ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Hours of tutorials: 1 Expository Class: 21 Interactive Classroom: 21 Total: 43
Use languages English
Type: Ordinary subject Master’s Degree RD 1393/2007 - 822/2021
Departments: Electronics and Computing, External department linked to the degrees
Areas: Computer Science and Artificial Intelligence, Área externa M.U en Intelixencia Artificial
Center Higher Technical Engineering School
Call: Second Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
The course introduces the student to the extraction, evaluation and analysis of information present on the Web through the use of technologies that interpret the semantics underlying the format of its contents. In this context, the student will be trained in its exploitation as a global source of data, regardless of their location and the device or platform of access, whether they are expressed in natural language or in languages directly interpretable by intelligent agents. In short, the aim is to facilitate access, sharing and integration of information between Web users.
Web structure. Search engines. Analysis and mining of web content and usage. Personalization, discovery and filtering. Recommender systems. Semantic technologies and semantic web. Ontologies and knowledge graphs. Data modeling languages. Linked data and open linked data. Applications and success stories.
Basic:
- Croft, W. B., Metzler, D., & Strohman, T. (2010). Search engines: Information retrieval in practice (Vol. 520, pp. 131-141). Reading: Addison-Wesley.
- Schütze, H., Manning, C. D., & Raghavan, P. (2008). Introduction to information retrieval (Vol. 39, pp. 234-265). Cambridge: Cambridge University Press.
- Berners-Le, T., Hendler, J., & Lassila, Ou. (2001). The semantic web. Scientific american, 284(5), 34-43.
- Gomez-Pérez, A., Fernández, M., Cortiza, Ou. (2003) Ontological Engineering. Springer
- Ehrlinger, Lisa; Wöß, Wolfram (2016). Towards a Definition of Knowledge Graphs (PDF). SEMANTiCS2016. Leipzig: Joint Proceedings of the Posters and Demos Track of 12 th International Conference on Semantic Systems - SEMANTiCS2016 and 1 st International Workshop on Semantic Change & Evolving Semantics ( SuCCESS16). pp. 13–16.
Complementary:
- Introduction to Semantic Web Technologies. Ivan Herman, W3C June 22nd, 2010: https://www.w3.org/2010/Talks/0622-SemTech-IH/Tutorial.pdf. Retrieved 2022-05-11.
- What is a Knowledge Graph?| Ontotext". Ontotext. https://www.ontotext.com/blog/ontotext-platform-building-smart-enterpri…. Retrieved 2022-05-11.
- Krötsch, Markus; Weikum, Gerhard (March 2016). "Editorial of the Special Issue on Knowledge Graphs". Journal of Web Semantics. 37–38: 53–54. doi:10.1016/ j. websem.2016.04.002. Retrieved 2022-05-11.
- Semantic Web at W3 C: https:// www. w3. org/ standards/ semanticweb/ Retrieved 2022-05-11.
BASIC AND GENERAL
CG1 - Maintain and extend theoretical approaches founded to allow the introduction and exploitation of new and advanced technologies in the field of Artificial Intelligence.
GC3 - Search and select the useful information needed to solve complex problems, handling with fluency the bibliographic sources of the field.
CG4 - To elaborate adequately and with certain originality written compositions or motivated arguments, to write plans, work projects, scientific articles and to formulate reasonable hypotheses in the field.
CB6 - Possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context.
CB7 - That students know how to apply the acquired knowledge and problem-solving skills in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their area of study.
TRANSVERSALS
CT7 - Develop the ability to work in interdisciplinary or transdisciplinary teams, to offer proposals that contribute to sustainable environmental, economic, political and social development.
CT8 - Value the importance of research, innovation and technological development in the socioeconomic and cultural progress of society.
SPECIFIC
CE1 - Understanding and mastery of techniques for lexical, syntactic and semantic processing of texts in natural language.
CE2 - Understanding and mastery of the fundamentals and techniques for processing linked documents, structured and unstructured, and the representation of their content.
CE3 - Understanding and knowledge of the techniques of representation and processing of knowledge through ontologies, graphs and RDF, as well as the tools associated with them.
The methodology includes the expository method / lecture, laboratory practices, tutorials, independent work, case studies and project-based learning. It will be carried out with the following training activities:
1) Problem-based learning, seminars, case studies and projects: these are sessions whose objective is that students acquire certain skills based on the resolution of exercises, case studies and projects that require the student to apply the knowledge and skills developed during the course. These sessions may require the student to present orally the solution to the problems posed. The work carried out by the students can be done individually or in work groups.
2) Theory classes: Oral exposition complemented with the use of audiovisual media and the introduction of some questions directed to the students, with the purpose of transmitting knowledge and facilitating learning. In addition to the time of oral exposition by the professor, this formative activity requires the student to dedicate some time to prepare and review on their own the materials object of the class.
3) Practical laboratory classes: classes dedicated to the development of practical work involving the resolution of complex problems, and the analysis and design of solutions that constitute a means for their resolution. This activity may require students to present their work orally. The work carried out by the students can be done individually or in work groups.
The assessment will consist of two parts:
- Final exam, with weighting of 50% of the final grade.
- Evaluation of practical work, with a weighting of 50% of the final grade.
It will be necessary to reach 40% of the score in each part.
The grade will be not presented when no practical work or final exam is handed in.
Second opportunity
The evaluation will be carried out according to the same criteria described above. A new term will be opened for the delivery of the practical works, in case they had not been delivered in the first opportunity.
A1: Theory classes: 21 classroom hours, 42 hours total dedication.
A2: Practical laboratory classes: 10 face-to-face hours, 40 hours total dedication.
A3: Problem-based learning, seminars, case studies and projects: 11 classroom hours, 68 hours total dedication.
Se recomienda estudio semanal de la asignatura.
The teaching of this subject will be in English.
The expository teaching (21 hours) will be given between the USC and the UDC and will be broadcast for all students.
The interactive teaching (21 hours) will be given between the USC, the UDC and the UVIGO and will be broadcast for all students.
María Jesús Taboada Iglesias
Coordinador/a- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- Phone
- 881813561
- maria.taboada [at] usc.es
- Category
- Professor: University Professor
| Wednesday | |||
|---|---|---|---|
| 17:00-18:30 | Grupo /CLE_01 | English | IA.02 |
| 18:30-20:00 | Grupo /CLIL_01 | English | IA.02 |
| 06.05.2025 10:30-14:00 | Grupo /CLIL_01 | IA.02 |
| 06.05.2025 10:30-14:00 | Grupo /CLE_01 | IA.02 |
| 07.08.2025 10:30-14:00 | Grupo /CLIL_01 | IA.02 |
| 07.08.2025 10:30-14:00 | Grupo /CLE_01 | IA.02 |