KNOWLEDGE REPRESENTATION AND REASONING - MOD. 2
Stampa
Anno immatricolazione
2021/2022
Anno offerta
2021/2022
Normativa
DM270
SSD
INF/01 (INFORMATICA)
Dipartimento
DIPARTIMENTO DI MATEMATICA 'FELICE CASORATI'
Corso di studio
ARTIFICIAL INTELLIGENCE
Curriculum
PERCORSO COMUNE
Anno di corso
Periodo didattico
Annualità Singola (04/10/2021 - 17/06/2022)
Crediti
6
Ore
56 ore di attività frontale
Lingua insegnamento
INGLESE
Tipo esame
SCRITTO E ORALE CONGIUNTI
Docente
Prerequisiti
In this module we assume that the student is familiar with the topics discussed in the first module. No other prerequisite is required.
Obiettivi formativi
The objective of this course is to provide students with sufficient knowledge and skills to design, debug, implement and use knowledge bases based on two main paradigms, that is, semantic technologies and logic programming. We expect to cover not only logical aspects of reasoning systems but also data management for graph-based knowledge bases. Also, we aim at covering foundational aspects of knowledge base development but also pragmatic ones with exercises based on existing software systems.
As a result, we expect that the students learn theoretical aspects of knowledge base design, but they also develop skills related to model knowledge bases with relevant knowledge-based frameworks.
Programma e contenuti
Introduction - AI and KRR: the many facets of intelligence, reasoning and inference, AI challenges and KRR.

Knowledge Graphs & Data Management: The KG abstraction, RDF, SPARQL. Exercises: modeling knowledge in RDF, querying data in RDF.

Knowledge Graphs & Reasoning: from vocabularies to ontologies; RDFS, OWL 2. Exercises: modeling knowledge in RDFS, modeling knowledge in OWL.

Declarative Problem Solving, Logic Programming & Nonmonotonic Reasoning: Logic Programming and Non-monotonic Reasoning, Datalog, Non-monotonic Reasoning, Answer Set Prolog (ASP). Exercises: Datalog with DLV; ASP, disjunction and Negation As Failure with DLV.

More on KRR for AI: how to build a knowledge base, KRR and AI challenges (reprise).
Metodi didattici
Lectures (4/6) + hands-on lessons with exercises and tools (2/6)
Testi di riferimento
Additional material:

Knowledge Graphs. Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, José Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel- Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, and Antoine Zimmermann. Synthesis Lectures on Data, Semantics, and Knowledge, November 2021, Vol. 12, No. 2 , Pages 1-257

The knowledge graph cookbook. Blumauer, Andreas, Helmut Nagy.

Knowledge Graphs: Fundamentals, Techniques, and Applications. Kejriwal, Mayank, Craig A. Knoblock, and Pedro Szekely. MIT Press, 2021
Modalità verifica apprendimento
Written test at the end of the course covering all the course topics (theory). Optional assignments based on the tools introduced in the course (practice).
Altre informazioni
Obiettivi Agenda 2030 per lo sviluppo sostenibile