Knowledge Representation

Recommended Prerequisites

Fundamentals of Artificial Intelligence, Programming and Mathematics.

Teaching Methods

The unit includes in theoretical classes, a detailed presentation of fundamental concepts, principles and theories of knowledge representation. In laboratory classes, whenever possible, learning process will be based on solving small projects based on real problems.

The evaluation includes an exam, which is worth of 60% of the final grade, and a practical component, worth 40%.

Learning Outcomes

The main objective of the unit is to introduce fundamentals of knowledge representation in the context of Artificial Intelligence.

The student is expected to acquire knowledge about the fundamentals of Knowledge Representation and develop problem solving skills, autonomous learning, and ability to plan and decide.

Upon completion of this course, the main competencies to be developed by the students are:

Instrumental – problem solving

Personal – critical thinking

Systemic - practical application of knowledge representation

The secondary competences are:

Instrumental – organizing and planning

Personal – work in teams

Systemic – autonomous learning.

Work Placement(s)

No

Syllabus

Information, Knowledge, Representation

.1 What is Information?

.2 What is Knowledge?

.3 What is Representation?

Types of Knowledge Representation

.1 Declarative Knowledge

.2 Procedural Knowledge

.3 Heuristic Knowledge

.4 Structural Knowledge  

Procedural Learning

.1 Example-based version space learning

.2 Explanation-based learning

.3 Inductive Logic Programming

.4 Instance-based Learning

.5 Bayesian learning; learning Bayesian Networks

Structural Knowledge

.1 Logic fundamentals (propositional logic, first-order logic)

.2 Languages RDF, RDF Schema, OWL

.3 Structures: taxonomies, thesauri, ontologies

.4 Linked Data and Vocabulary Reutilization

Applied Knowledge Representation

.1 Managing Data

.2 Cleaning Data

.3 Analyzing

.4 Modeling Data

.5 Organizing the data in a form suitable for plotting or tabular data

.6 Time Series

.7 Clustering, Regression and Classification.

Head Lecturer(s)

Professor A Definir - Departamento de Engenharia Informática

Assessment Methods

Assessment
Project: 40.0%
Exam: 60.0%

Bibliography

- Leon S. Sterling, Ehud Y. Shapiro, The Art of Prolog, 2^nd Edition, The MIT Press, 1994

- Pascal Hitzler, Markus Krotzsch, Sebastian Rudolph, Foundations of Semantic Web Technologies, Chapman and Hall/CRC, 2009

Stefanie Molin, Hands-On Data Analysis with Pandas, 2^nd Edition, <packt>, 2021

-Richard E. _Neapolitan, Learning Bayesian Networks, Pearson, 2019

 -Wes McKinney, Python for Data Analysis, 3^rd Edition, O' Reilly, 2022.