Ontolog Forum - User contributions [en]

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 ==
{{#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

2026-06-04T19:52:47Z

KennethBaclawski: