A model and an algebra for semi-structured and full-text queries

The need for a convergence between semi-structured data management and Information Retrieval techniques is manifest to the scientific community. In order to fulfil this growing request, W3C has recently proposed XQuery Full Text, an IR-oriented extension of XQuery. However, the issue of query optimization requires the study of important properties like query equivalence and containment; to this aim, a formal representation of document and queries is needed. The goal of this thesis is to establish such formal background. We define a data model for XML documents and propose an algebra able to represent most of XQuery Full-Text expressions. We show how an XQuery Full-Text expression can be translated into an algebraic expression and how an algebraic expression can be optimized.

Extending Implicit Computational Complexity and Abstract Machines to Languages with Control

The Curry-Howard isomorphism is the idea that proofs in natural deduction can be put in correspondence with lambda terms in such a way that this correspondence is preserved by normalization. The concept can be extended from Intuitionistic Logic to other systems, such as Linear Logic. One of the nice conseguences of this isomorphism is that we can reason about functional programs with formal tools which are typical of proof systems: such analysis can also include quantitative qualities of programs, such as the number of steps it takes to terminate. Another is the possiblity to describe the execution of these programs in terms of abstract machines. In 1990 Griffin proved that the correspondence can be extended to Classical Logic and control operators. That is, Classical Logic adds the possiblity to manipulate continuations. In this thesis we see how the things we described above work in this larger context.

Variety, flexibility, and use of abstract concepts. A multiple grounded perspective.

The nature of concepts is a matter of intense debate in cognitive sciences. While traditional views claim that conceptual knowledge is represented in a unitary symbolic system, recent Embodied and Grounded Cognition theories (EGC) submit the idea that conceptual system is couched in our body and influenced by the environment (Barsalou, 2008). One of the major challenges for EGC is constituted by abstract concepts (ACs), like fantasy. Recently, some EGC proposals addressed this criticism, arguing that the ACs comprise multifaced exemplars that rely on different grounding sources beyond sensorimotor one, including interoception, emotions, language, and sociality (Borghi et al., 2018). However, little is known about how ACs representation varies as a function of life experiences and their use in communication. The theoretical arguments and empirical studies comprised in this dissertation aim to provide evidence on multiple grounding of ACs taking into account their varieties and flexibility. Study I analyzed multiple ratings on a large sample of ACs and identified four distinct subclusters. Study II validated this classification with an interference paradigm involving motor/manual, interoceptive, and linguistic systems during a difficulty rating task. Results confirm that different grounding sources are activated depending on ACs kind. Study III-IV investigate the variability of institutional concepts, showing that the higher the law expertise level, the stronger the concrete/emotional determinants in their representation. Study V introduced a novel interactive task in which abstract and concrete sentences serve as cues to simulate conversation. Analysis of language production revealed that the uncertainty and interactive exchanges increase with abstractness, leading to generating more questions/requests for clarifications with abstract than concrete sentences. Overall, results confirm that ACs are multidimensional, heterogeneous, and flexible constructs and that social and linguistic interactions are crucial to shaping their meanings. Investigating ACs in real-time dialogues may be a promising direction for future research.

Mind the gaps: cognitive-inspired AI for high-level visual sensemaking. Towards abstract concept image classification

The abundance of visual data and the push for robust AI are driving the need for automated visual sensemaking. Computer Vision (CV) faces growing demand for models that can discern not only what images "represent," but also what they "evoke." This is a demand for tools mimicking human perception at a high semantic level, categorizing images based on concepts like freedom, danger, or safety. However, automating this process is challenging due to entropy, scarcity, subjectivity, and ethical considerations. These challenges not only impact performance but also underscore the critical need for interoperability. This dissertation focuses on abstract concept-based (AC) image classification, guided by three technical principles: situated grounding, performance enhancement, and interpretability. We introduce ART-stract, a novel dataset of cultural images annotated with ACs, serving as the foundation for a series of experiments across four key domains: assessing the effectiveness of the end-to-end DL paradigm, exploring cognitive-inspired semantic intermediaries, incorporating cultural and commonsense aspects, and neuro-symbolic integration of sensory-perceptual data with cognitive-based knowledge. Our results demonstrate that integrating CV approaches with semantic technologies yields methods that surpass the current state of the art in AC image classification, outperforming the end-to-end deep vision paradigm. The results emphasize the role semantic technologies can play in developing both effective and interpretable systems, through the capturing, situating, and reasoning over knowledge related to visual data. Furthermore, this dissertation explores the complex interplay between technical and socio-technical factors. By merging technical expertise with an understanding of human and societal aspects, we advocate for responsible labeling and training practices in visual media. These insights and techniques not only advance efforts in CV and explainable artificial intelligence but also propel us toward an era of AI development that harmonizes technical prowess with deep awareness of its human and societal implications.