Study programme competencies |
Code
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Study programme competences / results
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A5 |
Capacidade de comprender e saber aplicar o funcionamento e organización da internet, as tecnoloxías e protocolos de redes de nova xeración, os modelos de compoñentes, sóftware intermediario e servizos. |
A12 |
Capacidade para aplicar métodos matemáticos, estatísticos e de intelixencia artificial para modelar, deseñar e desenvolver aplicacións, servizos, sistemas intelixentes e sistemas baseados no coñecemento. |
B1 |
Capacidade de resolución de problemas. |
B5 |
Habilidades de xestión da información. |
B10 |
Capacidade para proxectar, calcular e deseñar produtos, procesos e instalacións en todos os ámbitos da enxeñaría informática |
B13 |
Capacidade para o modelado matemático, cálculo e simulación en centros tecnolóxicos e de enxeñaría de empresa, particularmente en tarefas de investigación, desenvolvemento e innovación en todos os ámbitos relacionados coa Enxeñaría en Informática |
B14 |
Capacidade para a elaboración, planificación estratéxica, dirección, coordinación e xestión técnica e económica de proxectos en todos os ámbitos da Enxeñaría en Informática seguindo criterios de calidade e ambientais |
B17 |
Capacidade para a aplicación dos coñecementos adquiridos e de resolver problemas en contornas novas ou pouco coñecidos dentro de contextos máis amplos e multidisciplinares, sendo capaces de integrar estes coñecementos |
B21 |
Posuír e comprender coñecementos que acheguen unha base ou oportunidade de ser orixinais no desenvolvemento e/ou aplicación de ideas, a miúdo nun contexto de investigación |
B22 |
Que os estudantes saiban aplicar os coñecementos adquiridos e a súa capacidade de resolución de problemas en contornas novas ou pouco coñecidos dentro de contextos máis amplos (ou multidisciplinares) relacionados coa súa área de estudo |
B23 |
Que os estudantes sexan capaces de integrar coñecementos e enfrontarse á complexidade de formular xuízos a partir dunha información que, sendo incompleta ou limitada, inclúa reflexións sobre as responsabilidades sociais e éticas vinculadas á aplicación dos seus coñecementos e xuízos |
B25 |
Que os estudantes posúan as habilidades de aprendizaxe que lles permitan continuar estudando dun modo que haberá de ser en gran medida autodirixido ou autónomo |
C4 |
Desenvolverse para o exercicio dunha cidadanía aberta, culta, crítica, comprometida, democrática e solidaria, capaz de analizar a realidade, diagnosticar problemas, formular e implantar solucións baseadas no coñecemento e orientadas ao ben común. |
C6 |
Valorar criticamente o coñecemento, a tecnoloxía e a información dispoñible para resolver os problemas cos que deben enfrontarse. |
C7 |
Asumir como profesional e cidadán a importancia da aprendizaxe ao longo da vida. |
C8 |
Valorar a importancia que ten a investigación, a innovación e o desenvolvemento tecnolóxico no avance socioeconómico e cultural da sociedade |
Learning aims |
Learning outcomes |
Study programme competences / results |
Know, understand and analyze different models of information retrieval and semantic web, techniques for their efficient implementation and their evaluation methodology. |
AJ5
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CJ6 CJ8
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Know, understand and analyze the software platforms used to create these systems. |
AJ5
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CJ6 CJ7 CJ8
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Design and build new systems or improve the existing ones. |
AJ5 AJ12
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BJ1 BJ5 BJ10 BJ13 BJ14 BJ17 BC1 BC2 BC5
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CJ6 CJ7
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Plan and perform the evaluation of information retrieval and semantic web systems. Analyze the evaluation results of the systems in order to improve their efficiency and effectiveness. |
AJ5
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BJ1 BJ5
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CJ6 CJ7
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Be able to treat correctly the ethical, privacy, confidentiality and security aspects of these systems. |
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BC3
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CJ4 CJ6
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Contents |
Topic |
Sub-topic |
Introduction |
Information Retrieval and Search Engine Architecture |
Information gathering
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Crawling and feeds. |
Text and Web page processing |
Text pre-processing and parsing. Anchor text and Web link analysis, internationalization. |
Indexes and ranking.
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Building and compressing indexes. Efficient query processing. |
Query formulation and results presentation.
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Formulation and query re-writing. Snippets. Results visualization. |
Information Retrieval Models.
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Boolean, Vector-space, probabilistic, language models.
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Evaluation |
Evaluation of Information Retrieval Systems. Evaluation campaigns. Efficiency and effectiveness metrics. Evaluation design: training, test, statistical significance. Crowd-sourced evaluation. |
Text mining. |
Document clustering and classification |
Distributed and Social search.
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Federated search and distributed search. Blogs, micro-blogs and social networks. |
Recommender systems |
Collaborative filtering. Models and algorithms for recommendation. Recommender systems. |
Planning |
Methodologies / tests |
Competencies / Results |
Teaching hours (in-person & virtual) |
Student’s personal work hours |
Total hours |
Workbook |
A5 A12 B1 B5 B10 B13 B14 C4 C6 C7 C8 |
1 |
15 |
16 |
Laboratory practice |
B10 B17 B21 B22 B23 B25 |
20 |
30 |
50 |
Problem solving |
A5 A12 B1 B5 B13 B14 B17 B21 B22 B23 |
4 |
12 |
16 |
Mixed objective/subjective test |
A5 A12 B1 B5 B10 B13 B14 C4 C6 C7 C8 |
2 |
18 |
20 |
Guest lecture / keynote speech |
A5 A12 B1 B5 B10 B13 C4 C6 C7 C8 |
16 |
32 |
48 |
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Personalized attention |
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0 |
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0 |
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(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
Methodologies |
Methodologies |
Description |
Workbook |
Readings in order to consolidate and complement the knowledge and skills acquired. |
Laboratory practice |
Labs assignments dealing with development platforms in commercial use (Lucene, Terrier, Nutch, Jena, Protege, Pellet) |
Problem solving |
Problems and short questions to consolidate the contents presented in the master classes. |
Mixed objective/subjective test |
Test about the fundamental contents of the subject. |
Guest lecture / keynote speech |
The student will attend to the lectures given by the teacher about the different techniques, models and algorithms related to Information Retrieval and the Wemantic Web. The teacher will employ different levels of abstraction-detail and will guide the student in the fundamental and complementary readings. |
Personalized attention |
Methodologies
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Laboratory practice |
Problem solving |
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Description |
Control of the development of the labs assignment in the allocated lab hours, and the teacher will pay special attention to the student in particularly difficult problems, if necessary.
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Assessment |
Methodologies
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Competencies / Results |
Description
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Qualification
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Laboratory practice |
B10 B17 B21 B22 B23 B25 |
Control of the labs assignments and evaluation of the results achieved. |
50 |
Mixed objective/subjective test |
A5 A12 B1 B5 B10 B13 B14 C4 C6 C7 C8 |
Questions related to the knowledge acquired. Questions that involve reasoning over the knowledge acquired, that involve practical problem-solving on real life issues related to Information Retrieval and the Semantic Web. |
50 |
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Assessment comments |
Partial time students have the same scale of qualifications and continuous assessment as other students
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Sources of information |
Basic
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Bob DuCharme (2011). Learning SPARQL. O'Reilly
C.D. Manning, P. Raghavan, H. Schutze. (2008). Introduction to Information Retrieval. Cambridge University Press
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
W.B. Croft, D. Metzler, T. Strohman. (2009). Search Engines. Information Retrieval in Practice. Pearson Education
John Hebeler, Matthew Fisher, Ryan Blace, Andrew Perez-Lopez, Mike Dean. (2009). Semantic Web Programming. Wiley |
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Complementary
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Recommendations |
Subjects that it is recommended to have taken before |
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Subjects that are recommended to be taken simultaneously |
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Subjects that continue the syllabus |
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