Study programme competencies |
Code
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Study programme competences
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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. |
A9 |
Capacidade para deseñar e avaliar sistemas operativos e servidores, e aplicacións e sistemas baseados en computación distribuída. |
B1 |
Capacidade de resolución de problemas. |
B3 |
Capacidade de análise e síntese. |
B5 |
Habilidades de xestión da información. |
B7 |
Preocupación pola calidade. |
B9 |
Capacidade para xerar novas ideas (creatividade). |
C2 |
Dominar a expresión e a comprensión de forma oral e escrita dun idioma estranxeiro. |
C3 |
Utilizar as ferramentas básicas das tecnoloxías da información e as comunicacións (TIC) necesarias para o exercicio da súa profesión e para a aprendizaxe ao longo da súa vida. |
C5 |
Entender a importancia da cultura emprendedora e coñecer os medios ao alcance das persoas emprendedoras. |
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 |
Subject competencies (Learning outcomes) |
Study programme competences |
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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BJ3
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CJ2 CJ6 CJ8
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Know, understand and analyze the software platforms used to create these systems. |
AJ5
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BJ3
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CJ2 CJ3 CJ6 CJ7 CJ8
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Design and build new systems or improve the existing ones. |
AJ5 AJ9
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BJ1 BJ3 BJ5 BJ9
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CJ3 CJ5 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 AJ9
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BJ1 BJ5 BJ7
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CJ3 CJ5 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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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. |
Introduction to the Semantic Web
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Semantic Web. Ontologies, definition, types and examples. |
Description and resource querying.
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XML, RDF and RDF Schema languages. SPARQL query language. OWL language. Tools for ontology development. Libraries for ontology management. RDF repositories. |
Reasoning and rules.
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Formal logic and reasoning foundations. Semantic rule representation. Reasoning engines. |
Semantic Web applications.
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Linked Data, FOAF, Dublin Core, WordNet. Semantic annotations. Semantic Search. Semantic Web services. |
Planning |
Methodologies / tests |
Ordinary class hours |
Student’s personal work hours |
Total hours |
Workbook |
1 |
15 |
16 |
Laboratory practice |
20 |
30 |
50 |
Problem solving |
4 |
12 |
16 |
Mixed objective/subjective test |
2 |
18 |
20 |
Guest lecture / keynote speech |
16 |
32 |
48 |
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Personalized attention |
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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Description
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Qualification
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Laboratory practice |
Control of the labs assignments and evaluation of the results achieved. |
50 |
Mixed objective/subjective test |
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 |
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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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