The document outlines a course on knowledge engineering that is divided into three parts: conceptual data modeling, ontology engineering, and application scenarios for a final project, with each part covering readings, lectures, assignments, and exams on topics such as conceptual modeling, ontologies, and semantic applications.

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Introduction & Outline
Lecture Notes on Knowledge Engineering
Birzeit University
2011
Dr. Mustafa Jarrar
University of Birzeit
mjarrar@birzeit.edu
www.jarrar.info
Knowledge Engineering (SCOM7348)

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Course Outline
Part I: Conceptual Data Modeling 13
Part II: Ontology Engineering
24
Part III: Application Scenarios and Project
10
The course is divided into three parts:

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Course Outline
Part I: Conceptual Data Modeling
Reading: Information Modeling and Relational Databases: From Conceptual
Analysis to Logical Design, Terry Halpin (ISBN 1-55860-672-6)
Reading Hours
Introduction 1
Concepts & Principles of Conceptual Data Modeling ch.1-2 1
Conceptual Schema Design Steps (ch.3) 1
Project1 (3%) Discussion 1
Uniqueness Constraints (ch.4) 1
Mandatory Constraints (ch.5) 1
Project2 (3%)
Other Constrains (ch.6&ch.7) 1.5
Final Check and Schema Engineering Issues (ch.7&ch.12&articles 1.5
Project 3 (4%) Discussion 3
First Exam (10%) -- 6/10/2011 1
Total 13

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Course Outline
Part II: Ontology Engineering
Reading: Selected Papers + Lecture Notes
24
Introduction to Description Logics and Reasoning Articles [1, 2] 2
RDF and RDFS Article [3] 4.5
Ontology Web Language (OWL) Article [4] 3
Project 4 (5%) 1.5
What is an ontology and Lexical Semantics Article [5,6] 4.5
Ontology Engineering Methodologies Lecture Notes 4.5
Project 5 (5%) 3
Second Exam (10%) -- 8/12/2011 1
[1] D. Nardi, R. J. Brachman. An Introduction to Description Logics. In the Description Logic Handbook, edited by F. Baader, D. Calvanese, D.L.
McGuinness, D. Nardi, P.F. Patel-Schneider, Cambridge University Press, 2002, pages 5-44.
[2] Mustafa Jarrar: Mapping ORM into the SHOIN/OWL Description Logic- Towards a Methodological and Expressive Graphical Notation for
Ontology Engineering. In Volume 4805, LNCS, Pages (729-741), Springer. ISBN: 9783540768890.
[3]RDF Primer: RDF/XML Syntax Specification. W3C Recommendation 10 February 2004. http://www.w3.org/TR/rdf-syntax-grammar/
[4] OWL Web Ontology Language Guide. W3C Recommendation 10 February 2004 http://www.w3.org/TR/2004/REC-owl-guide-
20040210/#BasicDefinitions
[5] Chapter 2 and 3 in Jarrar, M.: Towards Methodological Principles for Ontology Engineering. PhD thesis, Vrije Universiteit Brussel (2005).
[6] Mustafa Jarrar: Towards the notion of gloss, and the adoption of linguistic resources in formal ontology engineering. In proceedings of the
15th International World Wide Web Conference (WWW2006). ACM Press. May 2006.

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Course Outline
Part III: Application Scenarios and Final Project
Reading: External Papers
10
Seminar and Discussion Panel on selected applications: (5%)
Application 1: Ontology based Data Integration 1
Application 2: Ontology Enhanced Search and Retrieval 1
Application 3: Semantic Web and RDFa and LinkedData 1
Application 4: Ontology-based e-governments 1
Application 5: Ontologies in Bioinformatics 1
Application 6: XXXX 1
Final Project 6 (20%) 3
Final Exam (35%) 2

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Course Rules
• Attendance. Attendance is mandatory. University regulations
regarding this matter will be strictly enforced.
• Academic Honesty: Individual work must be each student’s own
work. Plagiarism or cheating will result in official University disciplinary
review.
• Missed Exams: There are no makeup exams, and project deadlines
are very hard.
• Etiquette: Cell phones must be turned off. Don’t come late. If you
must go out during the lecture don’t let us notice.
• Ritaj: We only communicate through Ritaj. I assume you check it
several times a day.

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Good and bad stories last year
• Most students told me that they under-estimated the huge amount of efforts
required in this course, many project and skills to learn!
• The course assumes knowledge about database systems, XML, and HTML; if
not, you should learn it yourself quickly.
• Some students told me that I assume a high motivation and huger to learn
know technologies otherwise difficult to pass the course.
• Some students wrote scientific articles in international conferences during the
previous courses.
• Almost half of the students expressed their interest to conduct their thesis on
this topic (Rami, Rana, Hanya, Hiba, Yahya, Mohammad, and others).
• Most students told me that the relations among them was strengthen after this
course. Well, this is because they had to work too days and nights on projects!
We all work as a family and help each other.
• The final average of the class was 83%.
 I promise you to: learn modern technologies, enjoy every minute, private
teaching, and get high mark; but promise be very hard work.

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Motivating Discussion
• What is Knowledge? Data?
• What is Modeling?
• What is Conceptual Modeling?
• What is Conceptual Data Modeling?
• What is Ontology?