Ontolog Forum - User contributions [en]

AngelHernandezRivera

2026-07-12T22:56:53Z

KennethBaclawski: Created page with "= Angel Hernández Rivera = I am an independent researcher working on Angelusarity, a framework that investigates how structured conversation can be used to discover, refine, and organize conceptual knowledge into coherent ontological structures. My goal is to discuss theoretical assumptions, identify limitations, and receive technical feedback from researchers and practitioners in formal ontology, ontology engineering, and knowledge representation."

= Angel Hernández Rivera =

I am an independent researcher working on Angelusarity, a framework that investigates how structured conversation can be used to discover, refine, and organize conceptual knowledge into coherent ontological structures. My goal is to discuss theoretical assumptions, identify limitations, and receive technical feedback from researchers and practitioners in formal ontology, ontology engineering, and knowledge representation.

MattiasLjung

2026-07-10T15:31:13Z

KennethBaclawski: Created page with "= Mattias Ljung = I am a business/information architect based in Sweden working for a nordic bank (https://sebgroup.com/) Through our membership to EDM Council I came in contact with the FIBO ontology 8 years ago and for about 4 years now I'm leading a practice within the bank to promote and build out a business ontology using WC3 standards. As champion in this field within my organization I spend quite a lot of effort on learning and following emergent trends and bes..."

= Mattias Ljung =

I am a business/information architect based in Sweden working for a nordic bank (https://sebgroup.com/)

Through our membership to EDM Council I came in contact with the FIBO ontology 8 years ago and for about 4 years now I'm leading a practice within the bank to promote and build out a business ontology using WC3 standards.

As champion in this field within my organization I spend quite a lot of effort on learning and following emergent trends and best-practices to contribute getting the organization "AI ready".

Over time this area has also become a quite deep personal interest, especially on academia within the area. Prefer Barry Smith over Palantir.

Don't consider myself an ontologist due to lack of experience but definately aspiring.

Here my LinkedIn:
https://www.linkedin.com/in/mattias-ljung-69baaa3?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=ios_app

ConferenceCall 2026 05 13

2026-07-09T03:55:04Z

KennethBaclawski:

{| class="wikitable" style="float:right; margin-left: 10px;" border="1" cellpadding="10"
|-
! scope="row" | Session
| [[session::Interoperability]]
|-
! scope="row" | Duration
| [[duration::1 hour]]
|-
! scope="row" rowspan="3" | Date/Time
| [[has date::13 May 2026 16:00 GMT]]
|-
| 9:00am PDT/12:00pm EDT
|-
| 5:00pm BST/6:00pm CST
|-
! scope="row" | Convener
| [[convener::ToddSchneider|Todd Schneider]]
|}

= [[OntologySummit2026|Ontology Summit 2026]] {{#show:{{PAGENAME}}|?session}} =
* '''Jim Wilson''' OAGi President & CEO ''Interoperability Fundamentals in the Age of AI''
** [https://ontologforum.s3.us-east-1.amazonaws.com/OntologySummit2026/Interoperability/Interoperability-Fundamentals-in-the-Age-of-AI--JimWilson_20260513.pdf Slides]
** [https://ontologforum.s3.us-east-1.amazonaws.com/OntologySummit2026/Interoperability/Interoperability-Fundamentals-in-the-Age-of-AI--JimWilson_20260513.mp4 Video Recording]
** [https://youtu.be/MFz14PZ5ayU YouTube Video]

== Conference Call Information ==
* Date: '''Wednesday, 13 May 2026'''
* Start Time: 9:00am PDT / 12:00pm EDT / 6:00pm CEST / 5:00pm BST / 1600 UTC
** ref: [http://www.timeanddate.com/worldclock/fixedtime.html?month=5&day=13&year=2026&hour=12&min=00&sec=0&p1=179 World Clock]
* Expected Call Duration: 1 hour
{{:OntologySummit2026/ConferenceCallInformation}}

== Discussion ==
12:06:17 deddy : What is relationship between OAGi & OMG… another standards body?

12:09:46 deddy : Fabulous AG entertainment on YouTube
* Laura Farms
* Larson Farms
* Millennial Farmer

They're into the fields for planting.

Incredibly complex computer driven machines.

12:13:40 deddy : Good luck eliminating cash outflows when combines START at $500,000+

12:20:45 TS : Can AI be used in creating or revising standards in the context in the value they may engender?

12:23:52 Ram Sriram : Recent report on Standards and Innovation: https://www.ansi.org/standards-news/all-news/5-11-26-standards-are-a-meaningful-driver-of-innovation

12:24:54 Ram Sriram : Paper written more than two decades ago on Standards and Innovation: https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=821473

12:25:53 deddy : "Philadelphia’s Philosopher Mechanics: A History of the Franklin Institute 1824 - 1865" by Bruce Sinclair

Three efforts:
# Water power
# Steamship boiler explosions
# Weights & measured standards

12:27:58 Ram Sriram : You can contact Jacob Collard at NIST for publications related to this: https://www.nist.gov/programs-projects/linguistic-and-ai-support-collective-standards-development

12:28:03 Michael DeBellis : This is not what I was expecting in a talk about interoperability. When I think about Interoperability, the last thing I think of is Porter. I see Interoperability as broadly two dimensions:
# Communication interoperability: message and event busses like RabbitMQ, Kafka, connectors that resolve issues between calling an API in Java vs. one in Python. Integrating two systems where both use knowledge graphs but one uses Neo4J and another uses OWL/RDF Standards like MCP.
# Semantic Interoperability. Resolving the fact that within the same company several groups have concepts like Customer but Customer mean different things for Marketing vs. Fulfillment, vs. Catalog and Shopping Cart teams. IT in the past usually just does this ad hoc. I worked with HL7 for a while and at the time, trying to define a common object model was greeted with wooden stakes and garlic but decades later they do it with HL7 FHIR.
* deddy : Folks at the top will need to allocate resources. Top level trained on Porter.
** Michael DeBellis : Assuming that is true, and that Porter is more than just standard MBA business babble, you could say the same thing about any problem: building better reasoners, supporting Big Data, etc. it all has to be justified at a business level so not sure why it matters more for Interoperability.
* Ram Sriram : What is the current State of Art on using FHIR for Semantic Interoperability?
** Michael DeBellis : Don't know. I think it has made some surprisingly good traction but haven't been working with it.

12:28:38 TS : How can we discern if AI responses have incorporated standards?

12:30:53 John Sowa : When the technology is developing so fast, it is too soon to propose and enforce any standards.

12:31:52 John Sowa : So far, he hasn't told us what his technology is and what does it do.
* Michael DeBellis : 👍
* Terry : 👍

12:34:50 Michael DeBellis : No on "Ontologies need to be Perfect". The best way to derail a project is to assume you need to get it right and perfect the first time. That's why we don't use the Waterfall model. Because you just aren't going to get it right the first time and the best way you know how you are wrong is to deploy something and get feedback from the users. And in spite of what some people say: change is constant in business so even if you get the ontology perfect, it's going to change so you need an Agile process.
* deddy : You assume there’s no upward error correction portion of SDLC?
* Gary Berg-Cross : To enable interoperability you might have to uncover hidden assumptions, clarify value trade-offs, and contextual constraints.

12:38:04 TS : The SCOPE Model for Interoperability Assessment can help is understanding and distinguishing context.

12:38:47 Gary Berg-Cross : I found Guizzardi, Giancarlo. "Ontology, ontologies and the “I” of FAIR." Data Intelligence 2.1-2 (2020): 181-191. useful for interoperability in a FAIR/ontology context. Giancarlo would have been a good speaker to recruit.

12:39:53 deddy : I’m holding my breath on when the drawing tools standardize on arrowhead meaning(s). < sigh >
* janet singer : 😀

12:40:44 janet singer : This approach provides the broader context for thinking through the relationships between IT standards, interoperability and ontologies.

12:40:48 Mike Bennett : In all OMG modeling standards, each line and line ending has a defined meaning. Unlike the more casual drawing tools. Model semantics is the key.

12:41:19 deddy : OMG embraces UML…?
* Mike Bennett : OMG wrote UML
* deddy : Ah, ha! So it has to be a standard. < sigh >
* deddy : My involvement with OMG pre-dates UML… so I missed it.

I’ve been exposed to, but never worked in a UML environment.

12:42:49 Mike Bennett : The advantage of a standard is precisely that you know what the different arrowheads mean because they have standard meanings.

12:45:17 janet singer : ‘Model semantics’ is a proposed standard to be used (de jure, de facto).

12:45:59 deddy : Does Archimate use UML?

12:50:54 janet singer : UML was created by bringing together OOAD approaches by Booch, Rumbaugh and Jacobson then adopted by OMG
* deddy : thx for genealogy

12:51:37 John Sowa : logic is fundamental.

12:52:06 John Sowa : UML and every other kind of diagram can be specified in logic.
* David Eddy : But systems are built on political power… with some logic on the side.
* TS : John, communication is fundamental; Logic is an aid.
** John Sowa : Then it Is possible to translate that logic to anything in any notation -- including any and every natural language.
* David Eddy : .. and as always ignoring the existence / importance of unnatural language? The language used inside the operational systems

12:52:50 Terry : Chart 81 - Continuum doesn't address (un) predictability

12:54:48 Gary Berg-Cross : It is better to say that ontologies provide the semantics for metadata and thus understanding data.
* David Eddy : You can say it.. doesn’t make it accurate or useful.

12:54:58 janet singer : Wirfs-Brock’s role-responsibility approach should have been incorporated as well, but it was challenging to incorporate (as I recall)

12:55:48 Gary Berg-Cross : Reference ontologies still matter.
* David Eddy : How long does it take me to wrap an ontology around the regulatory report my summer intern is assigned to?

12:57:23 Gary Berg-Cross : Environment Ontology (ENVO)is relevant for Ag with biomes, environmental features, and habitat descriptors. But there are some more specific.

12:58:12 Gary Berg-Cross : Candidate ontologies and vocabularies include: AGROVOC
* Scope: multilingual controlled vocabulary for agriculture, food, fisheries, forestry, environment.
* Use: indexing, metadata, semantic search; widely used by FAO and research repositories.

Crop Ontology (CO)
* Scope: standardized trait, phenotype, and trial descriptors for many crops (maize, rice, wheat, cassava, etc.).
* Use: phenotype data annotation, breeding databases, germplasm characterization.

Plant Ontology (PO)
* Scope: plant structures and growth/developmental stages across species.
* Use: linking phenotypic data, gene expression, and anatomical terms.

12:59:31 Gary Berg-Cross : For agronomic practices, experimental designs, management activities. we have Agronomy Ontology (AgrO). But harmonizing all of these is a big issue especially for interop.

13:00:31 David Eddy : Ahhhhh… “boundaries”… an ambiguous topic

13:01:14 Gary Berg-Cross : BTW, the AgroPortal / AGROVOC has a linked KG
* Focus: semantic integration of agricultural vocabularies (AGROVOC + others).
* Uses: metadata enrichment, term linking, powering semantic search across datasets.

13:03:00 Gary Berg-Cross : Wageningen University / WUR was mentioned. They seem to have some knowledge graphs that may be relevant (e.g., AgroLD). IS OAGI using or supporting things like this?

13:04:12 Gary Berg-Cross : FoodKG / FoodOn also provides some integrations of food products, supply chain, safety records linked to provenance and taxonomy.

13:06:18 Gary Berg-Cross : To ensure interoperability effective KGs for AG would need to combine resources like Crop Ontology, Plant Ontology, ENVO, AGROVOC, SAREF4AGRI, MIAPPE mappings .

13:07:34 David Eddy : A Minnesota family farm… starting planting: Larson Farms https://www.youtube.com/watch?v=tqYJkQZ_B_w

13:08:24 John Sowa : The methods for mapping a dialog in English to and from Common Logic are precise. Most importantly they exist and work -- successfully.
* David Eddy : Does this Common Logic work to map from operational systems, written in decidedly unnatural languages & meanings back into natural language?
** janet singer : Seeing languages as standardized codes is how one can see that ‘unnatural’ language and natural language are more alike than different, so their issues need to be addressed together

13:15:47 John Sowa : I am NOT suggesting that anybody learn or use Common Logic. I am saying that they can communicate precisely in their own native language.
* David Eddy : I’m searching for ability to map “back” from operational systems to whatever…

13:18:11 David Eddy : Duly note there is no Wikipedia page for IBM’S AD/CYCLE metadata effort in late ‘80s.
* Gary Berg-Cross : David, I hope that we have advanced from the 1980's on these issues.

13:19:49 janet singer : If all languages (human, machine, etc.) were appreciated as standardized codes for the agents in various communities, then common logic could be seen in the Rosetta Stone role

== Resources ==
* [https://ontologforum.s3.us-east-1.amazonaws.com/OntologySummit2026/Interoperability/Interoperability-Fundamentals-in-the-Age-of-AI--JimWilson_20260513.pdf Slides]
* [https://ontologforum.s3.us-east-1.amazonaws.com/OntologySummit2026/Interoperability/Interoperability-Fundamentals-in-the-Age-of-AI--JimWilson_20260513.mp4 Video Recording]
* [https://youtu.be/MFz14PZ5ayU YouTube Video]

== Previous Meetings ==
{{#ask: [[Category:OntologySummit2026]] [[Category:Icom_conf_Conference]] [[<<ConferenceCall_2026_05_12]]
|?|?Session|mainlabel=-|order=desc|limit=3}}

== Next Meetings ==
{{#ask: [[Category:OntologySummit2026]] [[Category:Icom_conf_Conference]] [[>>ConferenceCall_2026_05_14]]
|?|?Session|mainlabel=-|order=asc|limit=3}}

[[Category:Icom_conf_Conference]]
[[Category:Occurrence| ]]
[[Category:OntologySummit2026| ]]

OntologySummit2026

2026-07-09T03:52:42Z

KennethBaclawski:

= Ontology Summit 2026 =
{{:OntologySummit2026/Theme}}

The [[OntologySummit|Ontology Summit]] is an annual series of events that involves the ontology community and communities related to each year's theme chosen for the summit. The Ontology Summit was started by Ontolog and NIST, and the program has been co-organized by Ontolog and NIST along with the co-sponsorship of other organizations that are supportive of the Summit goals and objectives.

As part of Ontolog’s general advocacy to bring ontology science and related engineering into the mainstream, we endeavor to facilitate discussion and knowledge sharing amongst stakeholders and interested parties relevant to the use of ontologies. The results will be synthesized and summarized in the form of the Ontology Summit 2026 Communiqué, with expanded supporting material provided on the web and in journal articles.

= Process and Deliverables =
The [[OntologySummit2026|Ontology Summit 2026]] will consist of virtual discourse (over our archived mailing lists), virtual presentations and panel sessions as part of recorded video conference calls.
As in prior years the intent is to provide some synthesis of ideas and draft a communique summarizing major points.

Meetings are at Noon US/Canada Eastern Time on Wednesdays and last about an hour.

== Schedule ==
=== Launch ===
* [[ConferenceCall_2026_02_18|18 February 2026]]
=== Retrospective ===
* [[ConferenceCall_2026_02_25|25 February 2026]]
** '''[[KenBaclawski|Ken Baclawski]]''' (chair)
** '''[[JanetSinger|Janet Singer]]'''
** '''[[MichaelGruninger|Michael Grüninger]]'''
** '''[[SteveRay|Steve Ray]]'''
=== Ontologies and AI ===
* [[ConferenceCall_2026_03_04|4 March 2026]] '''[[GaryBergCross|Gary Berg-Cross]]''' and '''[[RamSriram|Ram D. Sriram]]'''
* [[ConferenceCall_2026_03_11|11 March 2026]] '''Randy Goebel''' ''A (partial) framework for debugging foundation models''
* [[ConferenceCall_2026_03_18|18 March 2026]] '''[[PascalHitzler|Pascal Hitzler]]''' ''Some Advances in Neurosymbolic AI Related to Ontologies and Knowledge Graphs''
* [[ConferenceCall_2026_03_25|25 March 2026]]
** '''Manas Gaur''' ''Neurosymbolic AI for High Stakes Applications in the LLM Era''
** '''Luis Lamb''' ''Neurosymbolic AI: From research to industry''
=== Foundations and Tools ===
* [[ConferenceCall_2026_04_01|1 April 2026]]
** '''[[KenBaclawski|Ken Baclawski]]''' ''Introduction to Foundations''
** '''Devika P. Madalli''' ''Faceted Classification as a Semantic Framework''
* [[ConferenceCall_2026_04_08|8 April 2026]] '''[[MarkMusen|Mark Musen]]''' ''Reports of the Death of Ontologies Are Greatly Exaggerated''
* [[ConferenceCall_2026_04_15|15 April 2026]] '''[[NicolaGuarino|Nicola Guarino]]''' ''Ontological Analysis as a Search for Truthmakers''
* [[ConferenceCall_2026_04_22|22 April 2026]] Synthesis Panel
=== Interoperability ===
* [[ConferenceCall_2026_04_29|29 April 2026]] '''[[WillSobel|Will Sobel]]''' ''The Evolution of an Industrial Standard: From Paper to Ontology''
* [[ConferenceCall_2026_05_06|6 May 2026]] '''[[MikeBennett|Mike Bennett]]''' ''The FIBO Journey: An Ontology for Financial Interoperability''
* [[ConferenceCall_2026_05_13|13 May 2026]] '''Jim Wilson''' OAGi President & CEO ''Interoperability Fundamentals in the Age of AI''
=== Education, Training and Certification ===
* [[ConferenceCall_2026_05_20|20 May 2026]] '''[[JohnBeverley|John Beverley]]''' ''Ontology Education at the University at Buffalo''
* [[ConferenceCall_2026_06_10|10 June 2026]] '''Bill Mandrick''' ''Ontology Engineering 101 - From Expert Knowledge to Ontological Models''
=== Cognition ===
* [[ConferenceCall_2026_06_03|3 June 2026]]
** '''[[JohnSowa|John Sowa]]''' and '''[[ArunMajumdar|Arun Majumdar]]''' ''Reasoning Beats Pattern Matching''
=== Synthesis ===
* [[ConferenceCall_2026_06_17|17 June 2026]] Synthesis Panel
* [[ConferenceCall_2026_06_24|24 June 2026]] Symposium

== Resources ==
* [[OntologySummit2026/ConferenceCallInformation|Conference Call Information]]
* [http://bit.ly/34DOmRV Ontology Summit YouTube Channel]

[[Category:OntologySummit]]
[[Category:OntologySummit2026]]

AmitWohl

2026-06-24T20:29:00Z

KennethBaclawski: Created page with "= Amit Wohl = Professional Interests Developing Neurosymbolic AI for compliance management Work Experience [currently] VP Product at AEYE Health (fully autonomous AI for diagnosing disease from retinal images) [previously] held various product management, marketing and business development positions in cybersecurity companies Education * M.Sc. MIT School of Engineering * MBA MIT Sloan School of Management * B.Sc. Technion - Israel Institute of Technology"

= Amit Wohl =

Professional Interests 
Developing Neurosymbolic AI for compliance management

Work Experience 
[currently] VP Product at AEYE Health (fully autonomous AI for diagnosing disease from retinal images) 
[previously] held various product management, marketing and business development positions in cybersecurity companies

Education
* M.Sc. MIT School of Engineering
* MBA MIT Sloan School of Management
* B.Sc. Technion - Israel Institute of Technology

ConferenceCall 2026 06 24

2026-06-24T03:18:49Z

KennethBaclawski:

{| class="wikitable" style="float:right; margin-left: 10px;" border="1" cellpadding="10"
|-
! scope="row" | Session
| [[session::Symposium]]
|-
! scope="row" | Duration
| [[duration::1 hour]]
|-
! scope="row" rowspan="3" | Date/Time
| [[has date::24 Jun 2026 16:00 GMT]]
|-
| 9:00am PDT/12:00pm EDT
|-
| 5:00pm BST/6:00pm CST
|-
! scope="row" | Convener
| [[convener::KenBaclawski|Ken Baclawski]]
|}

= [[OntologySummit2026|Ontology Summit 2026]] {{#show:{{PAGENAME}}|?session}} =
* At this session we will begin the process of developing a Communiqué to synthesize and summarize the Ontology Summit 2026.
* [[KenBaclawski|Ken Baclawski]] will present a draft overview document and a set of Google folders for contributions and discussions by volunteers.
* [[GaryBergCross|Gary Berg-Cross]] will present summaries in the Google folders.
* General discussion and assignments for volunteers

== Conference Call Information ==
* Date: '''Wednesday, 24 June 2026'''
* Start Time: 9:00am PDT / 12:00pm EDT / 6:00pm CEST / 5:00pm BST / 1600 UTC
** ref: [http://www.timeanddate.com/worldclock/fixedtime.html?month=6&day=24&year=2026&hour=12&min=00&sec=0&p1=179 World Clock]
* Expected Call Duration: 1 hour
{{:OntologySummit2026/ConferenceCallInformation}}

== Discussion ==

== Resources ==

== Previous Meetings ==
{{#ask: [[Category:OntologySummit2026]] [[Category:Icom_conf_Conference]] [[<<ConferenceCall_2026_06_23]]
|?|?Session|mainlabel=-|order=desc|limit=3}}

== Next Meetings ==
{{#ask: [[Category:OntologySummit2026]] [[Category:Icom_conf_Conference]] [[>>ConferenceCall_2026_06_25]]
|?|?Session|mainlabel=-|order=asc|limit=3}}

[[Category:Icom_conf_Conference]]
[[Category:Occurrence| ]]
[[Category:OntologySummit2026| ]]

ConferenceCall 2026 06 24

2026-06-24T03:17:23Z

KennethBaclawski:

{| class="wikitable" style="float:right; margin-left: 10px;" border="1" cellpadding="10"
|-
! scope="row" | Session
| [[session::Symposium]]
|-
! scope="row" | Duration
| [[duration::1 hour]]
|-
! scope="row" rowspan="3" | Date/Time
| [[has date::24 Jun 2026 16:00 GMT]]
|-
| 9:00am PDT/12:00pm EDT
|-
| 5:00pm BST/6:00pm CST
|-
! scope="row" | Convener
| [[convener::KenBaclawski|Ken Baclawski]]
|}

= [[OntologySummit2026|Ontology Summit 2026]] {{#show:{{PAGENAME}}|?session}} =
* At this session we will begin the process of developing a Communiqué to summarize
synthesize and summarize the Ontology Summit 2026.
* [[KenBaclawski|Ken Baclawski]] will present a draft overview document and a set of Google folders for contributions and discussions by volunteers.
* [[GaryBergCross|Gary Berg-Cross]] will present summaries in the Google folders.
* General discussion and assignments for volunteers

== Conference Call Information ==
* Date: '''Wednesday, 24 June 2026'''
* Start Time: 9:00am PDT / 12:00pm EDT / 6:00pm CEST / 5:00pm BST / 1600 UTC
** ref: [http://www.timeanddate.com/worldclock/fixedtime.html?month=6&day=24&year=2026&hour=12&min=00&sec=0&p1=179 World Clock]
* Expected Call Duration: 1 hour
{{:OntologySummit2026/ConferenceCallInformation}}

== Discussion ==

== Resources ==

== Previous Meetings ==
{{#ask: [[Category:OntologySummit2026]] [[Category:Icom_conf_Conference]] [[<<ConferenceCall_2026_06_23]]
|?|?Session|mainlabel=-|order=desc|limit=3}}

== Next Meetings ==
{{#ask: [[Category:OntologySummit2026]] [[Category:Icom_conf_Conference]] [[>>ConferenceCall_2026_06_25]]
|?|?Session|mainlabel=-|order=asc|limit=3}}

[[Category:Icom_conf_Conference]]
[[Category:Occurrence| ]]
[[Category:OntologySummit2026| ]]

Ns/

2026-06-21T01:16:30Z

KennethBaclawski: Redirected page to NidhalSaïdani

#REDIRECT [[NidhalSaïdani]]

Ns/

2026-06-21T01:13:15Z

KennethBaclawski: Created page with "#REDIRECT [https://nidh.al/saidani/]"

#REDIRECT [https://nidh.al/saidani/]

NidhalSaïdani

2026-06-21T01:09:38Z

KennethBaclawski: Created page with "= Nidhal Saïdani = A few words about myself: I am an AI engineer based in France, with a Master’s degree in Intelligent Systems Engineering (AI & Robotics track) from Sorbonne Université. My work sits at the intersection of semantic web technologies and machine learning. I design and build knowledge graph systems (RDF/OWL/SKOS, SPARQL, SHACL) using Altair (ex-Anzo) Graph Studio and LLM pipelines, and my current focus is on combining the two. My interest in ontology..."

= Nidhal Saïdani =

A few words about myself: I am an AI engineer based in France, with a Master’s degree in Intelligent Systems Engineering (AI & Robotics track) from Sorbonne Université. My work sits at the intersection of semantic web technologies and machine learning. I design and build knowledge graph systems (RDF/OWL/SKOS, SPARQL, SHACL) using Altair (ex-Anzo) Graph Studio and LLM pipelines, and my current focus is on combining the two.

My interest in ontology comes from exactly this convergence. I work on knowledge-graph-based retrieval, in particular GraphRAG, where a well-structured ontology and a curated knowledge graph give large language models grounded, traceable context rather than relying on vector retrieval alone. I am keen to follow the Forum's discussions on ontology design and modelling practices, and to learn how the community approaches applying these foundations to modern AI systems.

You are very welcome to add my website: https://nidh.al/saidani/ (currently under construction). If possible, I would be glad to have my namesake page link out to it (e.g. https://ontologforum.com/index.php/ns/).

ConferenceCall 2026 06 10

2026-06-10T22:22:21Z

KennethBaclawski:

{| class="wikitable" style="float:right; margin-left: 10px;" border="1" cellpadding="10"
|-
! scope="row" | Session
| [[session::Education]]
|-
! scope="row" | Duration
| [[duration::1 hour]]
|-
! scope="row" rowspan="3" | Date/Time
| [[has date::10 June 2026 16:00 GMT]]
|-
| 9:00am PDT/12:00pm EDT
|-
| 5:00pm BST/6:00pm CST
|-
! scope="row" | Convener
| [[convener::KenBaclawski|Ken Baclawski]]
|}

= [[OntologySummit2026|Ontology Summit 2026]] {{#show:{{PAGENAME}}|?session}} =
* '''Bill Mandrick''' ''Ontology Engineering 101 - From Expert Knowledge to Ontological Models''
** [https://ontologforum.s3.us-east-1.amazonaws.com/OntologySummit2026/Education/Ontology-Engineering-101--BillMandrick_20260610.mp4 Video Recording]
** [https://youtu.be/MbgJqH3-IMQ YouTube Video]
** Abstract: Ontology engineering is a technical field, but at its core it begins with a familiar philosophical task of making distinctions clear. Organizations routinely depend on data whose meaning is only partly understood. Ontology engineering provides a disciplined method for moving from informal expert language to reusable, inspectable, machine-checkable representations. In this talk, Bill Mandrick will present an overview of the weekly Ontology 101 series, 6-week cycles focused on developing foundational ontology skills with hands-on practice. The first two weeks involve participants learning how to work with subject matter experts without trying to turn them into ontologists, the goal being to elicit and refine competency questions: clear, testable questions that the ontology should help answer. In the second two weeks, those questions are translated into visual design patterns that expose the relevant entities, relations, roles, processes, and constraints. In the last pair of weeks, the patterns are implemented in OWL using tools such as Protégé, tested with reasoners, and evaluated against the original competency questions. Throughout the aim is not to master ontology engineering in a single session, but to understand the basic rhythm of the work, engage experts, clarify meaning, model the structure, encode the result, test it, and revise.
** Bio: Bill Mandrick, Ph.D. is a senior ontologist at CUBRC and retired U.S. Army Colonel whose work has focused on ontology development, OWL/RDF representation, Basic Formal Ontology compliance, and military/intelligence applications of ontology. Dr. Mandrick is a long-time contributor to early military ontology work and to NCOR/CUBRC best-practices work in ontology development. He also co-authored work with Barry Smith on the philosophical foundations of intelligence collection and analysis, including the role of BFO and the Common Core Ontologies in semantic interoperability for intelligence systems. Dr. Mandrick is the chair of the rather successful "Ontology 101” weekly working group, sponsored by NCOR.

== Conference Call Information ==
* Date: '''Wednesday, 10 June 2026'''
* Start Time: 9:00am PDT / 12:00pm EDT / 6:00pm CEST / 5:00pm BST / 1600 UTC
** ref: [http://www.timeanddate.com/worldclock/fixedtime.html?month=6&day=10&year=2026&hour=12&min=00&sec=0&p1=179 World Clock]
* Expected Call Duration: 1 hour
{{:OntologySummit2026/ConferenceCallInformation}}

== Discussion ==
10:15:52 Mike Bennett: Why are regulated products a subclass? Being regulated is not an inherent property of a product.

10:23:13 Victor.Bagwell: I'm a data guy, so to speak. So I view this as analogous to relational database normalization to 3rd normal down to dimension tables where the normalization passes are repeated until each hierarchy goes down to mutual exclusive components. Albeit, going a bit beyond how to store/access the data to ask what exists in reality and "how" they are related. Essentially a semantic normalization.

10:24:00 Paul Tyson: Session idea: unbiased head-to-head comparison of CL and OWL with working examples and applications. Does anyone know of such a resource already available?

10:26:59 Bill Mandrick: william.mandrick@cubrc.org

10:27:19 Gary Berg-Cross: I find that you can use, as a starting point an AGI agent to find definitions for things not in an Ontology. For example five folate-related chemicals. Here are their 2 definitions:
# Pteroylmonoglutamic Acid The synthetic form of vitamin B9 (folic acid), representing the first molecule in an enzymatic process that results in the bioactive form of folate. It has high bioavailability and is the only folate form authorized in fortified foods and drugs. Rootine
# 5-Methyltetrahydrofolate (5-MTHF) The main food folate and principal form of vitamin B9 found in plasma. It is the end product of folate metabolism and is involved in the remethylation of homocysteine to methionine, a critical step in methionine and DNA synthesis. ScienceDirectDoveMed
# Tetrahydrofolate (THF) A derivative of vitamin B9 and a coenzyme for metabolic reactions involving amino acid and nucleic acid formations. It participates in important single-carbon transfer reactions — often referred to as one-carbon metabolism — and in synthesizing several amino acids such as serine and methionine, purines, and thymine. Chemically, it consists of three structural components: para-aminobenzoic acid (PABA), a bicyclic pteridine ring, and glutamic acid. NCBI
# L-Methylfolate (5-MTHF) The bioactive, naturally occurring form of folate. It is the metabolic end point of the folate cycle, produced when the MTHFR enzyme converts 5,10-methylenetetrahydrofolate into 5-methyltetrahydrofolate. It is the only form of folate that can cross the blood-brain barrier. MDPI

10:29:17 Gary Berg-Cross: The Defs can then be axiomatized
* Object Properties — 9 semantic relations including has_metabolic_precursor, donates_group, converted_by_enzyme, is_active_form_of, and crosses_barrier, all with inverse declarations where applicable.
* Data Properties — 9 annotation/data properties covering molecular formula, molar mass, CAS number, ChEBI/PubChem IDs, IUPAC name, and boolean flags like is_synthetic and crosses_blood_brain_barrier.
* Class Hierarchy: ChemicalEntity → VitaminCompound → FolateCompound → CoenzymeFolate / SyntheticFolate
** Each of the five compounds as a named class with full OWL restriction axioms
* Key Axioms per compound:
** PteroylmonoglutamicAcid — synthetic, fully oxidised, is_metabolic_precursor_of THF, converted by DHFR
** Tetrahydrofolate — active form, OneCarbon_Carrier, precursor to both 5-MTHF and 10-CHO-THF
** FiveMethyltetrahydrofolate — MethylGroupDonor, crosses BBB, participates in homocysteine remethylation, converted by MTHFR
** LMethylfolate — declared owl:equivalentClass to 5-MTHF (same entity, different clinical name)
** TenFormyltetrahydrofolate — FormylGroupDonor at N10, drives purine synthesis
*General Class Axioms (GCIs) — closure axioms enforcing that only 5-MTHF crosses the BBB within this compound set, and that N10-formyl donation entails membership in TenFormyltetrahydrofolate.

10:30:32 Gary Berg-Cross: Here's a start on an ontology for your diagram. Here's the full ontology, directly loadable in Protégé or any OWL 2 DL reasoner (HermiT, Pellet, ELK). Here's a breakdown of what was axiomatized from the diagram:
* Upper Ontology (BFO-aligned, left spine of the graph) The full BFO chain is reproduced: Entity → Continuant → IndependentContinuant → MaterialEntity → Object → PortionOfProcessedMaterial, with BFO IRIs annotated on each class. The right spine follows Continuant → GenericallDependentContinuant → InformationContentEntity → DirectiveInformationContentEntity.
* Object Properties (11 named relations) All relations visible in the diagram are formally declared — has_ingredient, prescribed_by / prescribes, complies_with, governed_by / governs, specifies / specified_by, has_part / part_of (BFO-aligned), and concretizes / is_concretized_by — with domain/range documentation and inverses where applicable.
* Domain Class Hierarchy (two branches)
** Material branch: PortionOfProcessedMaterial → RegulatedProduct → SupplementProduct → VitaminSupplementProduct (with VitaminIngredient as a sibling)
** Directive branch: PerformanceSpecification → IntendedUseStatement → SupplementUseStatement; QualitySpecification → IngredientSpecification; ProcessRegulation → RegulatoryFramework → SupplementRegulatoryFramework → UruguaySupplementRegulatoryFramework
* Instance Layer — all four pink-bordered individuals from the diagram are instantiated with their exact named relations: VitaminSupplementProduct001 has_ingredient VitaminIngredient001, IngredientSpecification001 prescribes SupplementUseStatement001, and the Uruguay framework instance governs the product and prescribes the spec.
* Disjointness, GCIs,....
* Property Chains enforce closure: e.g., only 5-MTHF crosses the BBB (from the folate ontology pattern), anything governed by a Uruguay framework is a SupplementProduct, and a has_ingredient o specified_by chain propagates prescribed_by transitively.
* The diagram faithfully reproduces the ontograph structure. to read it:
** Color encoding gray boxes are BFO upper ontology (entity, continuant, independent/generically dependent continuant); blue is the material branch (material entity → object); teal is the information branch (ICE → Directive ICE and its three direct subclasses); purple is the domain layer (all the supplement-specific classes); amber with a heavier border marks the 5 named instances at the bottom.
** Left spine follows the BFO material chain down to Portion of Processed Material, which splits into Regulated Product (leading to Supplement Product → Vitamin Supplement Product) & Vitamin Ingredient side by side.
* Right spine descends from Generically Dependent Continuant → ICE → Directive ICE, which fans into three parallel columns: Performance Specification → Intended Use Statement → Supplement Use Statement; Quality Specification → Ingredient Specification; and Process Regulation → Regulatory Framework → Supplement Regulatory Framework → Uruguay Supplement Regulatory Framework.
* Instance layer (below the dashed separator) shows all five individuals with their named object-property relations rendered as colored dashed arrows: has ingredient, specified by, prescribed by, governed by, complies with, prescribes, and documents. Every node is clickable for deeper explanation.

10:28:12 Michael DeBellis: Since you're using Web Protege you couldn't define any axioms on classes. Do you have a process where you do further elaboration on the model and add axioms?

10:37:01 Paul A. Pope: Could non-structural characterizations, like "regulated", be placed in the Annotations attached to a class? Perhaps in a Description annotation? "Description: Regulated" or "Regulated: Yes" (BTW, I don't have mic capability)

10:40:45 Michael DeBellis: When modeling a domain, IMO you seldom use the natural language definition. That's far too expansive. In a general NLP ontology speed and velocity are synonyms. In a physics ontology velocity is a vector and speed is a scalar. The same is true for classes like product, purchase order, etc.

10:42:19 TS: In natural language processing the corpus of material processed provides the context for interpretation.

10:50:48 Victor.Bagwell: reponse to Michael/TS...just for clarity -- My comments were related to a foundational corpus. I've been involved in NLP for sometime and understand the specific effects of domains (specific to). Rather, I was thinking about public common use case at the top (e.g., websters) and what the prob. is of semantics for a specific word. Then a hierarchy that includes are is split (perhaps by domain), and adjusted for the hierarchical levels by the domain, sub-domain, sub-sub-domain, n as a cross section and then over time and perhaps even by other compnents (e.g., language, education culture, geography, to name a few).

Ultimately -- highly dimensional

I was thinking about how to operational information in real context

10:52:04 Paul A. Pope: (circling back here) Could non-structural characterizations, like "regulated", be placed in the Annotations attached to a class? Perhaps in a Description annotation? "Description: Regulated" or "Regulated: Yes"
* Thank you for addressing my question. Great discussion. Looking forward to subsequent meetings.

11:01:37 Gary Berg-Cross: We have to define what subsequent meetings we will have.

11:11:34 Victor.Bagwell: Thank you! Always learning from all of you.
* Marcia Zeng: 👍

== Resources ==
* [https://ontologforum.s3.us-east-1.amazonaws.com/OntologySummit2026/Education/Ontology-Engineering-101--BillMandrick_20260610.mp4 Video Recording]
* [https://youtu.be/MbgJqH3-IMQ YouTube Video]

== Previous Meetings ==
{{#ask: [[Category:OntologySummit2026]] [[Category:Icom_conf_Conference]] [[<<ConferenceCall_2026_06_09]]
|?|?Session|mainlabel=-|order=desc|limit=3}}

== Next Meetings ==
{{#ask: [[Category:OntologySummit2026]] [[Category:Icom_conf_Conference]] [[>>ConferenceCall_2026_06_11]]
|?|?Session|mainlabel=-|order=asc|limit=3}}

[[Category:Icom_conf_Conference]]
[[Category:Occurrence| ]]
[[Category:OntologySummit2026| ]]

ConferenceCall 2026 06 10

2026-06-10T22:18:50Z

KennethBaclawski:

{| class="wikitable" style="float:right; margin-left: 10px;" border="1" cellpadding="10"
|-
! scope="row" | Session
| [[session::Education]]
|-
! scope="row" | Duration
| [[duration::1 hour]]
|-
! scope="row" rowspan="3" | Date/Time
| [[has date::10 June 2026 16:00 GMT]]
|-
| 9:00am PDT/12:00pm EDT
|-
| 5:00pm BST/6:00pm CST
|-
! scope="row" | Convener
| [[convener::KenBaclawski|Ken Baclawski]]
|}

= [[OntologySummit2026|Ontology Summit 2026]] {{#show:{{PAGENAME}}|?session}} =
* '''Bill Mandrick''' ''Ontology Engineering 101 - From Expert Knowledge to Ontological Models''
** [https://ontologforum.s3.us-east-1.amazonaws.com/OntologySummit2026/Education/Ontology-Engineering-101--BillMandrick_20260610.mp4 Video Recording]
** Abstract: Ontology engineering is a technical field, but at its core it begins with a familiar philosophical task of making distinctions clear. Organizations routinely depend on data whose meaning is only partly understood. Ontology engineering provides a disciplined method for moving from informal expert language to reusable, inspectable, machine-checkable representations. In this talk, Bill Mandrick will present an overview of the weekly Ontology 101 series, 6-week cycles focused on developing foundational ontology skills with hands-on practice. The first two weeks involve participants learning how to work with subject matter experts without trying to turn them into ontologists, the goal being to elicit and refine competency questions: clear, testable questions that the ontology should help answer. In the second two weeks, those questions are translated into visual design patterns that expose the relevant entities, relations, roles, processes, and constraints. In the last pair of weeks, the patterns are implemented in OWL using tools such as Protégé, tested with reasoners, and evaluated against the original competency questions. Throughout the aim is not to master ontology engineering in a single session, but to understand the basic rhythm of the work, engage experts, clarify meaning, model the structure, encode the result, test it, and revise.
** Bio: Bill Mandrick, Ph.D. is a senior ontologist at CUBRC and retired U.S. Army Colonel whose work has focused on ontology development, OWL/RDF representation, Basic Formal Ontology compliance, and military/intelligence applications of ontology. Dr. Mandrick is a long-time contributor to early military ontology work and to NCOR/CUBRC best-practices work in ontology development. He also co-authored work with Barry Smith on the philosophical foundations of intelligence collection and analysis, including the role of BFO and the Common Core Ontologies in semantic interoperability for intelligence systems. Dr. Mandrick is the chair of the rather successful "Ontology 101” weekly working group, sponsored by NCOR.

== Conference Call Information ==
* Date: '''Wednesday, 10 June 2026'''
* Start Time: 9:00am PDT / 12:00pm EDT / 6:00pm CEST / 5:00pm BST / 1600 UTC
** ref: [http://www.timeanddate.com/worldclock/fixedtime.html?month=6&day=10&year=2026&hour=12&min=00&sec=0&p1=179 World Clock]
* Expected Call Duration: 1 hour
{{:OntologySummit2026/ConferenceCallInformation}}

== Discussion ==
10:15:52 Mike Bennett: Why are regulated products a subclass? Being regulated is not an inherent property of a product.

10:23:13 Victor.Bagwell: I'm a data guy, so to speak. So I view this as analogous to relational database normalization to 3rd normal down to dimension tables where the normalization passes are repeated until each hierarchy goes down to mutual exclusive components. Albeit, going a bit beyond how to store/access the data to ask what exists in reality and "how" they are related. Essentially a semantic normalization.

10:24:00 Paul Tyson: Session idea: unbiased head-to-head comparison of CL and OWL with working examples and applications. Does anyone know of such a resource already available?

10:26:59 Bill Mandrick: william.mandrick@cubrc.org

10:27:19 Gary Berg-Cross: I find that you can use, as a starting point an AGI agent to find definitions for things not in an Ontology. For example five folate-related chemicals. Here are their 2 definitions:
# Pteroylmonoglutamic Acid The synthetic form of vitamin B9 (folic acid), representing the first molecule in an enzymatic process that results in the bioactive form of folate. It has high bioavailability and is the only folate form authorized in fortified foods and drugs. Rootine
# 5-Methyltetrahydrofolate (5-MTHF) The main food folate and principal form of vitamin B9 found in plasma. It is the end product of folate metabolism and is involved in the remethylation of homocysteine to methionine, a critical step in methionine and DNA synthesis. ScienceDirectDoveMed
# Tetrahydrofolate (THF) A derivative of vitamin B9 and a coenzyme for metabolic reactions involving amino acid and nucleic acid formations. It participates in important single-carbon transfer reactions — often referred to as one-carbon metabolism — and in synthesizing several amino acids such as serine and methionine, purines, and thymine. Chemically, it consists of three structural components: para-aminobenzoic acid (PABA), a bicyclic pteridine ring, and glutamic acid. NCBI
# L-Methylfolate (5-MTHF) The bioactive, naturally occurring form of folate. It is the metabolic end point of the folate cycle, produced when the MTHFR enzyme converts 5,10-methylenetetrahydrofolate into 5-methyltetrahydrofolate. It is the only form of folate that can cross the blood-brain barrier. MDPI

10:29:17 Gary Berg-Cross: The Defs can then be axiomatized
* Object Properties — 9 semantic relations including has_metabolic_precursor, donates_group, converted_by_enzyme, is_active_form_of, and crosses_barrier, all with inverse declarations where applicable.
* Data Properties — 9 annotation/data properties covering molecular formula, molar mass, CAS number, ChEBI/PubChem IDs, IUPAC name, and boolean flags like is_synthetic and crosses_blood_brain_barrier.
* Class Hierarchy: ChemicalEntity → VitaminCompound → FolateCompound → CoenzymeFolate / SyntheticFolate
** Each of the five compounds as a named class with full OWL restriction axioms
* Key Axioms per compound:
** PteroylmonoglutamicAcid — synthetic, fully oxidised, is_metabolic_precursor_of THF, converted by DHFR
** Tetrahydrofolate — active form, OneCarbon_Carrier, precursor to both 5-MTHF and 10-CHO-THF
** FiveMethyltetrahydrofolate — MethylGroupDonor, crosses BBB, participates in homocysteine remethylation, converted by MTHFR
** LMethylfolate — declared owl:equivalentClass to 5-MTHF (same entity, different clinical name)
** TenFormyltetrahydrofolate — FormylGroupDonor at N10, drives purine synthesis
*General Class Axioms (GCIs) — closure axioms enforcing that only 5-MTHF crosses the BBB within this compound set, and that N10-formyl donation entails membership in TenFormyltetrahydrofolate.

10:30:32 Gary Berg-Cross: Here's a start on an ontology for your diagram. Here's the full ontology, directly loadable in Protégé or any OWL 2 DL reasoner (HermiT, Pellet, ELK). Here's a breakdown of what was axiomatized from the diagram:
* Upper Ontology (BFO-aligned, left spine of the graph) The full BFO chain is reproduced: Entity → Continuant → IndependentContinuant → MaterialEntity → Object → PortionOfProcessedMaterial, with BFO IRIs annotated on each class. The right spine follows Continuant → GenericallDependentContinuant → InformationContentEntity → DirectiveInformationContentEntity.
* Object Properties (11 named relations) All relations visible in the diagram are formally declared — has_ingredient, prescribed_by / prescribes, complies_with, governed_by / governs, specifies / specified_by, has_part / part_of (BFO-aligned), and concretizes / is_concretized_by — with domain/range documentation and inverses where applicable.
* Domain Class Hierarchy (two branches)
** Material branch: PortionOfProcessedMaterial → RegulatedProduct → SupplementProduct → VitaminSupplementProduct (with VitaminIngredient as a sibling)
** Directive branch: PerformanceSpecification → IntendedUseStatement → SupplementUseStatement; QualitySpecification → IngredientSpecification; ProcessRegulation → RegulatoryFramework → SupplementRegulatoryFramework → UruguaySupplementRegulatoryFramework
* Instance Layer — all four pink-bordered individuals from the diagram are instantiated with their exact named relations: VitaminSupplementProduct001 has_ingredient VitaminIngredient001, IngredientSpecification001 prescribes SupplementUseStatement001, and the Uruguay framework instance governs the product and prescribes the spec.
* Disjointness, GCIs,....
* Property Chains enforce closure: e.g., only 5-MTHF crosses the BBB (from the folate ontology pattern), anything governed by a Uruguay framework is a SupplementProduct, and a has_ingredient o specified_by chain propagates prescribed_by transitively.
* The diagram faithfully reproduces the ontograph structure. to read it:
** Color encoding gray boxes are BFO upper ontology (entity, continuant, independent/generically dependent continuant); blue is the material branch (material entity → object); teal is the information branch (ICE → Directive ICE and its three direct subclasses); purple is the domain layer (all the supplement-specific classes); amber with a heavier border marks the 5 named instances at the bottom.
** Left spine follows the BFO material chain down to Portion of Processed Material, which splits into Regulated Product (leading to Supplement Product → Vitamin Supplement Product) & Vitamin Ingredient side by side.
* Right spine descends from Generically Dependent Continuant → ICE → Directive ICE, which fans into three parallel columns: Performance Specification → Intended Use Statement → Supplement Use Statement; Quality Specification → Ingredient Specification; and Process Regulation → Regulatory Framework → Supplement Regulatory Framework → Uruguay Supplement Regulatory Framework.
* Instance layer (below the dashed separator) shows all five individuals with their named object-property relations rendered as colored dashed arrows: has ingredient, specified by, prescribed by, governed by, complies with, prescribes, and documents. Every node is clickable for deeper explanation.

10:28:12 Michael DeBellis: Since you're using Web Protege you couldn't define any axioms on classes. Do you have a process where you do further elaboration on the model and add axioms?

10:37:01 Paul A. Pope: Could non-structural characterizations, like "regulated", be placed in the Annotations attached to a class? Perhaps in a Description annotation? "Description: Regulated" or "Regulated: Yes" (BTW, I don't have mic capability)

10:40:45 Michael DeBellis: When modeling a domain, IMO you seldom use the natural language definition. That's far too expansive. In a general NLP ontology speed and velocity are synonyms. In a physics ontology velocity is a vector and speed is a scalar. The same is true for classes like product, purchase order, etc.

10:42:19 TS: In natural language processing the corpus of material processed provides the context for interpretation.

10:50:48 Victor.Bagwell: reponse to Michael/TS...just for clarity -- My comments were related to a foundational corpus. I've been involved in NLP for sometime and understand the specific effects of domains (specific to). Rather, I was thinking about public common use case at the top (e.g., websters) and what the prob. is of semantics for a specific word. Then a hierarchy that includes are is split (perhaps by domain), and adjusted for the hierarchical levels by the domain, sub-domain, sub-sub-domain, n as a cross section and then over time and perhaps even by other compnents (e.g., language, education culture, geography, to name a few).

Ultimately -- highly dimensional

I was thinking about how to operational information in real context

10:52:04 Paul A. Pope: (circling back here) Could non-structural characterizations, like "regulated", be placed in the Annotations attached to a class? Perhaps in a Description annotation? "Description: Regulated" or "Regulated: Yes"
* Thank you for addressing my question. Great discussion. Looking forward to subsequent meetings.

11:01:37 Gary Berg-Cross: We have to define what subsequent meetings we will have.

11:11:34 Victor.Bagwell: Thank you! Always learning from all of you.
* Marcia Zeng: 👍

== Resources ==
* [https://ontologforum.s3.us-east-1.amazonaws.com/OntologySummit2026/Education/Ontology-Engineering-101--BillMandrick_20260610.mp4 Video Recording]

== Previous Meetings ==
{{#ask: [[Category:OntologySummit2026]] [[Category:Icom_conf_Conference]] [[<<ConferenceCall_2026_06_09]]
|?|?Session|mainlabel=-|order=desc|limit=3}}

== Next Meetings ==
{{#ask: [[Category:OntologySummit2026]] [[Category:Icom_conf_Conference]] [[>>ConferenceCall_2026_06_11]]
|?|?Session|mainlabel=-|order=asc|limit=3}}

[[Category:Icom_conf_Conference]]
[[Category:Occurrence| ]]
[[Category:OntologySummit2026| ]]

ConferenceCall 2026 06 03

2026-06-08T03:49:44Z

KennethBaclawski:

{| class="wikitable" style="float:right; margin-left: 10px;" border="1" cellpadding="10"
|-
! scope="row" | Session
| [[session::Cognition]]
|-
! scope="row" | Duration
| [[duration::1 hour]]
|-
! scope="row" rowspan="3" | Date/Time
| [[has date::03 Jun 2026 16:00 GMT]]
|-
| 9:00am PDT/12:00pm EDT
|-
| 5:00pm BST/6:00pm CST
|-
! scope="row" | Convener
| [[convener::KenBaclawski|Ken Baclawski]]
|}

= [[OntologySummit2026|Ontology Summit 2026]] {{#show:{{PAGENAME}}|?session}} =
* '''[[ArunMajumdar|Arun K. Majumdar]]''' and '''[[JohnSowa|John F. Sowa]]''' ''Reasoning Beats Pattern Matching''
** For over 60 years, the best AI reasoning was based on the four step cognitive cycle: abduction, deduction, evaluation, induction, and repeat. Abduction generates hypotheses or educated guesses. Deduction derives implications. Evaluation chooses the best option. Induction combines the result with previous knowledge. Many versions of the cognitive cycle have been invented and named. For guiding fighter pilots, John Boyd called it the OODA loop: Observe, Orient, Decide, Act. He originally said that each step would be traversed in milliseconds, but he later applied the loop to design and analysis steps that may take minutes, hours, or days. Whatever the time scale, the four steps are fundamental to reasoning in science, business, and life. The pattern matching methods of Large Language Models (LLMs) are superb for translating languages, natural or artificial. They are also good for finding and relating patterns in large volumes of data of any kind. That enables them to answer questions by finding information or by applying previous methods to new data. For many problems, pattern matching can discover abductions or educated guesses. But deduction and evaluation cannot be done unless a similar cognitive cycle can be found somewhere on the WWW. With the VivoMind system from 2000 to 2010, the authors used conceptual graphs for symbolic reasoning about a wide range of problems. For the new Permion system, they added LLM pattern matching to map conceptual graphs to and from natural language. But pattern matching, by itself, cannot do any reasoning unless it can find and adapt an appropriate cycle on the WWW. It often requires a huge amount of searching even for relatively simple examples. In summary, LLMs cannot do reasoning unless and until the system finds a suitable cognitive cycle on the WWW. But the Permion reasoning methods automatically do the four-step cycle. If necessary, they can also do LLM searching, but none is required.
** [https://ontologforum.s3.us-east-1.amazonaws.com/OntologySummit2026/Cognition/Neurosymbolic-AI-to-Check-and-Correct-LLMs--JohnSowa_20260603.mp4 Video Recording]
** [https://youtu.be/oy-uzJuSr70 YouTube Video]

== Conference Call Information ==
* Date: '''Wednesday, 03 June 2026'''
* Start Time: 9:00am PDT / 12:00pm EDT / 6:00pm CEST / 5:00pm BST / 1600 UTC
** ref: [http://www.timeanddate.com/worldclock/fixedtime.html?month=6&day=3&year=2026&hour=12&min=00&sec=0&p1=179 World Clock]
* Expected Call Duration: 1.5 hour
{{:OntologySummit2026/ConferenceCallInformation}}

== Discussion ==
Josh Lieberman 12:30
The Burns quote is no longer a joke since present chatbots are dangerously good at faking sincerity and empathy.

Wesley Spacebar 12:32
except that they can't make eye contact yet

Josh Lieberman 12:35 (Edited)
You jest, but a huge number of people are using chatbots as defacto therapists because they are convinced they’re being “seen”.

Wesley Spacebar 12:36
well, LLMs don't have the best intersubjectivity in relationships, but they are trained to behave nicely by smart people in SF

Alican Tuezuen 12:38
Arun, are you using some sort of standard for the prompt or how did you come up with that "structure" of the prompt?

Nanjangud Narendra 12:46
Are Conceptual Graphs used here? If so, how?

Hussein Ezzeldin (FDA/CDER/OMP) 12:49
what is the difference between agents?

Gary Berg-Cross 12:53
What foundational models do you use?

Marcia Zeng 12:54
Can you demo your answer with any knowledge graph (KG)?

Gary Berg-Cross 12:56
Is common logic used to store the developed ontologies?

Simon Polovina 12:57
Can you demo the use of CGs?

Marcia Zeng 13:00
FYI: There is a demo in a previous session recorded: https://youtu.be/6K6F_zsQ264

Gary Berg-Cross 13:02
Is abductive reasoning used in the examples you showed?
Do you have an abductive agent?
So you could ask the system show this reasoning workflow

Gary Berg-Cross 13:11
At this point We can probably agree that your system is not an agent in the world and doesn't have real feelings but the idea is that you might expand on a BDI belief desire intention model and create a model that connects some likely feelings to an agent that includes BDI aspects.
Is there a plan to connect to and use an app like Mathematica for math computation?

== Resources ==
* [https://ontologforum.s3.us-east-1.amazonaws.com/OntologySummit2026/Cognition/Neurosymbolic-AI-to-Check-and-Correct-LLMs--JohnSowa_20260603.mp4 Video Recording]
* [https://youtu.be/oy-uzJuSr70 YouTube Video]

== Previous Meetings ==
{{#ask: [[Category:OntologySummit2026]] [[Category:Icom_conf_Conference]] [[<<ConferenceCall_2026_06_02]]
|?|?Session|mainlabel=-|order=desc|limit=3}}

== Next Meetings ==
{{#ask: [[Category:OntologySummit2026]] [[Category:Icom_conf_Conference]] [[>>ConferenceCall_2026_06_04]]
|?|?Session|mainlabel=-|order=asc|limit=3}}

[[Category:Icom_conf_Conference]]
[[Category:Occurrence| ]]
[[Category:OntologySummit2026| ]]

ConferenceCall 2026 06 03

2026-06-04T19:56:00Z

KennethBaclawski:

{| class="wikitable" style="float:right; margin-left: 10px;" border="1" cellpadding="10"
|-
! scope="row" | Session
| [[session::Cognition]]
|-
! scope="row" | Duration
| [[duration::1 hour]]
|-
! scope="row" rowspan="3" | Date/Time
| [[has date::03 Jun 2026 16:00 GMT]]
|-
| 9:00am PDT/12:00pm EDT
|-
| 5:00pm BST/6:00pm CST
|-
! scope="row" | Convener
| [[convener::KenBaclawski|Ken Baclawski]]
|}

= [[OntologySummit2026|Ontology Summit 2026]] {{#show:{{PAGENAME}}|?session}} =
* '''[[ArunMajumdar|Arun K. Majumdar]]''' and '''[[JohnSowa|John F. Sowa]]''' ''Reasoning Beats Pattern Matching''
** For over 60 years, the best AI reasoning was based on the four step cognitive cycle: abduction, deduction, evaluation, induction, and repeat. Abduction generates hypotheses or educated guesses. Deduction derives implications. Evaluation chooses the best option. Induction combies the result with previous knowledge. Many versions of the cognitive cycle have been invented and named. For guiding fighter pilots, John Boyd called it the OODA loop: Observe, Orient, Decide, Act. He originally said that each step would be traversed in milliseconds, but he later applied the loop to design and analysis steps that may take minutes, hours, or days. Whatever the time scale, the four steps are fundamental to reasoning in science, business, and life. The pattern matching methods of Large Language Models (LLMs) are superb for translating languages, natural or artificial. They are also good for finding and relating patterns in large volumes of data of any kind. That enables them to answer questions by finding information or by applying previous methods to new data. For many problems, pattern matching can discover abductions or educated guesses. But deduction and evaluation cannot be done unless a similar cognitive cycle can be found somewhere on the WWW. With the VivoMind system from 2000 to 2010, the authors used conceptual graphs for symbolic reasoning about a wide range of problems. For the new Permion system, they added LLM pattern matching to map conceptual graphs to and from natural language. But pattern matching, by itself, cannot do any reasoning unless it can find and adapt an appropriate cycle on the WWW. It often requires a huge amount of searching even for relatively simple examples. In summary, LLMs cannot do reasoning unless and until the system finds a suitable cognitive cycle on the WWW. But the Permion reasoning methods automatically do the four-step cycle. If necessary, they can also do LLM searching, but none is required.
** [https://ontologforum.s3.us-east-1.amazonaws.com/OntologySummit2026/Cognition/Neurosymbolic-AI-to-Check-and-Correct-LLMs--JohnSowa_20260603.mp4 Video Recording]

== Conference Call Information ==
* Date: '''Wednesday, 03 June 2026'''
* Start Time: 9:00am PDT / 12:00pm EDT / 6:00pm CEST / 5:00pm BST / 1600 UTC
** ref: [http://www.timeanddate.com/worldclock/fixedtime.html?month=6&day=3&year=2026&hour=12&min=00&sec=0&p1=179 World Clock]
* Expected Call Duration: 1.5 hour
{{:OntologySummit2026/ConferenceCallInformation}}

== Discussion ==
Josh Lieberman 12:30
The Burns quote is no longer a joke since present chatbots are dangerously good at faking sincerity and empathy.

Wesley Spacebar 12:32
except that they can't make eye contact yet

Josh Lieberman 12:35 (Edited)
You jest, but a huge number of people are using chatbots as defacto therapists because they are convinced they’re being “seen”.

Wesley Spacebar 12:36
well, LLMs don't have the best intersubjectivity in relationships, but they are trained to behave nicely by smart people in SF

Alican Tuezuen 12:38
Arun, are you using some sort of standard for the prompt or how did you come up with that "structure" of the prompt?

Nanjangud Narendra 12:46
Are Conceptual Graphs used here? If so, how?

Hussein Ezzeldin (FDA/CDER/OMP) 12:49
what is the difference between agents?

Gary Berg-Cross 12:53
What foundational models do you use?

Marcia Zeng 12:54
Can you demo your answer with any knowledge graph (KG)?

Gary Berg-Cross 12:56
Is common logic used to store the developed ontologies?

Simon Polovina 12:57
Can you demo the use of CGs?

Marcia Zeng 13:00
FYI: There is a demo in a previous session recorded: https://youtu.be/6K6F_zsQ264

Gary Berg-Cross 13:02
Is abductive reasoning used in the examples you showed?
Do you have an abductive agent?
So you could ask the system show this reasoning workflow

Gary Berg-Cross 13:11
At this point We can probably agree that your system is not an agent in the world and doesn't have real feelings but the idea is that you might expand on a BDI belief desire intention model and create a model that connects some likely feelings to an agent that includes BDI aspects.
Is there a plan to connect to and use an app like Mathematica for math computation?

== Resources ==
* [https://ontologforum.s3.us-east-1.amazonaws.com/OntologySummit2026/Cognition/Neurosymbolic-AI-to-Check-and-Correct-LLMs--JohnSowa_20260603.mp4 Video Recording]

== Previous Meetings ==
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== Next Meetings ==
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ConferenceCall 2026 06 03

2026-06-04T19:52:47Z

KennethBaclawski: