Ontological Engineering: Difference between revisions
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This course is an on-line compilation of video materials from [[Ontological Engineering 2013]] and [[Ontological Engineering 2014]] taught in the University at Buffalo Departments of Philosophy and Industrial Engineering in 2013 and 2014. The course provides an introduction to the methods and uses of ontological engineering, focusing on applications in areas such as military intelligence, healthcare, and document processing. It provides an overview of how ontologies are created and used. It also addresses some of the human factors underlying the success and failure of ontology projects, including issues of ontology governance and dissemination. | This course is an on-line compilation of video materials from [[Ontological Engineering 2013]] and [[Ontological Engineering 2014]] taught in the University at Buffalo Departments of Philosophy and Industrial Engineering in 2013 and 2014. The course provides an introduction to the methods and uses of ontological engineering, focusing on applications in areas such as military intelligence, healthcare, and document processing. It provides an overview of how ontologies are created and used. It also addresses some of the human factors underlying the success and failure of ontology projects, including issues of ontology governance and dissemination. | ||
The course is built out of | The course is built out of fifteen 3-credit-hour sessions. | ||
== '''Background''' == | == '''Background''' == | ||
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==2: Big Data and How to Overcome the Problems it Causes== | ==2: Big Data and How to Overcome the Problems it Causes== | ||
We are living in a world of big data. To find our way around this world, we need to identify and integrate the data that is important to our needs. The problem is that data is collected always from different perspectives, with different levels of detail, different granularities for example of space and time, and different communities use different technologies and different terminologies when collecting their data. This session provides an introduction to the problems of data fusion. | |||
[http://ncor.buffalo.edu/ | *How to Integrate Data | ||
*[http://ncor.buffalo.edu/2013/IE500/4-How-to-integrate-data.pptx Slides] &[http://ncor.buffalo.edu/2013/IE500/Videos/4-Integrating-Data.mp4 Video] | |||
*Ron Rudnicki: The CUBRC - US Army Ontology Collaboration | |||
*[http://ncor.buffalo.edu/2013/IE500/20-Ontologies-for-the-Intelligence-Community.pptx Slides] | |||
*[http://ncor.buffalo.edu/2013/IE500/Videos/20-AIRS-ontologies.mp4 Video] | |||
*Ron Rudnicki: The CUBRC - US Army Ontology Collaboration | |||
<!--*Lab 2: Protégé, building the taxonomy, introduction to defining classes with OWL [http://ncor.buffalo.edu/2013/IE500/Labs/Lab-2.pptx Slides] [http://ncor.buffalo.edu/2013/IE500/Videos/Labs/Ron-2.mp4 Video]--> | <!--*Lab 2: Protégé, building the taxonomy, introduction to defining classes with OWL [http://ncor.buffalo.edu/2013/IE500/Labs/Lab-2.pptx Slides] [http://ncor.buffalo.edu/2013/IE500/Videos/Labs/Ron-2.mp4 Video]--> | ||
Revision as of 23:03, 22 May 2016
Instructor: Barry Smith
Office hours: By appointment via email at [1]
The Course
This course is an on-line compilation of video materials from Ontological Engineering 2013 and Ontological Engineering 2014 taught in the University at Buffalo Departments of Philosophy and Industrial Engineering in 2013 and 2014. The course provides an introduction to the methods and uses of ontological engineering, focusing on applications in areas such as military intelligence, healthcare, and document processing. It provides an overview of how ontologies are created and used. It also addresses some of the human factors underlying the success and failure of ontology projects, including issues of ontology governance and dissemination.
The course is built out of fifteen 3-credit-hour sessions.
Background
Ontologies are an important tool in all areas where data is collected and described by different groups in different ways. Ontologies provide taxonomy-based computerized lexica used to describe diverse bodies of data. They thereby help to aggregate and compare data, to make data more easily discoverable, and to allow large bodies of data to be more effectively searched and analyzed. Ontologies also play an important role in the so-called Semantic Web, where the Web Ontology Language (OWL) forms a central building block in the stack of web technology standards created by the World Wide Web Consortium (W3C).
UB ontologists are involved in a variety of national and international projects in the military, healthcare, bioscience, transport and financial domains. There is an acknowledged shortage of persons with ontological engineering expertise in all these fields, and in related fields such as journalism, manufacturing and government administration.
1: Introduction to Ontology
- Ontology: A Brief Introduction Slides Video
- Ontology: From Philosophy to Engineering Slides Video
- Tanya Malyuta (CUNY): Ontologies vs. Data Models Slides Video
- Tanya Malyuta (CUNY): Horizontal Integration of Intelligence Data Slides Video
2: Big Data and How to Overcome the Problems it Causes
We are living in a world of big data. To find our way around this world, we need to identify and integrate the data that is important to our needs. The problem is that data is collected always from different perspectives, with different levels of detail, different granularities for example of space and time, and different communities use different technologies and different terminologies when collecting their data. This session provides an introduction to the problems of data fusion.
September 15 An Introduction to Basic Formal Ontology
- Why a standard ontology architecture is needed
- An introduction to Basic Formal Ontology (BFO)
- BFO and its competitors
- Building ontologies with BFO
Background
Lab
September 22: Use of Ontologies in Tracking Systems
Presenter: Werner Ceusters
A referent tracking system (RTS) is a special kind of digital information system that is designed to keep track of both (1) what is the case in reality and (2) what is expressed in other information systems about what is believed to be the case in reality. An RTS also keeps track of how changes in the information system correspond to changes in the reality outside that system. We will provide an introduction to referent tracking and its implementations. Reading: How to track absolutely everything?
Background
- Basics of Referent Tracking (RT) Video
- RT and Video Surveillance Video
- RT and Data descriptions Video
Lab
September 29: How to Build an Ontology
An overview of ontology research in Buffalo
How to build an ontology 1
- Military ontology
Lab
October 6: Creating Ontologies That Work Together
How to build an ontology 2 Slides Video
- The Airs Suite of Ontologies
- Annotating Intelligence Data
- Information Artifacts: Publications, databases, passports, emails
- The Email Ontology
- Minimal Information Checklists
Background
- An Introduction to BFO's Treatment of Information Artifacts Slides Video
- [Information Ontologies for the Intelligence Community http://ncorwiki.buffalo.edu/index.php/STIDS_2013]
Lab
October 13: Ontology and Information Engineering in the Healthcare Domain
Health care today rests increasingly on the proper use of data deriving from different sources (data pertaining to genes, diseases, symptoms, drugs, medical devices, procedures, hospital infections and other adverse events, hospital management, billing, reporting, and many more). We provide an introduction to the ontology of disease, with special reference to the phenomenon of aging.
Ontology for General Medical Science Slides Video
Background
- Informatics and Obamacare: Slides Video
- Electronic Diseases: Slides
- Genomics: Gene Ontology Annotations
- Electronic Health Records: Disease and Diagnosis
- Biodiversity: Semantics of Biodiversity
- Healthcare Information Management: Slides
- Strategies for Data Integration: Slides
Lab
October 20: The Science of Document Informatics
What is a document? Slides Video (to be edited)
What can we do with documents?
What can we do with digital documents that we can't do with paper documents?
What is a diagram?
How can we extend the technology of optical character recognition (OCR) to comprehend also the graphical content of documents?
Background:
- Mining images in biomedical publications
- Finding and accessing diagrams in biomedical publications
- Document Acts and the Ontology of Social Reality
Ontology projects to be discussed
Lab
October 27: The Semantic Web
Presentations by Alan Ruttenberg
- Semantic Web Vision and History [shttp://ncor.buffalo.edu/2014/IE500/9-Vision-History.pptx Slides] [2]
- Technology of the Semantic Web Slides Video
Background
- The term "Semantic Web" was introduced by Tim Berners-Lee and others in the late 1990's (1, 2) and first popularized in a paper in 2001 in Scientific American (see below). Berners-Lee summarizes the idea as "a web of data that can be processed directly and indirectly by machines", an extension of the web of documents primarily intended for consumption by people.
- Applications Slides
- Tim Berners Lee Scientific American Articles :
November 3: Ontology Examples
Topics will include:
- Background on Question Ontology
- Background on Engineering Ontology
Lab
November 10: Finance Ontology
Background
- Dennis E. Wisnosky: Video
- Background slides on FIBO, the Financial Industry Business Ontology
November 17: The Ontology of Plans
The Ontology of Planning Slides
Background
Erik Thomsen, William Duncan, Tatanya Malyuta and Barry Smith, “A Computational Framework for Living Plan Specification, Execution and Evaluation”, Proceedings of the Conference on Semantic Technology in Intelligence, Defense and Security (STIDS), George Mason University, Fairfax, VA, November 18-20, 2014.
Kym S. Pohl and Peter Morosoff, "ICODES: A Load-Planning System that Demonstrates the Value of Ontologies in the Realm of Logistical Command and Control (C2)", InterSymp-2011, Baden-Baden, Germany, 2 Aug, 2011.
Jens Pohl, "An Intelligent Supply Chain Planning and Execution Environment" Proceedings of InterSymp-2011: Baden-Baden, Germany. Aug. 2011.
Austin Tate, Towards a Plan Ontology (1996)
Austin Tate Plan Ontology Page
Planning Initiative Shared Planning and Activity Representation - SPAR (1997)
Philip R. Cohen and C. Raymond Perrault, Elements of a Plan‐Based Theory of Speech Acts (1979). Summarized here
Massively Planned Social Agency
November 24: Presentations of Student Projects 1
1. Philip Odonkor: Energy Ontology for Net-Zero Buildings
2. Jeon-Young Kang: An Ontology for Capturing Change
3. Joana Monteiro: Victim Management Ontology
December 1: : Presentations of Student Projects 2
1. Lauren Madar: Retail Banking Ontology
2. Cameron Bosinski: Question Ontology
3. Fumiaki Toyoshima: University Ontology
4. Keith Fitzsimmons: Lathe Maintenance Ontology
Examples of Student Projects from 2013
- Jordan Feenstra and Yonatan Schreiber: Music Ontology
- Yi Yang and Jeon-Young Kang: GIS Ontology
- David Lominac: Customer Ontology
- Lucas Mesmer: Manufacturing Ontology
- Travis Allen: Twitter Ontology
- Chad Stahl: Chemical Manufacturing Ontology
- Brian Donohue and Neil Otte: Personality Ontology
- Kevin Cui: GIS Data Model Ontology
- Xinnan Peng: Manufacturing Ontology
- John Beverley: Thermodynamic Equilibrium Ontology
- Paul Poenicke: Gettier Problem Ontology
- Adam Houser: Game Artifact Ontology
- William Hughes and Michael Moskal: Unmanned Aerial Vehicle Ontology
- Kanchan Karadkar: Supply Chain Management Ontology
- Norman Sung: Musical Genre Ontology
Guidance for Presentations and Reports
- Examples of what to include
- Statement of scope of the ontology
- The true path rule
- Identification of existing ontologies
- Explanation of how your ontology differs from (or incorporates) these
- Screenshots of parts of the ontology with some examples of important terms and definitions
- Summaries of potential applications of the ontology
- Evaluation
- Completeness
Grading and Related Policies and Services
All students will be required to take an active part in class discussions throughout the semester. In addition they will be required to design and complete an ontology project, including written description, and brief presentation of the project in class. Students enrolled in the practical segment will be required to create a Protégé file to accompany their ontology project, and also to complete quizzes designed to gauge developing competence in the use of the Protégé Ontology Editor and SPARQL query language.
For 3 credit hour students, your grade will be determined in five equal portions deriving from:
- 1. class participation (1.5% per class attended),
- 2. results of two quizzes relating to the lab portion of the course
- 3. written description of ontology project (3000 words; deadline December 2),
- 4. Protégé ontology file (deadline November 25),
- 5. class presentation.
For 2 credit hour students, your grade is determined as follows:
- 1. class participation (1.5% per class attended),
- 2. written description of ontology project (4000 words; deadline December 2) (50%),
- 3. class presentation (30%).
For policy regarding incompletes see here
For academic integrity policy see here
For accessibility services see here
