Ontology Engineering Fall 2022

Instructor: Professor Deborah McGuinness - dlm at cs dot rpi dot edu

Guest Lecturer: Ms. Elisa Kendall - ekendall at thematix dot com

Course Manager: Jade Franklin - frankj6 at rpi dot edu, Sola Shirai - shiras2 at rpi dot edu

Meeting Times: Monday afternoon 12:00 pm - 3:50 pm

Office Hours: By Appointment

Course Listing: Ontologies, CSCI 6340, CSCI 4340

Course Homepage: https://tw.rpi.edu/classes/ontology-engineering-fall-2022

Class Location: Winslow Building

Individual Project Locations

• TODO update following the format from last year's class

Description: This course provides an introduction to ontologies, their uses, and an overview of their application in semantically enabled systems. Ontologies, with their declarative encodings of meaning, can be used to improve communications between people and can enable computer programs to function more effectively. They provide the foundation for clear and unambiguous interaction. Ontologies have become increasingly common on the web, and class participants will not only learn about the use of ontologies in web-based applications but how to evaluate ontologies for reuse in such applications. Participants will read relevant papers, learn how to critically review ontology papers as well as ontologies themselves, and will participate in at least one group project designing, using, and evaluating ontologies.

Goal: To learn how to build human and machine-interpretable definitions of concepts and their relationships for use in knowledge-driven applications and automated systems.

Objectives:

• Learn what ontologies are, how to build them, and how to use them
• Learn what use cases are, how to construct them, and how to use them to capture requirements for ontology and applications development
• Learn about terminology work, including definition development, and how to use terminologies as the starting point for ontology development
• Learn about ontology languages and some existing ontology resources
• Learn about knowledge graphs and how ontologies are used in the context of a knowledge graph
• Learn how to design, implement, and evaluate an ontology project

Prerequisites:

• Basic knowledge of XML is expected
• Basic knowledge of Artificial Intelligence and the Web is expected
• CSCI 2300 Data Structures and Algorithms
• Note - If you are taking the class without the prerequisites, please read at least the first half of the Ontology Engineering Book and the first 4 chapters of Semantic Web for the Working Ontologist.

Reading List (from Fall 2020 course offering): https://docs.google.com/document/d/1C_kGqMXP8GNqmh0j2QkeMhI-2EeoTWTD67bchYlCiCA/edit?usp=sharing

Recommended Software:

• Protégé ontology editor - Available from the Stanford Center for Biomedical Informatics Research at the Stanford University School of Medicine, at http://protege.stanford.edu/products.php, Protégé 5.5 (desktop version)
• Atlassian Sourcetree - free desktop Git client for Mac and Windows, available at https://www.atlassian.com/software/sourcetree
• Concept Maps (Cmaps) tool from the Institute for Human and Machine Cognition (IHMC), at http://cmap.ihmc.us/ or an open source UML (Unified Modeling Language) tool of your choice
• All projects will be completed using Tetherless World public github accounts, linked to the course home page, and preserved for your reference in publications as well as for future students - template pages / accounts will be set up for each project after our 2nd class meeting.

Syllabus: https://docs.google.com/document/d/112pblup4uVHsrNR1O9_kTxv2N–XmMDE/

Assessment Criteria:

• Via written assignments
• Via oral presentations (individual and group)
• Via participation in class
• Via small group evaluation
• Late submission policy: first time with valid reason – no penalty, otherwise 20% of score deducted each late day

Attendance Policy: Attendance at all classes is expected. If you are ill and can not attend, please contact the professor in advance. The class includes many group participation activities and participation in class is included in the final grade evaluation. Missed classes are noted and will impact grades.

Remote Participation: We anticipate that this course will be either a combination of online and in person (hybrid) or entirely remote, depending on recommendations from the university and public health officials. We will use Cisco’s Webex tool as the basis for remote work and for at least some off-line tutoring and group work as needed. We expect live participation during the scheduled course time through the provided Webex system. Please go to the WebEx site, at https://help.webex.com/en-us/nrbgeodb/Join-a-Webex-Meeting and test your ability to participate prior to our first class meeting. There are instructions for most browsers and a video to help you get started. We expect your attention to be on the materials as they are being presented live, whether it is from the instructors or a presentation from another student. If you are participating remotely, we also expect you to get a web camera, if you don’t already have one, and have it on during the class. We also suggest testing your microphone and camera with webex in advance of the first class (see instructions in the FAQ on the Join a Webex Meeting page for instructions).
This class requires a group project. You should plan to make time to work with your group members outside of the regular class time, as necessary, to complete group assignments. You will have to either schedule these to be remote or in person, following the social distancing protocols set out by the campus.

Academic Integrity: The Rensselaer Handbook of Student Rights and Responsibilities and The Rensselaer Graduate Student Supplement define various forms of Academic Dishonesty and procedures for responding to them. All forms are violations of the trust between students and teachers. Student-teacher relationships are built on trust. For example, students must trust that teachers have made appropriate decisions about the structure and content of the courses they teach, and teachers must trust that the assignments that students turn in are their own performance. Acts that violate this trust undermine the educational process.
The Rensselaer Handbook of Student Rights and Responsibilities and The Rensselaer Graduate Student Supplement define various forms of Academic Dishonesty and you should make yourself familiar with these. In this class, all assignments that are turned in for a grade must represent the student’s own work. In cases where help was received, or teamwork was allowed, a notation on the assignment should indicate your collaboration. Submission of any assignment that is in violation of this policy will result in a penalty. If found in violation of the academic honesty policy, students may be subject to two types of penalty. The instructor administers an academic [grade] penalty and the student is reported to the Dean of Students or the Dean of Graduate Education as appropriate. The first violation results in 0 grade for that assignment. The second violation results in failure of the course. If you have any questions concerning this policy before submitting an assignment, please ask for clarification.

Students with Disabilities: Rensselaer Polytechnic Institute strives to make all learning experiences as accessible as possible. If you anticipate or experience academic barriers based on a disability, please let the instructors know immediately so that we can discuss your options. To establish reasonable accommodations, please register with The Office of Disability Services for Students. After registration, make arrangements with us as soon as possible to discuss your accommodations so that they may be implemented in a timely fashion. DSS contact information: dss@rpi.edu; 518-276-819; 4226 Academy Hall.