Dottorato di Ricerca in Data Science and Computation - XXXIII Ciclo - On the role of Computational Logic in Data Science

Oct 4, 2026

The thesis investigates how computational logic and data science can jointly support knowledge management in modern and future artificial intelligence. It adopts an agent-oriented perspective that highlights autonomous agents as central to achieving higher intelligence. The work identifies analogies and differences between computational logic and data science across representation, acquisition (learning), inference (reasoning), and explanation. A conceptual framework is proposed to bridge these disciplines, alongside a survey of current AI technologies and a new logic ecosystem solution for integrating symbolic and sub-symbolic AI through software realisation.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages363

Reading Time15 h 15 min

AccessFree to download

Abstract

Acknowledgements

1 Introduction

I What

2 Historical Perspective on AI

2.1 Computational Logic

2.2 Data Science

3 Representing Data and Knowledge

3.1 Symbolic Knowledge Representation

3.1.1 First Order Logic (FOL)

Dottorato di Ricerca in Data Science and Computation XXXIII Ciclo On the role of Computational Logic in Data Science: representing, learning, reasoning, and explaining knowledge Settore Concorsuale: 09/H1 – Sistemi di Elaborazione delle Informazioni Settore Scientifico Disciplinare: ING-INF/05 – Sistemi di Elaborazione delle Informazioni Coordinatore Dottorato Prof. Andrea Cavalli Supervisore Prof. Andrea Omicini Candidato Giovanni Ciatto Abstract In this thesis we discuss in what ways computational logic (CL) and data science (DS) can jointly contribute to the management of knowledge within the scope of modern and future artificial intelligence (AI), and how technically-sound software technologies can be realised along the path. An agent-oriented mindset permeates the whole discussion, by stressing pivotal role of autonomous agents in exploiting both means to reach higher degrees of intelligence. Accordingly, the goals of this thesis are manifold. First, we elicit the analogies and differences among CLand DS, hence looking for possible synergies and complementarities along 4 major knowledge-related dimensions, namely representation, acquisition (a.k.a. learning), inference (a.k.a. reasoning), and explanation. In this regard, we propose a conceptual framework through which bridges these disciplines can be described and designed. We then survey the current state of the art of AI technologies, w.r.t. their capability to support bridging CL and DS in practice. After detecting lacksand opportunities, we propose the notion of logic ecosystem as the new conceptual, architectural, and technological solution supporting the incremental integration of symbolic and sub-symbolic AI. Finally, we discuss how our notion of logic ecosystem can be reified into actual software technology and extended towards many DS-related directions. Keywords: Computational Logic · Data Science · XAI · Logic Ecosystem · 2P-Kt Odi et amo. Quare id faciam fortasse requiris. Nescio, sed fieri sentio et excrucior. Acknowledgements This thesis and my whole PhD wouldn’t have been the same without the many enlightening people I’ve met along the path. Hence, this section is my way of expressing my gratitude and esteem for the many mentors, colleagues, and friends I had the pleasure to work with in the last five years. Firstly, I’d wish to thank my supervisor and mentor, prof. Andrea Omicini, for the many years under his guide, and the many enlightening pieces of wisdom, and suggestions about the research, the academic career, and everyday life—other than, of course, the infinitely many ponsense nuns 1 . He showed to me how to become a fierce and autonomous researcher, as well as the importance of conceiving science as a community effort, and he gave me a lot of opportunities along the way. Of all such things, I’m sincerely grateful. Should I start another PhD (so to speak), I would choose his supervision once again. Concerning mentorship, I honestly owe my gratitude to prof. Mirko Viroli as well, for trusting me and letting me join the academic world back in 2017 . Revenge shall be sought, but up to then I’d wish to thank him for the many opportunities he gave me along the years, and for the many discussions and lunches we have shared. For similar reasons, and for being a continuous source of inspiration, I’d wish to thank professors Alessandro Ricci, and Enrico Denti. Concerning inspiration, as special mention is due to a few post-doc colleagues who have had quite an impact in shaping what my idea of “a good researcher”actually is. Accordingly, I’d with to thank dr. Danilo Pianini for being such an example of dedication to scientific and technical knowledge, dr. Stefano Mariani for helping me with my first steps in research and dr. Roberta Calegari for always supporting me since then. Finally, I’d wish to thank dr. Davide Calvaresi, for being such a great friend and co-author, as well as dr. Sara Montagna for all the preci

FAQs

How does the thesis connect computational logic and data science for knowledge management?

It analyzes how computational logic and data science jointly contribute to knowledge management in AI, emphasizing synergies and complementarities across representation, learning, reasoning, and explanation.

What agent-oriented perspective does the thesis use?

The discussion stresses the pivotal role of autonomous agents in exploiting both computational logic and data science to reach higher degrees of intelligence.

What is the proposed solution for integrating symbolic and sub-symbolic AI?

The thesis proposes the notion of a logic ecosystem as a conceptual, architectural, and technological solution that supports incremental integration, and then discusses how it can be reified into software technology.

Oct 4, 2026

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