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.

Knowledge management in intelligent tutoring systems

In the last years, Intelligent Tutoring Systems have been a very successful way for improving learning experience. Many issues must be addressed until this technology can be defined mature. One of the main problems within the Intelligent Tutoring Systems is the process of contents authoring: knowledge acquisition and manipulation processes are difficult tasks because they require a specialised skills on computer programming and knowledge engineering. In this thesis we discuss a general framework for knowledge management in an Intelligent Tutoring System and propose a mechanism based on first order data mining to partially automate the process of knowledge acquisition that have to be used in the ITS during the tutoring process. Such a mechanism can be applied in Constraint Based Tutor and in the Pseudo-Cognitive Tutor. We design and implement a part of the proposed architecture, mainly the module of knowledge acquisition from examples based on first order data mining. We then show that the algorithm can be applied at least two different domains: first order algebra equation and some topics of C programming language. Finally we discuss the limitation of current approach and the possible improvements of the whole framework.

Design, implementation and evaluation of a virtual laboratory for Computer Engineering Education

Broad consensus has been reached within the Education and Cognitive Psychology research communities on the need to center the learning process on experimentation and concrete application of knowledge, rather than on a bare transfer of notions. Several advantages arise from this educational approach, ranging from the reinforce of students learning, to the increased opportunity for a student to gain greater insight into the studied topics, up to the possibility for learners to acquire practical skills and long-lasting proficiency. This is especially true in Engineering education, where integrating conceptual knowledge and practical skills assumes a strategic importance. In this scenario, learners are called to play a primary role. They are actively involved in the construction of their own knowledge, instead of passively receiving it. As a result, traditional, teacher-centered learning environments should be replaced by novel learner-centered solutions. Information and Communication Technologies enable the development of innovative solutions that provide suitable answers to the need for the availability of experimentation supports in educational context. Virtual Laboratories, Adaptive Web-Based Educational Systems and Computer-Supported Collaborative Learning environments can significantly foster different learner-centered instructional strategies, offering the opportunity to enhance personalization, individualization and cooperation. More specifically, they allow students to explore different kinds of materials, to access and compare several information sources, to face real or realistic problems and to work on authentic and multi-facet case studies. In addition, they encourage cooperation among peers and provide support through coached and scaffolded activities aimed at fostering reflection and meta-cognitive reasoning. This dissertation will guide readers within this research field, presenting both the theoretical and applicative results of a research aimed at designing an open, flexible, learner-centered virtual lab for supporting students in learning Information Security.

Safety by design: production of engineering surface modified nanomaterials

This PhD thesis focused on nanomaterial (NM) engineering for occupational health and safety, in the frame of the EU project “Safe Nano Worker Exposure Scenarios (SANOWORK)”. Following a safety by design approach, surface engineering (surface coating, purification process, colloidal force control, wet milling, film coating deposition and granulation) were proposed as risk remediation strategies (RRS) to decrease toxicity and emission potential of NMs within real processing lines. In the first case investigated, the PlasmaChem ZrO2 manufacturing, the colloidal force control applied to the washing of synthesis rector, allowed to reduce ZrO2 contamination in wastewater, performing an efficient recycling procedure of ZrO2 recovered. Furthermore, ZrO2 NM was investigated in the ceramic process owned by CNR-ISTEC and GEA-Niro; the spray drying and freeze drying techniques were employed decreasing NM emissivity, but maintaining a reactive surface in dried NM. Considering the handling operation of nanofibers (NFs) obtained through Elmarco electrospinning procedure, the film coating deposition was applied on polyamide non-woven to avoid free fiber release. For TiO2 NF the wet milling was applied to reduce and homogenize the aspect ratio, leading to a significant mitigation of fiber toxicity. In the Colorobbia spray coating line, Ag and TiO2 nanosols, employed to transfer respectively antibacterial or depolluting properties to different substrates, were investigated. Ag was subjected to surface coating and purification, decreasing NM toxicity. TiO2 was modified by surface coating, spray drying and blending with colloidal SiO2, improving its technological performance. In the extrusion of polymeric matrix charged with carbon nanotube (CNTs) owned by Leitat, the CNTs used as filler were granulated by spray drying and freeze spray drying techniques, allowing to reduce their exposure potential. Engineered NMs tested by biologists were further investigated in relevant biological conditions, to improve the knowledge of structure/toxicity mechanisms and obtain new insights for the design of safest NMs.

Towards Unstructured Knowledge Integration in Natural Language Processing

In the last decades, Artificial Intelligence has witnessed multiple breakthroughs in deep learning. In particular, purely data-driven approaches have opened to a wide variety of successful applications due to the large availability of data. Nonetheless, the integration of prior knowledge is still required to compensate for specific issues like lack of generalization from limited data, fairness, robustness, and biases. In this thesis, we analyze the methodology of integrating knowledge into deep learning models in the field of Natural Language Processing (NLP). We start by remarking on the importance of knowledge integration. We highlight the possible shortcomings of these approaches and investigate the implications of integrating unstructured textual knowledge. We introduce Unstructured Knowledge Integration (UKI) as the process of integrating unstructured knowledge into machine learning models. We discuss UKI in the field of NLP, where knowledge is represented in a natural language format. We identify UKI as a complex process comprised of multiple sub-processes, different knowledge types, and knowledge integration properties to guarantee. We remark on the challenges of integrating unstructured textual knowledge and bridge connections with well-known research areas in NLP. We provide a unified vision of structured knowledge extraction (KE) and UKI by identifying KE as a sub-process of UKI. We investigate some challenging scenarios where structured knowledge is not a feasible prior assumption and formulate each task from the point of view of UKI. We adopt simple yet effective neural architectures and discuss the challenges of such an approach. Finally, we identify KE as a form of symbolic representation. From this perspective, we remark on the need of defining sophisticated UKI processes to verify the validity of knowledge integration. To this end, we foresee frameworks capable of combining symbolic and sub-symbolic representations for learning as a solution.

IDeS methodology applied to automotive component re-engineering focused on lightweight and high-strength by additive manufacturing

The project aims to gather an understanding of additive manufacturing and other manufacturing 4.0 techniques with an eyesight for industrialization. First the internal material anisotropy of elements created with the most economically feasible FEM technique was established. An understanding of the main drivers for variability for AM was portrayed, with the focus on achieving material internal isotropy. Subsequently, a technique for deposition parameter optimization was presented, further procedure testing was performed following other polymeric materials and composites. A replicability assessment by means of the use of technology 4.0 was proposed, and subsequent industry findings gathered the ultimate need of developing a process that demonstrate how to re-engineer designs in order to show the best results with AM processing. The latest study aims to apply the Industrial Design and Structure Method (IDES) and applying all the knowledge previously stacked into fully reengineer a product with focus of applying tools from 4.0 era, from product feasibility studies, until CAE – FEM analysis and CAM – DfAM. These results would help in making AM and FDM processes a viable option to be combined with composites technologies to achieve a reliable, cost-effective manufacturing method that could also be used for mass market, industry applications.