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Course Fact Sheet

Master in Informatics and Computing Engineering
Information Description, Storage and Retrieval
Instance: 2017/2018

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General Information

Course Unit: Information Description, Storage and Retrieval
Code: EIC0108
Programmes: MIEIC, 5º
Academic Year: 2017/2018
Semester: 1S
Credits: 6 ECTS
Hours/Weeks: 3 TP
Teachers: Cristina Ribeiro | João Correia Lopes

Teaching language

Suitable for English-speaking students



The "Information Description, Storage and Retrieval" unit assumes as its context the existence of large collections of heterogeneous information which needs to be organized, described, stored and retrieved.


  1. Make the students aware of the main issues in the organization and storage of large data collections.
  2. Make the students familiar with the main concepts in textual information retrieval and their application in retrieval tools.
  3. Explore the semantic web methods and tools, and use web resources and their descriptions in applications that make use of data semantics.

Learning outcomes and competences

On completion of this course, the student should be able to:

  • Identify data sources in data repositories, online services APIs and user logs;
  • Decide on the quality of the data sources and briefly characterize a selected dataset;
  • Choose the document granularity and a storage model for the dataset;
  • Use data manipulation tools to select appropriate data subsets and to fit the data to their intended applications;
  • Describe the models used in information retrieval, specifically in web retrieval;
  • Recognize the various tasks considered in information retrieval;
  • Apply information retrieval evaluation measures to the comparison of web retrieval tools;
  • Relate web documents with the metadata that describes or links them;
  • Treat ontologies as providers of description tools;
  • Explore the applications which manipulate semantic web information descriptions and create metadata sets for a chosen domain;
  • Compare semantic web- based services with simpler approaches to resource description.

Working method

In attendance

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Knowledge and practice in programming languages ​​for application development.


  • Introduction to datasets; tools for dataset collection, preparation and access; data models and dataset storage.
  • Text information retrieval; retrieval models; evaluation; web information retrieval.
  • Information description: semantic web languages; RDF, RDF-Schema, OWL; ontologies for data in a domain.

Mandatory bibliography

  • Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze;Introduction to Information Retrieval, Cambridge University Press, 2008. ISBN: 0521865719 (Library)
  • Anders Møller, Michael I. Schwartzbach; An Introduction to XML and Web Technologies, Addison Wesley Professional, 2006. ISBN: 0321269667 (Library)

Teaching methods and learning activities

Lectures include theoretical presentation of the course subjects and practical sessions where proposed research topics are discussed with the students and practical coursework reported.


  • Apache Lucene + Solr
  • Protégé


Physical sciences > Computer science > Informatics

Type of assessment

Distributed evaluation without final exam

Assessment and amount of time allocated

Description Type Time (hours) Date of conclusion
Attendance (estimated) Lectures 36
1st Project Delivery Project Work 15 12/10/2017
2nd Project Delivery Project Work 30 09/11/2017
3rd Project Delivery Project Work 30 14/12/2017
Test 1 Test/Examination 1 14/11/2017
Test 2 Test/Examination 1 02/01/2018
Study Study 49
Total: 162

Eligibility for exams

The course has a practical component which results from the execution of projects, to be delivered up to the due dates established in the course plan. The students pass the course if they achieve 50% in each component of the project work. Success in the course also requires 40% in each intermediate written test.

Working students and students with similar status who are not required to participate in class must present the evolution of their work in the time periods defined with teachers. These students are also required to take the mini-tests, deliver their practical work and participate in the presentation sessions.

Calculation formula of final grade

The final grade is computed using the formula: GRADE= 60% Projects + 40% Tests.

The Projects component is the result of the practical evaluation and can be obtained:

  • completing three practical assignments according to the proposed scripts;
  • proposing a semester-long project and reporting its results in the same sessions as the assignments.

The classification of each component of the Project may be different for each member of the group, depending on their participation in the presentation and discussion session.

The project and its workplan must be validated by the course instructors.


All students have to complete the projects and present them as scheduled.

Special evaluation (TE, DA, ...)

Distributed evaluation, performed during the semester, is required of all students, regardless of their enrollment status.

Classification improvement

Improving the classification requires a new enrollment in the course, taking the course projects and tests again.


teach/dapi/201718/sheet.txt · Last modified: 06/09/2018 14:17 by Correia Lopes

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