Checking date: 04/06/2021


Course: 2021/2022

Information access and retrieval
(15760)
Study: Dual Bachelor in Computer Science and Engineering, and Business Administration (233)


Coordinating teacher: MORATO LARA, JORGE LUIS

Department assigned to the subject: Department of Computer Science and Engineering

Type: Compulsory
ECTS Credits: 6.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
- Files and Data bases (Bachelor in Informatics Engineering, 2nd Course, Semester 2nd, Compulsory) - Object oriented programming (Bachelor in Informatics Engineering, 1st Course, 2nd Semester, Compulsory)
Objectives
CESI1 Ability to integrate Information and Communication Technology solutions and business processes to meet the information needs of organizations, enabling them to achieve their objectives effectively and efficiently, thus giving them competitive advantages. CESI3 Ability to actively participate in the specification, design, implementation and maintenance of information and communication systems. CESI6 Ability to understand and apply the principles and techniques of quality management and technological innovation in organizations. CGB4 - Basic knowledge on the use and programming of computers, operating systems, databases and computer programs with application in engineering. CG5 - Use general purpose, collaborative and work optimization tools for effective project planning and implementation. CG7 - Be able to present and discuss proposals in team work, demonstrating personal and social skills that allow him/her to assume different responsibilities within them. CG9 - Efficiently use ICT means to write technical reports and memories of projects and works on Computer Science, as well as quality presentations. CGO9 - Ability to solve problems with initiative, decision making, autonomy and creativity. Ability to know how to communicate and transmit the knowledge, skills and abilities of the profession of Technical Engineer in Computer Science. Specific Competences: - Cognitive 1. Retrieval models 2. Natural Language Processing Techniques 3. Systems to formalize, synthesize, and structure information 4. Traceability systems 5. Ability to show results in an appropriate way 6. Improve retrieval and knowledge reuse systems in the Web and in Software Engineering - Procedimental/Instrumental Competences 1. Design of Retrieval Systems 2. Design of natural language analyzers 3. Application of text mining techniques to improve the representation and sorting of results
Skills and learning outcomes
Description of contents: programme
Description: Retrieval Models, Natural Language Processing, semantic analysis, metadata, linked data, information retrieval, positioning techniques, knowledge reuse, data mining The course examines fundamental concepts about retrieval systems, introducing a variete of basic techniques. This includes the use of knowledge organization systems, positioning techniques, natural language processing techniques and resources, and evaluation by retrieval metrics. Course content, 3 units: Unit 1. Information retrival - Lesson 1: Search basics in different web types: classic web, Semantic Web, Social Web, Data Web, Dark Web, Deep Web, question-answering web, and commercial web. - Lesson 2: Search Engine Optimization (SEO/SEM) - Lesson 3. Basic information retrieval models - Lesson 4: Access, acquisition and cleansing of semantic web data and bigdata - Lesson 5. Crawlers, scrapers and search engine arquitecture Unit 2. Retrieval evaluation - Lesson 6. Evaluation metrics for information retrieval systems Unit 3. Advanced techniques for information retrieval systems - Lesson 7. Natural Language Processing (NLP) - Lesson 8. Information extraction techniques (IE) - Lesson 9. Relevance feedback and query expansion
Learning activities and methodology
Lectures (theory): 1.6 ECTS. To achieve the specific cognitive competences of the course Lectures (practices): To develop the attitudinal and specific competences as well as most of the general ones, such as collaborative teamwork, skills to apply theoretical concepts, design planning, information organization, analysis, and abstraction. Students must design and develop an information retrieval system. Workshops and labs 0.2 ECTS, individual or group exercises 3 ECTS Tutorials to solve practical and theoretical questions 1 ECTS Exercises and examination: 0.2 ECTS. The goal is to complete the development of specific cognitive and procedural capacities. Exercises and results of the practices will be discussed in class.
Assessment System
  • % end-of-term-examination 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60
Calendar of Continuous assessment
Basic Bibliography
  • C. Manning, P. Raghavan y H. Schütze. Introduction to Information Retrieval. . Cambridge University Press. 2008
  • Dean Allemang, James Hendler. Semantic Web for the Working Ontologists: Effective Modeling in RDFS and OWL. Elservier. 2011
  • J. Urbano, M. Marrero, D. Martín y J. Morato. Bringing Undergraduate Students Closer to a Real-World Information Retrieval Setting: Methodology and Resources. ACM SIGCSE ITiCSE. 2011
  • R. Baeza-Yates y B. Ribeiro-Neto. Modern Information Retrieval: The Concepts and Technology behind Search (2nd edition). Addison Wesley. 2011
  • Verborgh, R., De Wilde, M., & Sawant, A.. Using OpenRefine: The essential OpenRefine guide that takes you from data analysis and error fixing to linking your dataset to the web. Packt Publishing. 2013
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • Dale R. Handbook of Natural Language Processing. Marcel Dekker. 2000
  • Ian H. Witten, Alistair Moffat and Timothy C. Bell. Managing Gigabytes: compressing and indexing documents. Morgan Kauffman. 1999
  • Moens Marie-Francine. Information Extraction: algorithms and prospects in a retrieval context (Chps. 1, 2 & 4). Springer. 2006
  • Morato, J, Sánchez-Cuadrado, S, Moreno, V Moreiro JA . Evolución de los factores de posicionamiento web y adaptación de las herramientas de optimización. Revista española de Documentación Científica, Vol 36, No 3. 2013
  • Nadeau D. and Sekine S.. A survey of named entity recognition and classification. Linguisticae Investigationes vol. 30 n.1. 2007
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN


The course syllabus may change due academic events or other reasons.