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The goal of the National Center for Ontological Research is to advance ontological investigation within the United States. NCOR serves as a vehicle to coordinate, to enhance, to publicize, and to seek funding for ontological research activities. It lays a special focus on ontology training and on the establishment of tools and measures for quality assurance of ontologies. NCOR provides ontology services to multiple organizations, including the US Department of Defense.

Events

See here

For past events see here

News

Interview with Barry Smith on Careers in Ontology, September 15, 2022

Interview with Jobst Landgrebe and Barry Smith on 'Why Machines Will Never Rule the World', August 30, 2022.

New book on limits of AI published, August 12, 2022.

Machines ruling the world? Impossible, say researchers

This will totally blow your mind

UB professor’s ontology work recognized in an international standard, April 29, 2022.

Press release on launch of Industrial Ontologies Foundry, February 19, 2021.

Video recording of talk by Barry Smith on "Defining Intelligence", February 17, 2021

Press-release launching the new Industrial Ontologies Foundry, February 19, 2021

NCOR-Brasil established, December 1, 2020

Using Ontology as Powerful Weapon in COVID-19 Fight, July 14, 2020

Leveraging a powerful weapon in the fight against COVID-19 — ontology, June 10, 2020

UB workshop to address human and machine capabilities, April 20, 2018

Working group seeks to extend the depth and functionality of biomedical ontologies, October 14, 2017

Barry Smith wins 2016 IAOA Ontology Competition, August 18, 2016

Doctoral Candidate Invited to Work on United Nations Project, January 4, 2016

Advantages of the Financial Report Ontology in Accounting Research, February 23, 2013

UB Ontologists Win Bioinformatics Integration Award to Support National Institutes of Health

Announcing Clinical and Translational Science Ontology Affinity Group

Information Overload in the Era of Big Data

Botanists building ontologies to cope with information overload

UB Applied Informatics Portal unveiled.

Courses

Barry Smith

Werner Ceusters

Defining 'Ontology'

An ontology is a representation of some part of reality, (e.g. medicine, social reality, physics, etc.). Smith states that: “Ontology is the science of what is, of the kinds and structures of objects, properties, events, processes and relations in every area of reality…Ontology seeks to provide a definitive and exhaustive classification of entities in all spheres of being.” To be an accurate representation of reality an ontology includes the types of entities and events in a given domain (along with their definitions) arranged in a hierarchical structure, along with relations (such as part-of, depends-on, caused-by, etc. where necessary). Ontologies enable the formulation of robust and shareable descriptions of a given domain by providing a common controlled vocabulary for doctrine writers, IT Developers, and war-fighters alike, thereby allowing these disparate communities to communicate with each other. An ontology should be a shared resource between communities, and its continued collaborative development should support the integration of information and facilitate knowledge discovery. These two goals are realized by ensuring wide dissemination of the ontology, so that it will be used by many stakeholders, and its terms will be correspondingly familiar and readily used for search.

Basic Formal Ontology 2.0

Basic Formal Ontology 2020

BFO 2020

Working Group on AI and Complex Systems

This working group has been established to facilitate discussion of the potential and the limits of AI, especially as concerns applications to complex systems in areas such as weather, climate, transport, finance, geothermal and geoseismic systems, as well as in all life sciences. Our work also includes collaborating with systems engineers to develop an ontology of systems under the auspices of the Industrial Ontologies Foundry. Persons interested in participating in meetings of the group should contact Jobst Landgrebe (email: jobstlan@buffalo.edu)

The Philosophome

Philosophome Website

Philosophome Wiki

Semantics of Biodiversity

Paper: Semantics in Support of Biodiversity Knowledge Discovery (PLoS ONE, 2013)

Video Presentations from: Semantics of Biodiversity Workshop (2012)

Ontologies as a method of viewing data

Basic Formal Ontology (BFO)

How to build an ontology with BFO

Tracking referents with Instance Unique Identifiers (IUIs)

Tracking Changes in Our Understanding of Reality: Reality vs. Beliefs

Darwin Core (DwC) and Basic Formal Ontology: Putting it All Together

Building Darwin Core top-down in BFO

Organisms, photographs, media

How to re-use ontologies

Principles of singular nouns, secondary use, understandability

Writing good definitions (DwC Examples)

Management strategies

Ontologies for reuse (BFO, EnvO, IDO, OBI, Plant Ontology , Uberon, IAO)

Educational resources (OBI, Protege, BFO)

Finance and Economics

An Application of Basic Formal Ontology to the Ontology of Services and Commodities, Institute for Business Informatics, University of Koblenz, Germany July 23, 2013

Barry Smith, Reference Data Integration: A Strategy for the Future, Financial Reference Data Management Conference (FIMA), New York, March 2012

The Wernicke Ontology Principle

Wernicke is an ontology-dependent AI system used to automate recurring business processes. Wernicke is based on formal logic developed by Jobst Landgrebe and co-workers at Cognotekt. Its ontologies do not have an Aristotelian taxonomic structure, but are fully axiomatised and logically describe the syntactic structure of recurring language patterns in the Prolog-subset of first order logic. The use of terms in two or more axiomatic definitions of ontological entities creates an implicit network structure within the ontology.

Examples (in German)

1. Implication relations for verbs and verb phrases. (There are hundreds of examples of such formulae in each Wernicke ontology.)

 ((zahlung(Y) AND nachkommen(Z) AND verb(Z,X,Y)) IMPL zahlen(Z))
 ((geld(Y) AND schicken(Z) AND (verb(Z,X) OR verb(Z,X,Y1))) IMPL zahlen(Z))
 ((kosten(Y) AND tragen(Z) AND verb(Z,X,Y)) IMPL zahlen(Z))
 ((überweisungsträger(Y) AND einwerfen(Z) AND verb(Z,X,Y)) IMPL zahlen(Z))
 ((bringen(Z) AND ausgleich(A) AND zum(B) AND mod(B,A,Z) AND verb(Z,X,Y)) IMPL zahlen(Z))
 ((möglich(A) AND mod(A,Z) AND sein(Z) AND (verb(Z,X) OR verb(Z,X,Y))) IMPL möglichsein(Z))  
 ((bitten(Z) AND mod(B,A,Z) AND möglichkeit(A) AND verb(Z,X,Y)) IMPL möglichsein(Z))

2. Temporal structures

 ((übermorgen(W) AND (Y=2)) IMPL zeitabstand(W,in,Y,tagen))
 ((morgen(W) AND (Y=1)) IMPL zeitabstand(W,in,Y,tagen))
 ((heute(W) AND (Y=0)) IMPL zeitabstand(W,in,Y,tagen))
 ((gestern(W) AND (Y=1)) IMPL zeitabstand(W,vor,Y,tagen))
 ((vorgestern(W) AND (Y=2)) IMPL zeitabstand(W,vor,Y,tagen))

3. Domain pattern formulae (ontologic entities)

 past payment a: ((zahlung(X) OR geld(X)) AND rausgehen(Z) AND (I=vergangen) AND verb(Z,X) AND vergangentemp(Z))
 past payment b: ((zahlung(Y) AND tätigen(Z) AND verb(Z,X,Y) AND (I=vergangen) AND vergangentemp(Z))
 past payment c: ((sein(Z) AND (betrag(X) OR forderung(X)) AND zahlen(A) AND mod(A,Z) AND (I=vergangen)
 AND verb(Z,X) AND NOT temp_mod(Z, praet, konj2)