Applied Ontology 2018
PHI 598
An ontology is a structured collection of terms and definitions that is developed with the goal of making data deriving from heterogeneous sources more easily searchable, comparable or combinable. The course will provide an introduction to ontology from an application oriented point of view, including examples in the areas of data science and artificial intelligence. Examples will be drawn from biology and medicine, social science, law, and finance. The course will be of interest not only to philosophers but also to those interested in biomedical informatics and in the computer and information sciences.
Venue: 200G Baldy, UB North Campus
Faculty: Barry Smith and Werner Ceusters
Background reading:
- 1. Arp, Spear and Smith, 2016: Building Ontologies with Basic Formal Ontology, MIT Press, 2016
- 2. Please read in advance of August 27 class: Hoehndorf, Schofield & Gkoutos, 2015
August 27: Introduction to Ontology
- What is an ontology?
- Key elements of an ontology
- What are ontologies useful for?
Class assignment: write a 2-page essay documenting which of the key elements identified in the lecture are considered (or not considered) in: Hoehndorf, Schofield and Gkoutos, 2015. Deadline: noon, September 6.
Advance reading (prior to September 10 lecture): 1. Scheuermann, Ceusters and Smith, 2009. 2. Haendel et. al., 2018
September 3: Labor Day – No class
==September 10: Ontology of Disease
Class assignment: write a 2-page essay discussing the extent on which the framework offered by Scheuermann, Ceusters and Smith, 2009 can (or can not) solve the issues discussed on pp. 16-21 of Haendel et. al., 2018. Deadline: noon, September 24
Advance reading (prior to September 17 lecture): Chapters 5 and 6 of Arp, Smith and Spear, 2016
September 17 Basic Formal Ontology
Advance reading (prior to September 24 lecture): SW Smith and Koppel, 2014
September 24: Ontology and Information Engineering in the Healthcare Domain
Class assignment: discuss in a 2-page essay the extent to which the Basic Formal Ontology can assist in dealing with the problems discussed in (Smith & Koppel, 2014). Deadline: noon, October 18
Advance reading (prior to October 1 lecture): Merelli, et al, 2014
October 1: Big Data and How to Overcome the Problems It Causes
- Definition of 'Big Data'
- Overview of machine learning and other approaches to the exploitation of Big Data
- Role of ontology in Data Science
