Information Description, Storage and Retrieval
Instance: 2015/2016 - 1S
Cycles of Study/Courses
||No. of Students
||Syllabus since 2009/2010
Last updated on 2015-07-22.
Fields changed: Components of Evaluation and Contact Hours, Lingua de trabalho
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.
- Make the students aware of the main issues in the organization and storage of large data collections.
- Make the students familiar with the main concepts in textual information retrieval and their application in retrieval tools.
- 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.
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.
Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze; Introduction to Information Retrieval, Cambridge University Press, 2008. ISBN: 0521865719
Anders Møller, Michael I. Schwartzbach; An Introduction to XML and Web Technologies, Addison Wesley Professional, 2006. ISBN: 0321269667
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.
Oxygen XML Editor Academic Classroom
Physical sciences > Computer science > Informatics
Distributed evaluation without final exam
Amount of time allocated to each course unit
|Elaboração de projeto
|Frequência das aulas
Eligibility for exams
The course has a practical component which results from the execution of projects, to be delivered up to their due dates established in the course plan.
The students are admitted to the final exam if they achieve 50% in each component of the project work.
Success in the course requires 40% in each intermediate written test.
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 project and its workplan must be validated by the course instructors.
Examinations or Special Assignments
None. All students have to complete the projects and present them as scheduled.
Special assessment (TE, DA, ...)
Distributed evaluation, performed during the semester, is required of all students, regardless of their enrollment status.
Improving the classification requires a new enrollment in the course, taking the course projects and tests again.