ConferenceCall 2024 03 06 - Revision history

Forum: /* Agenda */

2024-03-07T22:12:51Z

Agenda

← Older revision		Revision as of 22:12, 7 March 2024
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	* 12:07 - 12:37 '''Hamed Babaei Giglou''' ''Exploring LLMs for Ontology: Ontology Learning and Ontology Matching'' [https://bit.ly/3IqYWAL Slides]		* 12:07 - 12:37 '''Hamed Babaei Giglou''' ''Exploring LLMs for Ontology: Ontology Learning and Ontology Matching'' [https://bit.ly/3IqYWAL Slides]
	Abstract: Large Language Models for Ontology Learning (LLMs4OL) framework utilizes Large Language Models (LLMs) for Ontology Learning (OL). LLMs have shown significant advancements in natural language processing, demonstrating their ability to capture complex language patterns in different knowledge domains. Within the LLMs4OL framework, we investigate the "Can LLMs effectively apply their language pattern capturing capability to OL, which involves automatically extracting and structuring knowledge from natural language text?"		Abstract: Large Language Models for Ontology Learning (LLMs4OL) framework utilizes Large Language Models (LLMs) for Ontology Learning (OL). LLMs have shown significant advancements in natural language processing, demonstrating their ability to capture complex language patterns in different knowledge domains. Within the LLMs4OL framework, we investigate the "Can LLMs effectively apply their language pattern capturing capability to OL, which involves automatically extracting and structuring knowledge from natural language text?"


	To explore LLMs in the area of OL, the first challenge is the formulation of the work and where to use LLM to be suitable for the given task. We conduct a comprehensive analysis using the zero-shot prompting method to evaluate nine different LLM model families for three main OL tasks (where our formulation to use LLMs comes in): term typing, taxonomy discovery, and extraction of non-taxonomic relations. Additionally, the evaluations encompass diverse genres of ontological knowledge, including lexicosemantic knowledge in WordNet, geographical knowledge in GeoNames, and medical knowledge in UMLS.		To explore LLMs in the area of OL, the first challenge is the formulation of the work and where to use LLM to be suitable for the given task. We conduct a comprehensive analysis using the zero-shot prompting method to evaluate nine different LLM model families for three main OL tasks (where our formulation to use LLMs comes in): term typing, taxonomy discovery, and extraction of non-taxonomic relations. Additionally, the evaluations encompass diverse genres of ontological knowledge, including lexicosemantic knowledge in WordNet, geographical knowledge in GeoNames, and medical knowledge in UMLS.


	The obtained empirical results show that foundational LLMs are not sufficiently suitable for ontology construction that entails a high degree of reasoning skills and domain expertise. Nevertheless, when effectively fine-tuned they just might work as suitable assistants, alleviating the knowledge acquisition bottleneck, for ontology construction.		The obtained empirical results show that foundational LLMs are not sufficiently suitable for ontology construction that entails a high degree of reasoning skills and domain expertise. Nevertheless, when effectively fine-tuned they just might work as suitable assistants, alleviating the knowledge acquisition bottleneck, for ontology construction.


	Within this presentation, the main focus will be given to experimented LLM variants and results analysis within the OL task. The whole project is released to the community with detailed documentation here: [https://github.com/HamedBabaei/LLMs4OL https://github.com/HamedBabaei/LLMs4OL]		Within this presentation, the main focus will be given to experimented LLM variants and results analysis within the OL task. The whole project is released to the community with detailed documentation here: [https://github.com/HamedBabaei/LLMs4OL https://github.com/HamedBabaei/LLMs4OL]

Forum: /* Participants */

2024-03-07T22:12:11Z

Participants

← Older revision		Revision as of 22:12, 7 March 2024
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	== Participants ==		== Participants ==
			* [[BobbinTeegarden\|Bobbin Teegarden]]
			* [[FrankLoebe\|Frank Loebe]]
			* [[GaryBergCross\|Gary Berg-Cross]]
			* Hamed Babaei Giglou
			* [[KenBaclawski\|Ken Baclawski]]
			* [[MikeBennett\|Mike Bennett]]
			* Phil Jackson
			* [[RaviSharma\|Ravi Sharma]]
			* [[ToddSchneider\|Todd Schneider]]

	== Discussion ==		== Discussion ==

Forum: /* Resources */

2024-03-07T22:08:49Z

Resources

← Older revision		Revision as of 22:08, 7 March 2024
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	== Resources ==		== Resources ==
			* [https://bit.ly/4a4S1t8 Video Recording]
			* [https://youtu.be/R0Hjnqi2eT0 Youtube Video]

	== Previous Meetings ==		== Previous Meetings ==

Forum: /* Agenda */

2024-03-07T22:08:33Z

Agenda

← Older revision		Revision as of 22:08, 7 March 2024
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	* 12:37 - 13:00 Discussion		* 12:37 - 13:00 Discussion
	* [https://bit.ly/4a4S1t8 Video Recording]		* [https://bit.ly/4a4S1t8 Video Recording]
			* [https://youtu.be/R0Hjnqi2eT0 Youtube Video]

	== Conference Call Information ==		== Conference Call Information ==

Forum: /* Agenda */

2024-03-07T21:15:57Z

Agenda

← Older revision		Revision as of 21:15, 7 March 2024
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	== Agenda ==		== Agenda ==
	* 12:00 - 12:07 '''[[GaryBergCross\|Gary Berg-Cross]]''' ''Summary of some issues from Session 1 and some foundational issues for LLMs vs Cognitive/Symbolic systems'' [https://~~ontologforum~~.~~s3.amazonaws.com/OntologySummit2024/TrackB~~/~~recapitulating+and+foundational+issues+3-5-24.pdf~~ Slides]		* 12:00 - 12:07 '''[[GaryBergCross\|Gary Berg-Cross]]''' ''Summary of some issues from Session 1 and some foundational issues for LLMs vs Cognitive/Symbolic systems'' [https://bit.ly/3IMSrJ3 Slides]
	* 12:07 - 12:37 '''Hamed Babaei Giglou''' ''Exploring LLMs for Ontology: Ontology Learning and Ontology Matching''		* 12:07 - 12:37 '''Hamed Babaei Giglou''' ''Exploring LLMs for Ontology: Ontology Learning and Ontology Matching'' [https://bit.ly/3IqYWAL Slides]
	Abstract: Large Language Models for Ontology Learning (LLMs4OL) framework utilizes Large Language Models (LLMs) for Ontology Learning (OL). LLMs have shown significant advancements in natural language processing, demonstrating their ability to capture complex language patterns in different knowledge domains. Within the LLMs4OL framework, we investigate the "Can LLMs effectively apply their language pattern capturing capability to OL, which involves automatically extracting and structuring knowledge from natural language text?"		Abstract: Large Language Models for Ontology Learning (LLMs4OL) framework utilizes Large Language Models (LLMs) for Ontology Learning (OL). LLMs have shown significant advancements in natural language processing, demonstrating their ability to capture complex language patterns in different knowledge domains. Within the LLMs4OL framework, we investigate the "Can LLMs effectively apply their language pattern capturing capability to OL, which involves automatically extracting and structuring knowledge from natural language text?"

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	Bio: Hamed Babaei Giglou is a researcher at TIB and is currently, involved in the Neural-Symbolic SCholarly InnovatioN EXTraction (SciNEXT) project in collaboration with Open Research Knowledge Graph (ORKG) project at TIB -- German National Library of Science and Technology. Hamed got a bachelor's and Master's degree in computer science and worked as an NLP Researcher for more than 3 years in the industry before joining the TIB as a PhD candidate. Currently, he is pursuing a PhD in "Computer Science: NLP and Semantic Web Technologies" under the supervision of Dr. Jennifer D'Souza and Prof. Dr. Sören Auer. His current research focuses on employing LLM in various ontology tasks, such as Ontology Learning and Ontology Matching.		Bio: Hamed Babaei Giglou is a researcher at TIB and is currently, involved in the Neural-Symbolic SCholarly InnovatioN EXTraction (SciNEXT) project in collaboration with Open Research Knowledge Graph (ORKG) project at TIB -- German National Library of Science and Technology. Hamed got a bachelor's and Master's degree in computer science and worked as an NLP Researcher for more than 3 years in the industry before joining the TIB as a PhD candidate. Currently, he is pursuing a PhD in "Computer Science: NLP and Semantic Web Technologies" under the supervision of Dr. Jennifer D'Souza and Prof. Dr. Sören Auer. His current research focuses on employing LLM in various ontology tasks, such as Ontology Learning and Ontology Matching.
	* 12:37 - 13:00 Discussion		* 12:37 - 13:00 Discussion
			* [https://bit.ly/4a4S1t8 Video Recording]

	== Conference Call Information ==		== Conference Call Information ==

Forum at 18:31, 7 March 2024

2024-03-07T18:31:47Z

← Older revision		Revision as of 18:31, 7 March 2024
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	== Discussion ==		== Discussion ==
	== Foundational Ontologies ==		=== Foundational Ontologies ===

	[12:21] ToddSchneider: How can existing foundational ontologies be incorporated into this work?		[12:21] ToddSchneider: How can existing foundational ontologies be incorporated into this work?

	== Biomedical ==		=== Biomedical ===

	[12:33] DrRavi Sharma : Why NCi related results are lower, is it hat the information is highly correlated in medicine		[12:33] DrRavi Sharma : Why NCi related results are lower, is it hat the information is highly correlated in medicine

	== Reasoning ==		=== Reasoning ===

	[12:35] ToddSchneider: Was a reasoner used to test the (logical) consistency of the resulting artifacts?		[12:35] ToddSchneider: Was a reasoner used to test the (logical) consistency of the resulting artifacts?
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	[12:56] ToddSchneider: Logical reasoning is applied to a collection of logical and non-logical symbols, along with rules of inference (on those symbols).		[12:56] ToddSchneider: Logical reasoning is applied to a collection of logical and non-logical symbols, along with rules of inference (on those symbols).

	== Track D: Finance Panel ==		=== Track D: Finance Panel ===

	[12:56] MikeBennett : Finance is still looking for speakers		[12:56] MikeBennett : Finance is still looking for speakers


	== Resources ==		== Resources ==

Forum: /* Discussion */

2024-03-07T18:30:24Z

Discussion

← Older revision		Revision as of 18:30, 7 March 2024
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	== Discussion ==		== Discussion ==
			== Foundational Ontologies ==

			[12:21] ToddSchneider: How can existing foundational ontologies be incorporated into this work?

			== Biomedical ==

			[12:33] DrRavi Sharma : Why NCi related results are lower, is it hat the information is highly correlated in medicine

			== Reasoning ==

			[12:35] ToddSchneider: Was a reasoner used to test the (logical) consistency of the resulting artifacts?

			[12:41] ToddSchneider: How could the results of a reasoner be incorporated in the ‘process’? That is, how could a reasoner be used as part of the ‘learning’ process?

			[12:47] PhilJackson : can the system learn ontologies of verbs (actions) or adjectives (properties), as well as ontologies of nouns (classes)?

			[12:51] BobbinTeegarden: Doesn't reasoning imply a logical structure, and is there a 'logical structure' assumed in a neural net process?

			[12:56] ToddSchneider: Logical reasoning is applied to a collection of logical and non-logical symbols, along with rules of inference (on those symbols).

			== Track D: Finance Panel ==

			[12:56] MikeBennett : Finance is still looking for speakers

	== Resources ==		== Resources ==

Forum: /* Agenda */

2024-03-06T16:53:59Z

Agenda

← Older revision		Revision as of 16:53, 6 March 2024
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	== Agenda ==		== Agenda ==
	* 12:00 - 12:07 '''[[GaryBergCross\|Gary Berg-Cross]]''' ''Summary of some issues from Session 1 and some foundational issues for LLMs vs Cognitive/Symbolic systems''		* 12:00 - 12:07 '''[[GaryBergCross\|Gary Berg-Cross]]''' ''Summary of some issues from Session 1 and some foundational issues for LLMs vs Cognitive/Symbolic systems'' [https://ontologforum.s3.amazonaws.com/OntologySummit2024/TrackB/recapitulating+and+foundational+issues+3-5-24.pdf Slides]
	* 12:07 - 12:37 '''Hamed Babaei Giglou''' ''Exploring LLMs for Ontology: Ontology Learning and Ontology Matching''		* 12:07 - 12:37 '''Hamed Babaei Giglou''' ''Exploring LLMs for Ontology: Ontology Learning and Ontology Matching''
	Abstract: Large Language Models for Ontology Learning (LLMs4OL) framework utilizes Large Language Models (LLMs) for Ontology Learning (OL). LLMs have shown significant advancements in natural language processing, demonstrating their ability to capture complex language patterns in different knowledge domains. Within the LLMs4OL framework, we investigate the "Can LLMs effectively apply their language pattern capturing capability to OL, which involves automatically extracting and structuring knowledge from natural language text?"		Abstract: Large Language Models for Ontology Learning (LLMs4OL) framework utilizes Large Language Models (LLMs) for Ontology Learning (OL). LLMs have shown significant advancements in natural language processing, demonstrating their ability to capture complex language patterns in different knowledge domains. Within the LLMs4OL framework, we investigate the "Can LLMs effectively apply their language pattern capturing capability to OL, which involves automatically extracting and structuring knowledge from natural language text?"

Forum at 03:41, 3 March 2024

2024-03-03T03:41:22Z

@@ Line 21: / Line 21: @@
 == Agenda ==
-* '''Hamed Babaei Giglou''' ''LLMs4OL: Large Language Models for Ontology Learning''
+* 12:00 - 12:07 '''[[GaryBergCross|Gary Berg-Cross]]''' ''Summary of some issues from Session 1 and some foundational issues for LLMs vs Cognitive/Symbolic systems''
 == Conference Call Information ==

Forum: Created page with "{| class="wikitable" style="float:right; margin-left: 10px;" border="1" cellpadding="10" |- ! scope="row" | Session | session::LLMs, Ontologies and KGs |- ! scope="row" | Duration | duration::1 hour |- ! scope="row" rowspan="3" | Date/Time | has date::6 Mar 2024 17:00 GMT |- | 9:00am PST/12:00pm EST |- | 5:00pm GMT/6:00pm CET |- ! scope="row" | Convener | Gary Berg-Cross |} = Ontology Summit 2024 {{#show:{{P..."

2024-02-19T20:07:36Z

Created page with "{| class="wikitable" style="float:right; margin-left: 10px;" border="1" cellpadding="10" |- ! scope="row" | Session | session::LLMs, Ontologies and KGs |- ! scope="row" | Duration | duration::1 hour |- ! scope="row" rowspan="3" | Date/Time | has date::6 Mar 2024 17:00 GMT |- | 9:00am PST/12:00pm EST |- | 5:00pm GMT/6:00pm CET |- ! scope="row" | Convener | Gary Berg-Cross |} = Ontology Summit 2024 {{#show:{{P..."

New page

{| class="wikitable" style="float:right; margin-left: 10px;" border="1" cellpadding="10"
|-
! scope="row" | Session
| [[session::LLMs, Ontologies and KGs]]
|-
! scope="row" | Duration
| [[duration::1 hour]]
|-
! scope="row" rowspan="3" | Date/Time
| [[has date::6 Mar 2024 17:00 GMT]]
|-
| 9:00am PST/12:00pm EST
|-
| 5:00pm GMT/6:00pm CET
|-
! scope="row" | Convener
| [[convener::GaryBergCross|Gary Berg-Cross]]
|}

= [[OntologySummit2024|Ontology Summit 2024]] {{#show:{{PAGENAME}}|?session}} =

== Agenda ==
* '''Hamed Babaei Giglou''' ''LLMs4OL: Large Language Models for Ontology Learning''

== Conference Call Information ==
* Date: '''Wednesday, 6 March 2024'''
* Start Time: 9:00am PST / 12:00pm EST / 6:00pm CET / 5:00pm GMT / 1700 UTC
** ref: [http://www.timeanddate.com/worldclock/fixedtime.html?month=3&day=6&year=2024&hour=12&min=00&sec=0&p1=179 World Clock]
* Expected Call Duration: 1 hour
{{:OntologySummit2024/ConferenceCallInformation}}

== Participants ==

== Discussion ==

== Resources ==

== Previous Meetings ==
{{#ask: [[Category:OntologySummit2024]] [[Category:Icom_conf_Conference]] [[<<ConferenceCall_2023_03_06]]
|?|?Session|mainlabel=-|order=desc|limit=3}}

== Next Meetings ==
{{#ask: [[Category:OntologySummit2024]] [[Category:Icom_conf_Conference]] [[>>ConferenceCall_2023_03_06]]
|?|?Session|mainlabel=-|order=asc|limit=3}}

[[Category:OntologySummit2024]]
[[Category:Icom_conf_Conference]]
[[Category:Occurrence| ]]