semi-automatic ontological knowledge base construction system (SAOKBCS), automated question answering system (Aquas), Ontology, expert locator

Connected Information Management

Society is currently inundated with more information than ever, making efficient management a necessity. Alas, most of current information management suffers from several levels of disconnectedness: Applications partition data into segregated islands, small notes don’t fit into traditional application categories, navigating the data is different for each kind of data; data is either available at a certain computer or only online, but rarely both. Connected information management (CoIM) is an approach to information management that avoids these ways of disconnectedness. The core idea of CoIM is to keep all information in a central repository, with generic means for organization such as tagging. The heterogeneity of data is taken into account by offering specialized editors. The central repository eliminates the islands of application-specific data and is formally grounded by a CoIM model. The foundation for structured data is an RDF repository. The RDF editing meta-model (REMM) enables form-based editing of this data, similar to database applications such as MS access. Further kinds of data are supported by extending RDF, as follows. Wiki text is stored as RDF and can both contain structured text and be combined with structured data. Files are also supported by the CoIM model and are kept externally. Notes can be quickly captured and annotated with meta-data. Generic means for organization and navigation apply to all kinds of data. Ubiquitous availability of data is ensured via two CoIM implementations, the web application HYENA/Web and the desktop application HYENA/Eclipse. All data can be synchronized between these applications. The applications were used to validate the CoIM ideas