Fall 2016

CS586 "Data and Web Semantics"

Schedule: T-R 12:30-1:45 pm

News Welcome! The Blackboard web site will contain further information for this course (but no information is in there yet).

This page constitutes the preliminary syllabus for the course. Changes will be made as the semester unfolds.


Instructor: Professor Isabel F. Cruz, SEO 1134
Contact email:
ifc AT cs DOT uic DOT edu (please mention CS586 in the subject of the message).

Additional help: Booma Sowkarthiga Balasubramani bbalas3 AT uic DOT edu

Who can attend this course

This course is mainly intended for PhD students. Other students intending to register must contact the instructor before classes start to obtain authorization to register.

The course and its objectives

The course is part of the Data Science Curriculum at UIC. Data Science consists of the following disciplines: Data Modeling, Data Management, Data Extraction, Data Visualization, and Data Analytics. This course touches on all of these areas with the particular aim to prepare students to undertake research in the important subjects that comprise the Semantic Web research area. Material will be formally covered following the textbook, to be presented in class or assigned as reading. There may be course projects representative of current research and development in the Semantic Web area, especially designed to further the students' understanding of the main research topics. The exam will be comprehensive of all the material taught in the course.

Recommended background

  • Be a PhD student at UIC. This course can be used as part of the course requirements for the PhD qualifier.
An advanced undergraduate course or a graduate course (or equivalent experience) in the following area is required:

  • Databases (CS480 or equivalent)
Further, the following courses can help:

  • Information Retrieval
  • Data Mining
  • Artificial Intelligence
  • Visual Analytics
  • Machine Learning
  • Data Science
Office Hours and Contact with the Instructor

Office hours will be by appointment only. To communicate with the instructor use email above (please mention CS586 in the subject of the message).

Research Topics

List of possible topics:

1. Algorithms (including structural based similarity measures, context-based matching)
2. Reasoning
3. Information visualization and visual analytics
4. Linked open data
5. Instance and ontology matching
6. Association rule mining using ontologies
7. Spatial ontologies and reasoning

Readings and References

There is a recommended book for the course:

  • A Semantic Web Primer by Grigoris Antoniou, Paul Groth, Frank van Harmelen, and Rinke Hoekstra (The MIT Press, 3rd edition, 2012).
The full book will be covered in the exam(s), including those sections or chapters not explicitly covered in class.

Further readings will be posted as the semester unfolds. Specific readings for the projects will be suggested. We will be posting readings for an initial quizz taking place during the first two weeks of the course. The first assignment is due the second week of classes.

Class Outline (preliminary)

  • Lectures by instructor (or invited lecturers)
  • Topic presentations (by selected students)
  • Final project presentations (by selected students)

First Class: August 23.
Thanksgiving break: November 24-25
Last Class: December 9.
Note: Attendance of this class requires permanence on campus till December 9 (last day of exam week). No exceptions will be made.


A midterm and an individual exam. Assignments. Quizzes. Project (tentative). Class presentations.

Class participation is essential and is graded.

Grading components:

Major Components Components Deadline Grade Total




Project and/or class presentations (tentative) Intro to project and survey (in class presentation and written pages)




Midterm report



Demo and presentation (slides and delivery)



Final report



Class participation






Grades to the (tentative) project and presentations will be awarded as follows:

  • A: Comprehensive presentations, the quality expected at a conference. As for the project, it should have research value as to be presented at a conference or workshop (applied or theoretical).
  • B: Solid work with attention to detail. Work that is valuable for the class, but which will not necessarily be of interest to a wider audience.
  • C: Completed work but lacking the above qualities.

    Note: Only those projects/topic investigations earning an A will be selected for class presentation.
High quality work is expected both in substance (research depth for the project and breadth for the survey) and presentation (organization, formatting, and spelling). Work that does not satisfy these criteria will not receive a passing grade. Regular feedback will be provided by the instructor so that the students will have a good understanding of their progress. No late submissions are accepted.

All project reports should be submitted using LaTeX and bibliography should be prepared using BibTeX. Each bibliographic reference should be complete. The reports cannot have typos and need to be professionally prepared.

Course Policy

Cheating will not be tolerated in this course. In particular, individual work must be performed by the student alone and group projects must be performed only by the elements of the group. Note that plagiarism, including copying information from the web, is a form of cheating. Any form of cheating will result in immediate failing of the course. In addition, the case may be reported to the university.

Students are urged to check with the instructor on what constitutes proper and improper use of references and software both available in printed form or electronic form and on what constitutes proper and improper forms of collaboration and authoring. Understanding such distinctions will be extremely useful in a student's research or professional career.

-- %{ifcruz - 2016-08-19}%

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