Approche basée sur des patrons pour concevoir des logiciels d’enseignement adaptés aux technologies du Web

Les applications Web en général ont connu d’importantes évolutions technologiques au cours des deux dernières décennies et avec elles les habitudes et les attentes de la génération de femmes et d’hommes dite numérique. Paradoxalement à ces bouleversements technologiques et comportementaux, les logiciels d’enseignement et d’apprentissage (LEA) n’ont pas tout à fait suivi la même courbe d’évolution technologique. En effet, leur modèle de conception est demeuré si statique que leur utilité pédagogique est remise en cause par les experts en pédagogie selon lesquels les LEA actuels ne tiennent pas suffisamment compte des aspects théoriques pédagogiques. Mais comment améliorer la prise en compte de ces aspects dans le processus de conception des LEA? Plusieurs approches permettent de concevoir des LEA robustes. Cependant, un intérêt particulier existe pour l’utilisation du concept patron dans ce processus de conception tant par les experts en pédagogie que par les experts en génie logiciel. En effet, ce concept permet de capitaliser l’expérience des experts et permet aussi de simplifier de belle manière le processus de conception et de ce fait son coût. Une comparaison des travaux utilisant des patrons pour concevoir des LEA a montré qu’il n’existe pas de cadre de synergie entre les différents acteurs de l’équipe de conception, les experts en pédagogie d’un côté et les experts en génie logiciel de l’autre. De plus, les cycles de vie proposés dans ces travaux ne sont pas complets, ni rigoureusement décrits afin de permettre de développer des LEA efficients. Enfin, les travaux comparés ne montrent pas comment faire coexister les exigences pédagogiques avec les exigences logicielles. Le concept patron peut-il aider à construire des LEA robustes satisfaisant aux exigences pédagogiques ? Comme solution, cette thèse propose une approche de conception basée sur des patrons pour concevoir des LEA adaptés aux technologies du Web. Plus spécifiquement, l’approche méthodique proposée montre quelles doivent être les étapes séquentielles à prévoir pour concevoir un LEA répondant aux exigences pédagogiques. De plus, un répertoire est présenté et contient 110 patrons recensés et organisés en paquetages. Ces patrons peuvent être facilement retrouvés à l’aide du guide de recherche décrit pour être utilisés dans le processus de conception. L’approche de conception a été validée avec deux exemples d’application, permettant de conclure d’une part que l’approche de conception des LEA est réaliste et d’autre part que les patrons sont bien valides et fonctionnels. L’approche de conception de LEA proposée est originale et se démarque de celles que l’on trouve dans la littérature car elle est entièrement basée sur le concept patron. L’approche permet également de prendre en compte les exigences pédagogiques. Elle est générique car indépendante de toute plateforme logicielle ou matérielle. Toutefois, le processus de traduction des exigences pédagogiques n’est pas encore très intuitif, ni très linéaire. D’autres travaux doivent être réalisés pour compléter les résultats obtenus afin de pouvoir traduire en artéfacts exploitables par les ingénieurs logiciels les exigences pédagogiques les plus complexes et les plus abstraites. Pour la suite de cette thèse, une instanciation des patrons proposés serait intéressante ainsi que la définition d’un métamodèle basé sur des patrons qui pourrait permettre la spécification d’un langage de modélisation typique des LEA. L’ajout de patrons permettant d’ajouter une couche sémantique au niveau des LEA pourrait être envisagée. Cette couche sémantique permettra non seulement d’adapter les scénarios pédagogiques, mais aussi d’automatiser le processus d’adaptation au besoin d’un apprenant en particulier. Il peut être aussi envisagé la transformation des patrons proposés en ontologies pouvant permettre de faciliter l’évaluation des connaissances de l’apprenant, de lui communiquer des informations structurées et utiles pour son apprentissage et correspondant à son besoin d’apprentissage.

The difference of Being in the early modern world: a relational-material approach to life in Scotland in the period of the witch trials

This thesis investigates how ways of being in different ontologies emerge from material and embodied practice. This general concern is explored through the particular case study of Scotland in the period of the witch trials (the 16th and 17th centuries C.E.). The field of early modern Scottish witchcraft studies has been active and dynamic over the past 15 years but its prioritisation of what people said over what they did leaves a clear gap for a situated and relational approach focusing upon materiality. Such an approach requires a move away from the Cartesian dichotomies of modern ontology to recognise past beliefs as real to those who experienced them, coconstitutive of embodiment and of the material worlds people inhabited. In theory, method and practice, this demands a different way of exploring past worlds to avoid flattening strange data. To this end, the study incorporates narratives and ‘disruptions’ – unique engagements with Contemporary Art which facilitate understanding by enabling the temporary suspension of disbelief. The methodology is iterative, tacking between material and written sources in order to better understand the heterogeneous assemblages of early modern (counter-) witchcraft. Previously separate areas of discourse are (re-)constituted into alternative ontic categories of newly-parallel materials. New interpretations of things, places, bodies and personhoods emerge, raising questions about early modern experiences of the world. Three thematic chapters explore different sets of collaborative agencies as they entwine into new things, co-fabricating a very different world. Moving between witch trial accounts, healing wells, infant burial grounds, animals, discipline artefacts and charms, the boundaries of all prove highly permeable. People, cloth and place bleed into one another through contact; trees and water emerge as powerful agents of magical-place-making; and people and animals meet to become single, hybrid-persons spread over two bodies. Life and death consistently emerge as protracted processes with the capacity to overlap and occur simultaneously in problematic ways. The research presented in this thesis establishes a new way of looking at the nature of Being as experienced by early modern Scots. This provides a foundation for further studies, which can draw in other materials not explored here such as communion wares and metal charms. Comparison with other early modern Western societies may also prove fruitful. Furthermore, the methodology may be suitable for application to other interdisciplinary projects incorporating historical and material evidence.

OntoSPARES: da linguagem natural às ontologias. Contributos para a classificação automática de dados históricos (séc. XVI-XVIII)

O processamento de linguagem natural e as ontologias são ferramentas cuja interação permite uma melhor compreensão dos dados armazenados. Este trabalho, ao associar estas duas áreas aos elementos disponíveis numa base de dados prosopográfica, tornou possível identificar e classificar relacionamentos entre setores de ocupação na forma como eram designados na época, setores de atividade num formato mais próximo do de hoje e o estatuto social que essas incumbências tinham na sociedade coeva. Os dados utilizados são sobretudo de membros do Santo Ofício – do século XVI ao século XVIII. Para atingir este objetivo utilizaram-se algumas descrições textuais de ocorrências da época e outras pouco estruturadas, disponíveis no repositório SPARES. A aplicação de processamento de linguagem natural (remoção de stopwords e aplicação de stemming), conjugada com a construção de duas ontologias, tornou possível classificar esses dados, permitindo consultas mais eficazes. Ao contribuir para a classificação automática de dados históricos, propõem-se metodologias que podem ser aplicadas em dados de qualquer outra área do conhecimento, especialmente as que lidam com as variáveis de tempo e espaço de forma mais intensa; Abstract: OntoSPARES: from natural language to ontologies Contributions to the automatic classification of historical data (16th-18th centuries) The interaction between the natural language processing and ontologies are tools allowing a better understanding of the data stored. This work, by combining these two areas to the elements available in a prosopographic database, has made possible to identify and classify relationships between occupations of many individuals (in general Holy Office members of the 16th-18th centuries). To achieve this goal the data used was gathered in SPARES repository, including some textual descriptions of the time occurrences. They are all few structured. The application of natural language processing (stopwords removal and stemming application), combined with the construction of two ontologies, made possible to classify those data, allowing a more effective search. By contributing to the automatic classification of historical data, this thesis proposes methodologies that can be applied to data from any other field of knowledge, specially data dealing with time and space variables.

Using a Dialogue Manager to Improve Semantic Web Search

Question Answering systems that resort to the Semantic Web as a knowledge base can go well beyond the usual matching words in documents and, preferably, find a precise answer, without requiring user help to interpret the documents returned. In this paper, the authors introduce a Dialogue Manager that, through the analysis of the question and the type of expected answer, provides accurate answers to the questions posed in Natural Language. The Dialogue Manager not only represents the semantics of the questions, but also represents the structure of the discourse, including the user intentions and the questions context, adding the ability to deal with multiple answers and providing justified answers. The authors’ system performance is evaluated by comparing with similar question answering systems. Although the test suite is slight dimension, the results obtained are very promising.

Empirical ontology design patterns

Knowledge graphs (KGs) and ontologies have been widely adopted for modelling numerous domains. However, understanding the content of an ontology/KG is far from straightforward: existing methods partially address this issue. This thesis is based on the assumption that identifying the Ontology Design Patterns (ODPs) in an ontology or a KG contributes to address this problem. Most times, the reused ODPs are not explicitly annotated, or their reuse is unintentional. Therefore, there is a challenge to automatically identify ODPs in existing ontologies and KGs, which is the main focus of this research work. This thesis analyses the role of ODPs in ontology engineering, through experiences in actual ontology projects, placing this analysis in the context of existing ontology reuse approaches. Moreover, this thesis introduces a novel method for extracting empirical ODPs (EODPs) from ontologies, and a novel method for extracting EODPs from knowledge graphs, whose schemas are implicit. The first method groups the extracted EODPs in clusters: conceptual components. Each conceptual component represents a modelling problem, e.g. representing collections. As EODPs are fragments possibly extracted from different ontologies, some of them will fall in the same cluster, meaning that they are implemented solutions to the same modelling problem. EODPs and conceptual components enable the empirical observation and comparison of modelling solutions to common modelling problems in different ontologies. The second method extracts EODPs from a KG as sets of probabilistic axioms/constraints involving the ontological entities instantiated. These EODPs may support KG inspection and comparison, providing insights on how certain entities are described in a KG. An additional contribution of this thesis is an ontology for annotating ODPs in ontologies and KGs.

Connecting works of art within the semantic web of symbolic meanings

My doctoral research is about the modelling of symbolism in the cultural heritage domain, and on connecting artworks based on their symbolism through knowledge extraction and representation techniques. In particular, I participated in the design of two ontologies: one models the relationships between a symbol, its symbolic meaning, and the cultural context in which the symbol symbolizes the symbolic meaning; the second models artistic interpretations of a cultural heritage object from an iconographic and iconological (thus also symbolic) perspective. I also converted several sources of unstructured data, a dictionary of symbols and an encyclopaedia of symbolism, and semi-structured data, DBpedia and WordNet, to create HyperReal, the first knowledge graph dedicated to conventional cultural symbolism. By making use of HyperReal's content, I showed how linked open data about cultural symbolism could be utilized to initiate a series of quantitative studies that analyse (i) similarities between cultural contexts based on their symbologies, (ii) broad symbolic associations, (iii) specific case studies of symbolism such as the relationship between symbols, their colours, and their symbolic meanings. Moreover, I developed a system that can infer symbolic, cultural context-dependent interpretations from artworks according to what they depict, envisioning potential use cases for museum curation. I have then re-engineered the iconographic and iconological statements of Wikidata, a widely used general-domain knowledge base, creating ICONdata: an iconographic and iconological knowledge graph. ICONdata was then enriched with automatic symbolic interpretations. Subsequently, I demonstrated the significance of enhancing artwork information through alignment with linked open data related to symbolism, resulting in the discovery of novel connections between artworks. Finally, I contributed to the creation of a software application. This application leverages established connections, allowing users to investigate the symbolic expression of a concept across different cultural contexts through the generation of a three-dimensional exhibition of artefacts symbolising the chosen concept.

Ethics in the flesh: formalizing moral values in embodied cognition

Values are beliefs or principles that are deemed significant or desirable within a specific society or culture, serving as the fundamental underpinnings for ethical and socio-behavioral norms. The objective of this research is to explore the domain encompassing moral, cultural, and individual values. To achieve this, we employ an ontological approach to formally represent the semantic relations within the value domain. The theoretical framework employed adopts Fillmore’s frame semantics, treating values as semantic frames. A value situation is thus characterized by the co-occurrence of specific semantic roles fulfilled within a given event or circumstance. Given the intricate semantics of values as abstract entities with high social capital, our investigation extends to two interconnected domains. The first domain is embodied cognition, specifically image schemas, which are cognitive patterns derived from sensorimotor experiences that shape our conceptualization of entities in the world. The second domain pertains to emotions, which are inherently intertwined with the realm of values. Consequently, our approach endeavors to formalize the semantics of values within an embodied cognition framework, recognizing values as emotional-laden semantic frames. The primary ontologies proposed in this work are: (i) ValueNet, an ontology network dedicated to the domain of values; (ii) ISAAC, the Image Schema Abstraction And Cognition ontology; and (iii) EmoNet, an ontology for theories of emotions. The knowledge formalization adheres to established modeling practices, including the reuse of semantic web resources such as WordNet, VerbNet, FrameNet, DBpedia, and alignment to foundational ontologies like DOLCE, as well as the utilization of Ontology Design Patterns. These ontological resources are operationalized through the development of a fully explainable frame-based detector capable of identifying values, emotions, and image schemas generating knowledge graphs from from natural language, leveraging the semantic dependencies of a sentence, and allowing non trivial higher layer knowledge inferences.

Supporting requirement elicitation and ontology testing in knowledge graph engineering

Knowledge graphs and ontologies are closely related concepts in the field of knowledge representation. In recent years, knowledge graphs have gained increasing popularity and are serving as essential components in many knowledge engineering projects that view them as crucial to their success. The conceptual foundation of the knowledge graph is provided by ontologies. Ontology modeling is an iterative engineering process that consists of steps such as the elicitation and formalization of requirements, the development, testing, refactoring, and release of the ontology. The testing of the ontology is a crucial and occasionally overlooked step of the process due to the lack of integrated tools to support it. As a result of this gap in the state-of-the-art, the testing of the ontology is completed manually, which requires a considerable amount of time and effort from the ontology engineers. The lack of tool support is noticed in the requirement elicitation process as well. In this aspect, the rise in the adoption and accessibility of knowledge graphs allows for the development and use of automated tools to assist with the elicitation of requirements from such a complementary source of data. Therefore, this doctoral research is focused on developing methods and tools that support the requirement elicitation and testing steps of an ontology engineering process. To support the testing of the ontology, we have developed XDTesting, a web application that is integrated with the GitHub platform that serves as an ontology testing manager. Concurrently, to support the elicitation and documentation of competency questions, we have defined and implemented RevOnt, a method to extract competency questions from knowledge graphs. Both methods are evaluated through their implementation and the results are promising.