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==News== | ==News== | ||
[https://blog.apaonline.org/2022/09/15/careers-in-ontology-an-interview-with-professor-barry-smith/ Interview with Barry Smith on Careers in Ontology], September 15, 2022 | |||
[https://www.routledge.com/9781032309934 New book on limits of AI published], August 12, 2022. | |||
[https://www.dpaonthenet.net/article/192369/Machines-ruling-the-world--Impossible--say-researchers.aspx Machines ruling the world? Impossible, say researchers] | |||
[https://www.amazon.com/gp/customer-reviews/R35NHUZZQN8226?ref=pf_vv_at_pdctrvw_srp This will totally blow your mind] | |||
[https://www.buffalo.edu/news/releases/2022/04/0290.html UB professor’s ontology work recognized in an international standard], April 29, 2022. | [https://www.buffalo.edu/news/releases/2022/04/0290.html UB professor’s ontology work recognized in an international standard], April 29, 2022. | ||
Revision as of 18:33, 18 September 2022
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
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
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
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
Semantics of Biodiversity
Paper: Semantics in Support of Biodiversity Knowledge Discovery (PLoS ONE, 2013)
Video Presentations from: Semantics of Biodiversity Workshop (2012)
- 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)
Information Ontology
BFO-based data and information ontologies
Military and Intelligence Ontology
JFCOM: Semantic Web and Joint Training (2010)
I2WD: Semantic Enhancement for DSGS-A: Distributed Development of a Shared Semantic Resource (2012-13)
I2WD: PED Fusion via Enterprise Ontology
Common Core Ontologies (preliminary statement)
Ontology for Navy Systems Engineering
Ontology of Planning
Ontology of Engineering
BFO-Based Engineering Ontologies
Bob Young: Towards a Reference Ontology for Manufacturing (2016)
Interoperable Manufacturing Knowledge Systems (2017)
Ontology for Navy Systems Engineering
Materials Ontology
Ontology for Clinical and Translational Science
Clinical and Translational Science Ontology Group
Microbiome Ontology
Ontology
What Biofilms Can Teach Us about Individuality
Designing an Ontology Tool for the Unification of Biofilms Data
Eearth Microbiome Project Ontlogy EMPO
The Human Microbiome
Functional and phylogenetic assembly of microbial communities in the human microbiome
The human microbiome, including as appendix: A microbiome glossary
Parts and Wholes: The Human Microbiome, Ecological Ontology, and the Challenges of Community
Microbiomes and the external environment
A communal catalogue reveals Earth’s multiscale microbial diversity
MetaSUB: Metagenomics and Metadesign of Subways & Urban Biome
Tracking human sewage microbiome in a municipal wastewater treatment plant
Varia
Collective bio-molecular processes: The hidden ontology of systems biology
A review of methods and databases for metagenomic classification and assembly
Suggested Reading
Coordinated Evolution of Biomedical Ontologies
Avoiding Perspective-Relative Silos
Training Videos
Ontology for Intelligence, Defense and Security
A Repeatable Process for Ontology Development
Avoiding Semantic Stovepipes: Five Ontological Principles for Interoperability
