Information Description, Storage and Retrieval

Code:

EIC0108

Acronym:

DAPI

Keywords
Classification	Keyword
OFICIAL	Information Systems

Instance: 2017/2018 - 1S

Active?	Yes
Web Page:	http://www.fe.up.pt/~jlopes/doku.php/teach/dapi/index
Responsible unit:	Department of Informatics Engineering
Course/CS Responsible:	Master in Informatics and Computing Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MIEIC	20	Syllabus since 2009/2010	5	-	6	42	162

Teaching language

English

Objectives

BACKGROUND

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.

SPECIFIC OBJECTIVES

• 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.

Working method

Presencial

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

Knowledge and practice in programming languages for application development.

Program

• 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 literature

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.

Software

Apache Lucene
Apache Solr
Protégé

keywords

Physical sciences > Computer science > Informatics

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Teste	40,00
Trabalho escrito	60,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Elaboração de projeto	81,00
Estudo autónomo	48,00
Frequência das aulas	33,00
Total:	162,00

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.

Classification improvement

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