KNOWLEDGE REPRESENTATION AND REASONING - MOD. 2
Stampa
Enrollment year
2021/2022
Academic year
2021/2022
Regulations
DM270
Academic discipline
INF/01 (COMPUTER SCIENCE)
Department
DEPARTMENT OF MATHEMATICS "FELICE CASORATI"
Course
ARTIFICIAL INTELLIGENCE
Curriculum
PERCORSO COMUNE
Year of study
Period
(04/10/2021 - 17/06/2022)
ECTS
6
Lesson hours
56 lesson hours
Language
English
Activity type
WRITTEN AND ORAL TEST
Teacher
Prerequisites
In this module we assume that the student is familiar with the topics discussed in the first module. No other prerequisite is required.
Learning outcomes
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.
Course contents
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).
Teaching methods
Lectures (4/6) + hands-on lessons with exercises and tools (2/6)
Reccomended or required readings
Additional material:

Knowledge Graphs. Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, JoseĢ 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
Assessment methods
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).
Further information
Sustainable development goals - Agenda 2030