ECTS credits ECTS credits: 3
ECTS Hours Rules/Memories Hours of tutorials: 1 Expository Class: 10 Interactive Classroom: 15 Total: 26
Use languages Spanish, Galician
Type: Ordinary Degree Subject RD 1393/2007 - 822/2021
Departments: Electronics and Computing
Areas: Computer Science and Artificial Intelligence
Center Higher Technical Engineering School
Call: Second Semester
Teaching: With teaching
Enrolment: Enrollable
This course aims to provide students with the knowledge and skills needed to work with Semantic Web technologies and Knowledge Graphs. Throughout the course, students will study the main W3C standards such as RDF, RDFS, SPARQL, OWL, [R2]RML, and SHACL, which enable the representation, exchange, and validation of knowledge graphs. Students will learn how to create, enrich, and validate knowledge graphs—essential tools for integrating and analyzing large volumes of heterogeneous data.
In addition, the course will explore hybrid Artificial Intelligence systems based on knowledge graphs, such as question-answering systems, voice assistants, (large) language models, and other neuro-symbolic approaches. The course will also cover practical use cases, from European data spaces to real-world examples in various industries, where knowledge graphs play a key role in large-scale data interoperability and integration.
1. Introduction to the Semantic Web and Knowledge Graphs
2. Methodologies for Knowledge Graph development
3. Neuro-symbolic Artificial Intelligence based on Knowledge Graphs
4. Applications and use cases: Data Spaces
Core Bibliography:
Gómez-Pérez, A., Fernández-López, M., & Corcho, O. (2006). Ontological Engineering: with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web. Springer Science & Business Media.
Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., Melo, G. D., Gutierrez, C., ... & Zimmermann, A. (2021). Knowledge Graphs. ACM Computing Surveys (Csur), 54(4), 1–37.
Hendler, J., Lassila, O., & Berners-Lee, T. (2001). The Semantic Web. Scientific American, 284(5), 34–43.
Supplementary Bibliography:
Hitzler, P., Eberhart, A., Ebrahimi, M., Sarker, M. K., & Zhou, L. (2022). Neuro-symbolic approaches in artificial intelligence. National Science Review, 9(6), nwac035.
Poveda-Villalón, M., Fernández-Izquierdo, A., Fernández-López, M., & García-Castro, R. (2022). LOT: An industrial oriented ontology engineering framework. Engineering Applications of Artificial Intelligence, 111, 104755.
BASIC
[CB2] Ability to apply knowledge professionally, demonstrating the skills typically associated with building and defending arguments and solving problems within one’s field of study.
[CB4] Ability to communicate information, ideas, problems, and solutions to both specialist and non-specialist audiences.
[CB5] Development of learning skills necessary for undertaking further studies with a high degree of autonomy.
[CG2] Ability to solve problems with initiative, decision-making, autonomy, and creativity.
GENERAL
[CG3] Ability to design and create high-quality models and solutions based on Artificial Intelligence that are efficient, robust, transparent, and responsible.
[CG4] Ability to select and justify appropriate methods and techniques to solve a specific problem, or to develop and propose new methods based on AI.
[CG5] Ability to conceive new computational systems and/or evaluate the performance of existing systems that integrate AI models and techniques.
SPECIFIC
[CE13] Ability to model and design systems based on knowledge representation and logical or approximate reasoning and apply them to various domains and problems, including those under uncertainty.
[CE14] Knowledge of semantic technologies for storing and accessing knowledge graphs and their application to problem-solving.
CROSS-CUTTING
[TR3] Ability to create new models and solutions autonomously and creatively, adapting to new situations. Initiative and entrepreneurial spirit.
The following teaching methods will be used: Lecture Sessions (LS) and Interactive Sessions (IS). LS will consist of master classes focused on theoretical concepts. IS will involve applying the theoretical content through examples and real-world use cases.
The course will be assessed in two complementary ways, designed to evaluate students’ competencies in the practical development of knowledge-based systems and ontologies. Assessment will differentiate between the regular and resit (make-up) examination periods:
REGULAR EXAMINATION PERIOD
Final Exam: This will assess the student’s understanding of theoretical and practical aspects of the course and will account for 30% of the final grade.
Practical Assignments: A set of exercises to demonstrate practical mastery in developing knowledge graph-based systems and hybrid AI methods. This will account for 70% of the final grade.
Submitting any of the proposed exercises will be considered as participation in the course. Full or partial plagiarism in one or more exercises will result in a failing grade for the entire course.
RESIT EXAMINATION PERIOD
The assessment criteria for theory and practice in the resit period will be the same as in the regular period.
In the event of fraudulent activity in exercises or exams, the rules outlined in the "Regulations on Academic Performance Evaluation and Grade Review for Students" will apply. According to the ETSE regulations on plagiarism (approved by the ETSE Board on 19/12/2019), full or partial plagiarism in practical or theoretical exercises will result in a failing grade (0.0) for both exam opportunities.
In-class work:
- Theoretical classes: 10 hours
- Practical classes: 15 hours
- Individual tutoring: 1 hour
Total classroom hours: 26 hours
Independent student work (study, exercises, projects) and other activities (assessment): 49 hours
Total workload: 75 hours
The USC virtual campus and GitHub platform will be used for all teaching activities, material distribution, practice instructions, and submission of assignments.
Se utilizará el campus virtual de la USC y la plataforma Github para toda la docencia, publicación de material, guiones de prácticas y entregas de trabajos.
David Chaves Fraga
Coordinador/a- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881815525
- david.chaves [at] usc.es
- Category
- Professor: LOU (Organic Law for Universities) PhD Assistant Professor
| Thursday | |||
|---|---|---|---|
| 15:30-17:00 | Grupo /CLIL_01 | Spanish | IA.03 |
| 05.28.2026 16:00-20:00 | Grupo /CLE_01 | IA.01 |
| 05.28.2026 16:00-20:00 | Grupo /CLIL_01 | IA.01 |
| 07.07.2026 16:00-20:30 | Grupo /CLIL_01 | IA.11 |
| 07.07.2026 16:00-20:30 | Grupo /CLE_01 | IA.11 |