| Study programme competencies |
|
Code
|
Study programme competences
|
| A2 |
CE01 - Understanding and command of techniques for lexical, syntactic and semantic processing of text in natural language |
| A3 |
CE02 - Understanding and command of fundamentals and techniques for processing linked documents, both structured and unstructured, and of the representation of their contents |
| A4 |
CE03 - Understanding and knowledge of the techniques for knowledge representation and processing for ontologies, graphs and RDF, together with their associated tools |
| B1 |
CG01 - Maintaining and extending theoretical foundations to allow the introduction and exploitation of new and advanced technologies in the field of AI |
| B3 |
CG03 - Searching and selecting that useful information required to solve complex problems, with a confident handling of bibliographical sources in the field |
| B4 |
CG04 - Suitably elaborating written essays or motivated arguments, including some point of originality, writing plans, work projects, scientific papers and formulating reasonable hypotheses in the field |
| B6 |
CB01 - Acquiring and understanding knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, frequently in a research context |
| B7 |
CB02 - The students will be able to apply the acquired knowledge and to use their capacity of solving problems in new or poorly explored environments inside wider (or multidisciplinary) contexts related to their field of study |
| B10 |
CB05 - The students will acquire learning abilities to allow them to continue studying in way that will mostly be self-directed or autonomous |
| C2 |
CT02 - Command in understanding and expression, both in oral and written forms, of a foreign language |
| C3 |
CT03 - Use of the basic tools of Information and Communications Technology (ICT) required for the student's professional practice and learning along her life |
| C7 |
CT07 - Developing the ability to work in interdisciplinary or cross-disciplinary teams to provide proposal that contribute to a sustainable environmental, economic, political and social development |
| C8 |
CT08 - Appreciating the importance of research, innovation and technological development in the socioeconomic and cultural progress of society |
| Learning aims |
| Learning outcomes |
Study programme competences |
| Know, understand and analyse the different models for web search and mining |
AC2
AC3
|
BC3
BC4
BC6
|
|
| Know, understand and analyse the different models for semantic technologies |
|
BC1
BC7
|
CC3
CC7
CC8
|
| Know, understand and analyse the software platforms for the creation of these systems |
AC1
|
BC10
|
CC2
|
| Know techniques, methods and good practices for the representation and publication of data and their subsequent consultation, using semantic technologies |
AC2
AC3
|
BC1
BC6
|
|
| Design, implement and know how to use algorithms and data structures for recommendation systems |
AC2
|
BC7
|
CC7
CC8
|
| Contents |
| Topic |
Sub-topic |
| 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 |
|
| Planning |
| Methodologies / tests |
Competencies |
Ordinary class hours |
Student’s personal work hours |
Total hours |
| Problem solving |
A2 A3 A4 B1 B3 B6 B7 B10 C2 C3 C7 |
11 |
55 |
66 |
| Laboratory practice |
A2 A3 A4 B1 B3 B6 C2 C7 |
10 |
30 |
40 |
| Mixed objective/subjective test |
A2 A3 A4 B4 C8 |
2 |
0 |
2 |
| Guest lecture / keynote speech |
A2 A3 A4 B1 |
21 |
21 |
42 |
| |
| Personalized attention |
|
0 |
0 |
0 |
| |
| (*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
| Methodologies |
|
Methodologies |
Description |
| Problem solving |
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. |
| Laboratory practice |
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. |
| Mixed objective/subjective test |
Final exam |
| Guest lecture / keynote speech |
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. |
| Personalized attention |
|
Methodologies
|
| Problem solving |
| Laboratory practice |
|
| Description |
| Monitoring of the development of the practices in the reserved hours of laboratory and attention to the student in the necessary cases of problems of particular difficulty |
|
| Assessment |
|
Methodologies
|
Competencies |
Description
|
Qualification
|
| Laboratory practice |
A2 A3 A4 B1 B3 B6 C2 C7 |
Evaluation of practical works |
50 |
| Mixed objective/subjective test |
A2 A3 A4 B4 C8 |
Final exam |
50 |
| |
|
Assessment comments |
It will be necessary to reach 40% of the score in each part. The evaluation will be considered as not presented when no practical work or final exam is submitted. Second opportunity The evaluation will be carried out with the same criteria described above. A new deadline will be opened for the delivery of the practical works, in the event that they are not delivered at the first opportunity. |
| Sources of information |
|
Basic
|
|
|
- W.B. Croft, D. Metzler, T. Strohman. 2009 Search Engines. Information Retrieval in Practice Pearson Education - C.D. Manning, P. Raghavan, H. Schutze. 2008 Introduction to Information Retrieval Cambridge University Press - Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The semantic web. Scientific american, 284(5), 34-43. - Gomez-Pérez, A., Fernández, M., Corcho, O. (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-enterprise-applications/. 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. - R. Baeza-Yates and B. Ribeiro-Neto. 2011 Modern Information Retrieval (second edition) Addison Wesley/Pearson Education - F. Cacheda, J.M. Fernández, J. Huete (eds.) 2011 Recuperación de Información. Un enfoque práctico y multidisciplinar Ra-Ma |
| Recommendations |
| Subjects that it is recommended to have taken before |
| Natural Language Understanding/614544008 |
|
| Subjects that are recommended to be taken simultaneously |
| Language Modelling/614544009 |
|
| Subjects that continue the syllabus |
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