ID:
509480
Durata (ore):
56
CFU:
6
SSD:
INFORMATICA
Anno:
2024
Dati Generali
Periodo di attività
Annualità Singola (30/09/2024 - 13/06/2025)
Syllabus
Obiettivi Formativi
The objective of this course is to provide students with sufficient knowledge and skills to design, debug, implement and use knowledge bases. We introduce some preliminary concepts related to discrete mathematics (order relations and lattices) and relational databases (what is a database, data definition languages, data manipulation languages, basic notions of relational algebra and SQL). Then we present two main paradigms, each one to stress some important concept in Knowledge Representation and Reasoning (KRR): logic programming, which provide support for non-monotonic reasoning, and semantic technologies, which provide native support for knowledge graphs, i.e., graph-based knowledge bases. We expect to cover not only logical aspects of reasoning systems but also data management, especially for knowledge graphs. 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.
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.
Prerequisiti
In this module we assume that the student is familiar with the topics discussed in the first module. No other prerequisite is required.
Metodi didattici
Lectures (4/6) + hands-on lessons with exercises and tools (2/6)
Verifica Apprendimento
Written test at the end of the course covering all the course topics (theory).
Depending on the teaching support availability (practice): 1) assignments based on the tools introduced in the course; 2) extra exercises in the final test.
Depending on the teaching support availability (practice): 1) assignments based on the tools introduced in the course; 2) extra exercises in the final test.
Testi
Essential references:
The Web of Data. Aidan Hogan. 2020. Springer. Pages 1-680. [Selected chapters]
The DLV system for knowledge representation and reasoning. Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Perri, S., & Scarcello, F. (2006). ACM Transactions on Computational Logic (TOCL), 7(3), 499-562.
Additional material:
Artificial Intelligence: Foundations of Computational Agents, second edition. Pool, D, and Mackworth, A. Cambridge University Press 2017. Chapter 12
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
The Web of Data. Aidan Hogan. 2020. Springer. Pages 1-680. [Selected chapters]
The DLV system for knowledge representation and reasoning. Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Perri, S., & Scarcello, F. (2006). ACM Transactions on Computational Logic (TOCL), 7(3), 499-562.
Additional material:
Artificial Intelligence: Foundations of Computational Agents, second edition. Pool, D, and Mackworth, A. Cambridge University Press 2017. Chapter 12
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
Contenuti
Introduction - AI and KRR: the many facets of intelligence, reasoning and inference, AI challenges and KRR.
Preliminaries: relations, properties of relations, order relations, extremals and lattices.
Introduction to relational databases: representing data with tables, data definition and data manipulation languages, basic notions of relational algebra and SQL.
Logic Programming & Nonmonotonic Reasoning: Answer Set Programming (ASP), Datalog with ASP; Non-monotonic Reasoning with ASP; Disjunctive Reasoning with ASP. Exercises: Datalog with DLV; ASP, disjunction and Negation As Failure with DLV.
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; from Description Logics to RDFS and OWL 2. Exercises: modeling knowledge in RDFS, modeling knowledge in OWL2.
More on KRR for AI: how to build a knowledge base, KRR and AI challenges (reprise).
Preliminaries: relations, properties of relations, order relations, extremals and lattices.
Introduction to relational databases: representing data with tables, data definition and data manipulation languages, basic notions of relational algebra and SQL.
Logic Programming & Nonmonotonic Reasoning: Answer Set Programming (ASP), Datalog with ASP; Non-monotonic Reasoning with ASP; Disjunctive Reasoning with ASP. Exercises: Datalog with DLV; ASP, disjunction and Negation As Failure with DLV.
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; from Description Logics to RDFS and OWL 2. Exercises: modeling knowledge in RDFS, modeling knowledge in OWL2.
More on KRR for AI: how to build a knowledge base, KRR and AI challenges (reprise).
Lingua Insegnamento
INGLESE
Altre informazioni
Some topics of MOD 2 expand topics developed in MOD 1, especially by providing a practical counterpart (e.g., using tools and software). The programs of the two modules are coordinated.
Corsi
Corsi
ARTIFICIAL INTELLIGENCE
Laurea
3 anni
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Persone
Persone (2)
Docente
Docente
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