Modelling and Recognizing Personal Data

PhD candidate Enrico Bignotti

April 18, 2018
Versione stampabile

Time: h 10:00 am
Location: Room Garda, via Sommarive 5 - Polo Ferrari 1

PhD candidate

  • Enrico Bignotti

Abstract of Dissertation

To define what a person is represents a hard task, due to the fact that personal data, i.e., data that refer or describe a person, have a very heterogeneous nature. The issue is only worsening with the advent of technologies that, while allowing unprecedented collection and processing capabilities, cannot \textit{understand} the world as humans do.  This problem is a well-known long-standing problem in computer science called the Semantic Gap Problem. It was originally defined in the research area of image processing as "... the lack of coincidence between the information that one can extract from the visual data and the interpretation that the same data have for a user in a given situation...". In the context of this work, the semantic gap is the lack of coincidence is between sensor data collected by ubiquitous devices and the human knowledge about the world that relies on their intelligence, habits, and routines.

This thesis addresses the semantic gap problem from a representational point of view, proposing an interdisciplinary approach able to model and recognize personal data in real life scenarios. In fact, the semantic gap affects many communities, ranging from ubiquitous computing to user modelling, that must face the issue of managing the complexity of personal data in terms of modelling and recognition.

The contributions of this Ph. D. thesis are:

1) The definition of a methodology based on an interdisciplinary approach that can account for how to represent and allow the recognition of personal data. The interdisciplinary approach relies on the entity-centric approach and on an interdisciplinary categorization to define and structure personal data.
2) The definition of an ontology of personal data to represent human in a general way while also accounting their different dimensions of their everyday life;
3) The instantiation of the personal data representation above in a reference architecture that allows implementing the ontology and that can exploit the methodology to account for how to recognize personal data.
4) The adoption of the methodology for defining personal data and its instantiation in three real-life use cases with different goals in mind, proving that our modelling works in different domains and can account for several dimensions of the user.