Legal knowledge extraction in the data protection domain based on Ontology Design Patterns

In the European Union, the entry into force of the General Data Protection Regulation (GDPR) has brought the domain of data protection to the fore-front, encouraging the research in knowledge representation and natural language processing (NLP). On the one hand, several ontologies adopted Semantic Web standards to provide a formal representation of the data protection framework set by the GDPR. On the other hand, different NLP techniques have been utilised to implement services addressed to individuals, for helping them in understanding privacy policies, which are notoriously difficult to read. Few efforts have been devoted to the mapping of the information extracted from privacy policies to the conceptual representations provided by the existing ontologies modelling the data protection framework. In the first part of the thesis, I propose and put in the context of the Semantic Web a comparative analysis of existing ontologies that have been developed to model different legal fields. In the second part of the thesis, I focus on the data protection domain and I present a methodology that aims to fill the gap between the multitude of ontologies released to model the data protection framework and the disparate approaches proposed to automatically process the text of privacy policies. The methodology relies on the notion of Ontology Design Pattern (ODP), i.e. a modelling solution to solve a recurrent ontology design problem. Implementing a pipeline that exploits existing vocabularies and different NLP techniques, I show how the information disclosed in privacy policies could be extracted and modelled through some existing ODPs. The benefit of such an approach is the provision of a methodology for processing privacy policies texts that overlooks the different ontological models. Instead, it uses ODPs as a semantic middle-layer of processing that different ontological models could refine and extend according to their own ontological commitments

The Proceedings of the 23rd Annual International Conference on Digital Government Research (DGO2022) Intelligent Technologies, Governments and Citizens June 15-17, 2022

The 23rd Annual International Conference on Digital Government Research theme is “Intelligent Technologies, Governments and Citizens”. Data and computational algorithms make systems smarter, but should result in smarter government and citizens. Intelligence and smartness affect all kinds of public values - such as fairness, inclusion, equity, transparency, privacy, security, trust, etc., and is not well-understood. These technologies provide immense opportunities and should be used in the light of public values. Society and technology co-evolve and we are looking for new ways to balance between them. Specifically, the conference aims to advance research and practice in this field. The keynotes, presentations, posters and workshops show that the conference theme is very well-chosen and more actual than ever. The challenges posed by new technology have underscored the need to grasp the potential. Digital government brings into focus the realization of public values to improve our society at all levels of government. The conference again shows the importance of the digital government society, which brings together scholars in this field. Dg.o 2022 is fully online and enables to connect to scholars and practitioners around the globe and facilitate global conversations and exchanges via the use of digital technologies. This conference is primarily a live conference for full engagement, keynotes, presentations of research papers, workshops, panels and posters and provides engaging exchange throughout the entire duration of the conference

Software engineering for AI-based systems: A survey

AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image-, speech-recognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems. To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state-of-the-art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula.This work has been partially funded by the “Beatriz Galindo” Spanish Program BEAGAL18/00064 and by the DOGO4ML Spanish research project (ref. PID2020-117191RB-I00)Peer ReviewedPostprint (author's final draft

User Expectations and Experiences of Mobile Augmented Reality Services

Mobile Augmented Reality (MAR) as an emerging field of technology has the potential to engender services that demonstrate novel aspects like enriching the reality with digital information, location-based interaction, and tangible user interfaces. The early visions of MAR anticipated it to revolutionize the way of accessing and interacting with information in mobile contexts. However, one hindrance in this path is the lack of research understanding of the subjective user experience (UX) resulting from, e.g., the novel interaction metaphors and the mixing of realities that MAR embodies. What is more, little is known about users’ expectations of the futuristic concept of MAR and the experiences it could evoke. Because of the increasing importance of UX as a quality attribute in products and services, there is a need to understand the characteristics and expectations of UX in specific emerging fields like MAR. The goal of this thesis research is twofold: (1) to understand potential users’ expectations with regard to UX of future MAR services and (2) to understand the actual UX of the recent first-generation MAR applications like Junaio and Layar. By understanding the scope of expectations and experience that can take place in the field of MAR, it is possible to help the design and engineering of AR-based services to consider also the experiential aspirations of potential end users. This compound thesis belongs to the research field of Human-Computer Interaction. It contains four studies, in which altogether 401 persons participated in either interviews or online surveys. The empirical findings on expected and actual experiences are reported in six publications. The theoretical contribution is mostly conceptual, culminating to a framework that describes the facets of UX and categories of meaningful experiences in MAR. Based on the empirical findings and the framework, the practical contribution is concretized in the form of (1) design implications and (2) subjective evaluation measures to help designing future MAR services with an experience-oriented approach. According to the results, potential users (early adopters) expected MAR services to create a great extent of pleasurable experiences, such as empowerment, surprise, awareness, liveliness, playfulness, tangibility, collectivity, inspiration and creativity. Furthermore, the expectations were attributed to a variety of service components, also relating to other ubiquitous computing aspects (e.g., the augmentation as an output, proactive functionalities, and embedding of digital content to the reality). This implies that emerging technological concepts like MAR are perceived in very diverse ways and that people’s expectations of them consist largely of general needs and desires. The existing first-generation MAR applications, however, seem generally not to fulfill the expectations, showing a much narrower extent of actualized experience characteristics. This experiential gap, as well as the narrower extent of functionalities in current applications, contains much potential with regard to pursuing a rich and pleasurable UX in future design of MAR services. The empirical results, conceptualizations and practical implications can be utilized and built on in academic research as well as in development of MAR. The novelty and complexity of both MAR and UX as concepts elicit an extensive breadth of aspects to be studied in detail in future research and development – regarding both MAR as a field of technology and UX as a field of theory

The appraisal of Facebook online community: An exposition of mobile commerce in social media reviews

Peer reviewe