Ontology-based feature modeling: an empirical study in changing scenarios

A software product line (SPL) is a set of software systems that have a particular set of common features\ud and that satisfy the needs of a particular market segment or mission. Feature modeling is one of the key\ud activities involved in the design of SPLs. The feature diagram produced in this activity captures the commonalities\ud and variabilities of SPLs. In some complex domains (e.g., ubiquitous computing, autonomic\ud systems and context-aware computing), it is difficult to foresee all functionalities and variabilities a\ud specific SPL may require. Thus, Dynamic Software Product Lines (DSPLs) bind variation points at runtime\ud to adapt to fluctuations in user needs as well as to adapt to changes in the environment. In this context,\ud relying on formal representations of feature models is important to allow them to be automatically analyzed\ud during system execution. Among the mechanisms used for representing and analyzing feature\ud models, description logic (DL) based approaches demand to be better investigated in DSPLs since it provides\ud capabilities, such as automated inconsistency detection, reasoning efficiency, scalability and\ud expressivity. Ontology is the most common way to represent feature models knowledge based on DL reasoners.\ud Previous works conceived ontologies for feature modeling either based on OWL classes and properties\ud or based on OWL individuals. However, considering change or evolution scenarios of feature\ud models, we need to compare whether a class-based or an individual-based feature modeling style is\ud recommended to describe feature models to support SPLs, and especially its capabilities to deal with\ud changes in feature models, as required by DSPLs. In this paper, we conduct a controlled experiment to\ud empirically compare two approaches based on each one of these modeling styles in several changing scenarios\ud (e.g., add/remove mandatory feature, add/remove optional feature and so on). We measure time to\ud perform changes, structural impact of changes (flexibility) and correctness for performing changes in our\ud experiment. Our results indicate that using OWL individuals requires less time to change and is more\ud flexible than using OWL classes and properties. These results provide insightful assumptions towards\ud the definition of an approach relying on reasoning capabilities of ontologies that can effectively support\ud products reconfiguration in the context of DSPL.CNPqCAPE

Exploiting general-purpose background knowledge for automated schema matching

The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process. In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources. A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems. One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented. In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications

Authoring gamified intelligent tutoring systems.

Sistemas Tutores Inteligentes (STIs) têm recibo a atenção de acadêmicos e profissionais desde da década de 70. Tem havido um grande número de estudos recentes em apoio da efetividade de STIs. Entretanto, é muito comum que estudantes fiquem desengajados ou entediados durante o processo de aprendizagem usando STIs. Para considerar explicitamente os aspectos motivacionais de estudantes, pesquisadores estão cada vez mais interessados em usar gamificação em conjunto com STIs. Contudo, apesar de prover tutoria individualizada para estudantes e algum tipo de suporte para professores, estes usuários não têm recebido alta prioridade no desenvolvimento destes tipos de sistemas. De forma a contribuir para o uso ativo e personalizado de STIs gamificados por professores, três problemas técnicos devem ser considerados. Primeiro, projetar STI é muito complexo (deve-se considerar diferentes teorias, componentes e partes interessadas) e incluir gamificação pode aumentar significativamente tal complexidade e variabilidade. Segundo, as funcionalidades de STIs gamificados podem ser usadas de acordo com vários elementos (ex.: nível educacional, domínio de conhecimento, teorias de gamificaçãoe STI, etc). Desta forma, é imprescindível tirar proveito das teorias e práticas de ambos os tópicos para reduzir o espaço de design destes sistemas. Terceiro, para efetivamente auxiliar professores a usarem ativamente estes sistemas, faz-se necessário prover uma solução simples e usável para eles. Para lidar com estes problemas, o principal objetivo desta tese é projetar uma solução computacional de autoria para fornecer aos professores uma forma de personalizar as funcionalidades de STIs gamificados gerenciando a alta variabilidade destes sistemas e considerando as teorias/práticas de gamificação e STI. Visando alcançar este objetivo, nós identificamos o espaço de variabilidade e o representamos por meio do uso de uma abordagem de modelagem de features baseada em ontologias (OntoSPL). Desenvolvemos um modelo ontológico integrado (Ontologia de tutoria gamificada ou Gamified tutoring ontology) que conecta elementos de design de jogos apoiados por evidências no domínio de e-learning, além de teorias e frameworks de gamificação aos conceitos de STI. Finalmente, desenvolvemos uma solução de autoria (chamada AGITS) que leva em consideração tais ontologias para auxiliar professores na personalização de funcionalidades de STIs gamificados. As contribuições deste trabalho são avaliadas por meio da condução de quatro estudos empíricos: (1) conduzimos um experimento controlado para comparar a OntoSPL com uma abordagem de modelagem de features bem conhecida na literatura. Os resultados sugerem que esta abordagem é mais flexível e requer menos tempo para mudar; (2) avaliamos o modelo ontológico integrado usando um método de avaliação de ontologias (FOCA) com especialistas tanto de contexto acadêmico quanto industrial. Os resultados sugerem que as ontologias estão atendendo adequadamente os papeis de representação do conhecimento; (3) avaliamos versões não-interativas da solução de autoria desenvolvida com 59 participantes. Os resultados indicam uma atitude favorável ao uso da solução de autoria projetada,nos quais os participantes concordaram que a solução é fácil de usar, usável, simples, esteticamente atraente,tem um suporte bem percebido e alta credibilidade; e (4) avaliamos, por fim,versões interativas (do zero e usando um modelo) da solução de autoria com 41 professores. Os resultados sugerem que professores podem usar e reusar, com um alto nível de aceitação, uma solução de autoria que inclui toda a complexidade de projetar STI gamificado.Intelligent Tutoring Systems (ITSs) have been drawing the attention of academics and practitioners since early 70’s. There have been a number of recent studies in support of the effectiveness of ITSs. However, it is very common that students become disengaged or bored during the learning process by using ITSs. To explicitly consider students’ motivational aspects, researchers are increasingly interested in using gamification along with ITS.However, despite providing individualized tutoring to students and some kind of support for teachers, teachers have been not considered as first-class citizens in the development of these kinds of systems. In order to contribute to the active and customized use of gamified ITS by teachers, three technical problems should be considered. First, designing ITS is very complex (i.e., take into account different theories, components, and stahekolders) and including gamification may significantly increase such complexity and variability. Second, gamified ITS features can be used depending on several elements (e.g., educational level, knowledge domain, gamification and ITS theories, etc). Thus, it is imperative to take advantage of theories and practices from both topics to reduce the design space of these systems. Third, in order to effectively aid teachers to actively use such systems, it is needed to provide a simple and usable solution for them. To deal with these problems, the main objective of this thesis is to design an authoring computational solution to provide for teachers a way to customize gamified ITS features managing the high variability of these systems and considering gamification and ITS theories/practices. To achieve this objective, we identify the variability space and represent it using an ontology-based feature modeling approach (OntoSPL). We develop an integrated ontological model (Gamified tutoring ontology) that connects evidence-supported game design elements in the e-learning domain as well as gamification theories and frameworks to existing ITS concepts. Finally, we develop an authoring solution (named AGITS) that takes into account these ontologies to aid teachers in the customization of gamified ITS features. We evaluate our contributions by conducting four empirical studies: (1) we perform a controlled experiment to compare OntoSPL against a well-known ontology-based feature modeling approach. The results suggest that our approach is more flexible and requires less time to change; (2) we evaluate the ontological integrated model by using an ontology evaluation method (FOCA) with experts from academic and industrial settings. The results suggest that our ontologies are properly targeting the knowledge representation roles; (3) we evaluate non-interactive versions of the designed authoring solution with 59 participants. The results indicate a positive attitude towards the use of the designed authoring solutions, in which participants agreed that they are ease to use, usable, simple, aesthetically appealing, have a well-perceived system support and high credibility; and (4) we also evaluate interactive versions (scratch and template) of our authoring solution with 41 teachers. The results suggest that teachers can use and reuse, with a high acceptance level, an authoring solution that includes all the complexity to design gamified ITS