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Fall 2015
"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.

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

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

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

CS 586 is primarily intended for PhD students. CS586 can be used for the course requirements of the qualifier exam.

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 We will cover a variety of topics. Here are some of the possible topics that were covered in previous offerings of the course:

1. Algorithms (including structural based similarity measures, context-based matching)
2. Reasoning
3. Visualization
4. Linked open data
5. Structural ontology parsing
6. Pairing of algorithms with ontologies
7. Semantic explanation of ontology matching
8. Information matching
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).
Further readings will be posted as the semester unfolds. Specific readings for the projects will be suggested. Reading assignment for September 1. A Quizz will be given on September 1 on these readings:

(1) Textbook, Chapter 1.

(2) The following two papers:



author = {Fader, Anthony and Soderland, Stephen and Etzioni, Oren},

title = {{Identifying Relations for Open Information Extraction}},

booktitle = emnlp,

year = {2011},

pages = {1535--1545},




author = {Mausam and Schmitz, Michael and Bart, Robert and Soderland, Stephen and Etzioni, Oren},

title = {{Open Language Learning for Information Extraction}},

booktitle = emnlpconll,

year = {2012},

pages = {523--534},
} Textbook, Chapter 1.

Class Outline (preliminary)

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

First Class: August 25.
Thanksgiving break: November 26-27
Last Class: December 11.
Note: Attendance of this class requires permanence on campus till December 11 (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 (tentative):

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.

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.

Isabel F. Cruz

-- Main.ifcruz - 2015-08-24

Topic revision: r3 - 2016-08-19 - 22:54:09 - Main.ifcruz
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