ConferenceCall 2023 10 25 - Revision history

Forum at 20:56, 16 February 2024

2024-02-16T20:56:39Z

← Older revision		Revision as of 20:56, 16 February 2024
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	\|}		\|}

	= [[~~OntologySummit2024~~\|Ontology Summit 2024]] {{#show:{{PAGENAME}}\|?session}} =		= [[OntologySummit2024_FallSeries\|Ontology Summit 2024 Fall Series]] {{#show:{{PAGENAME}}\|?session}} =

	== Agenda ==		== Agenda ==
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	\|?\|?Session\|mainlabel=-\|order=asc\|limit=3}}		\|?\|?Session\|mainlabel=-\|order=asc\|limit=3}}

	[[Category:~~OntologySummit2024~~]]		[[Category:OntologySummit2024_FallSeries]]
	[[Category:Icom_conf_Conference]]		[[Category:Icom_conf_Conference]]
	[[Category:Occurrence\| ]]		[[Category:Occurrence\| ]]

AndreaWesterinen: /* Participants */

2023-11-13T15:42:13Z

Participants

← Older revision		Revision as of 15:42, 13 November 2023
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	* silke		* silke
	* James Logan		* James Logan
			* [[AlexShkotin\|Alex Shoktin]]
			* Anders Tell
			* [[AnatolyLevenchuk\|Anatoly Levenchuk]]
			* Nancy Wiegand
			* Stefan De Giorgis
			* [[JohnSowa\|John Sowa]]

	Complete list of participants was not captured		Complete list of participants was not captured

Forum: /* Resources */

2023-11-08T21:05:19Z

Resources

← Older revision		Revision as of 21:05, 8 November 2023
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	* [https://bit.ly/46VaEOu Yuan He Slides]		* [https://bit.ly/46VaEOu Yuan He Slides]
	* [https://bit.ly/3Q3o0Be Video Recording]		* [https://bit.ly/3Q3o0Be Video Recording]
			* [https://youtu.be/OYJWARcH5B0 YouTube Video]

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

AndreaWesterinen: /* Participants */

2023-11-04T17:36:32Z

Participants

← Older revision		Revision as of 17:36, 4 November 2023
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	== Participants ==		== Participants ==
	~~Complete list of participants was not captured~~

	* Evren Sirin		* Evren Sirin
	* Yuan He		* Yuan He
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	* silke		* silke
	* James Logan		* James Logan

			Complete list of participants was not captured

	== Discussion ==		== Discussion ==

AndreaWesterinen: /* Participants */

2023-11-04T17:36:15Z

Participants

← Older revision		Revision as of 17:36, 4 November 2023
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	== Participants ==		== Participants ==
			Complete list of participants was not captured

	* Evren Sirin		* Evren Sirin
	* Yuan He		* Yuan He

AndreaWesterinen at 17:35, 4 November 2023

2023-11-04T17:35:24Z

← Older revision		Revision as of 17:35, 4 November 2023
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	== Participants ==		== Participants ==
			* Evren Sirin
			* Yuan He
			* Jiaoyan Chen
			* Hang Dong
			* [[KenBaclawski\|Ken Baclawski]]
			* [[AndreaWesterinen\|Andrea Westerinen]]
			* [[MikeBennett\|Mike Bennett]]
			* [[RaviSharma\|Ravi Sharma]]
			* [[MichaelDeBellis\|Michael DeBellis]]
			* Riley Moher
			* [[ToddSchneider\|Todd Schneider]]
			* [[PeteRivett\|Pete Rivett]]
			* [[DouglasMiles\|Douglas Miles]]
			* [[JohnSowa\|John Sowa]]
			* [[BartGajderowicz\|Bart Gajderowicz]]
			* E S
			* [[GaryBergCross\|Gary Berg-Cross]]
			* silke
			* James Logan

	== Discussion ==		== Discussion ==

AndreaWesterinen at 17:29, 4 November 2023

2023-11-04T17:29:48Z

Show changes

Forum: /* Resources */

2023-10-25T19:03:42Z

Resources

← Older revision		Revision as of 19:03, 25 October 2023
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	== Resources ==		== Resources ==
			* [https://bit.ly/3MeGrSy Evrin Sirin Slides]
			* [https://bit.ly/46VaEOu Yuan He Slides]
			* [https://bit.ly/3Q3o0Be Video Recording]

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

Forum: /* Agenda */

2023-10-25T19:02:14Z

Agenda

← Older revision		Revision as of 19:02, 25 October 2023
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	** '''Title:''' Stardog Voicebox: LLM-Powered Question Answering with Knowledge Graphs		** '''Title:''' Stardog Voicebox: LLM-Powered Question Answering with Knowledge Graphs
	** '''Abstract:''' Large Language Models (LLMs) and Generative AI technologies have caused a shift in all areas of information technology but especially for question answering use cases. Leveraging LLMs for question answering can help fully democratize enterprise analytics and data access. However, using LLMs with enterprise data bring significant challenges around security, privacy, accuracy, and explainabilty. In this talk we will present Stardog [https://www.stardog.com/categories/voicebox/ Voicebox] which leverages an open-source foundational LLM to build, manage, and query knowledge graphs using ordinary language. The answers to user questions directly come from the knowledge graph providing complete traceability and access control. Stardog Voicebox combines statistical reasoning in the form of LLMs with logical reasoning in knowledge graphs providing a powerful hybrid reasoning system with a natural language interface.		** '''Abstract:''' Large Language Models (LLMs) and Generative AI technologies have caused a shift in all areas of information technology but especially for question answering use cases. Leveraging LLMs for question answering can help fully democratize enterprise analytics and data access. However, using LLMs with enterprise data bring significant challenges around security, privacy, accuracy, and explainabilty. In this talk we will present Stardog [https://www.stardog.com/categories/voicebox/ Voicebox] which leverages an open-source foundational LLM to build, manage, and query knowledge graphs using ordinary language. The answers to user questions directly come from the knowledge graph providing complete traceability and access control. Stardog Voicebox combines statistical reasoning in the form of LLMs with logical reasoning in knowledge graphs providing a powerful hybrid reasoning system with a natural language interface.
	* [https://bit.ly/3MeGrSy Slides]		** [https://bit.ly/3MeGrSy Slides]
	* '''Yuan He''', Key contributor to [https://krr-oxford.github.io/DeepOnto/ DeepOnto], a package for ontology engineering with deep learning		* '''Yuan He''', Key contributor to [https://krr-oxford.github.io/DeepOnto/ DeepOnto], a package for ontology engineering with deep learning
	** '''Title:''' DeepOnto: A Python Package for Ontology Engineering with Deep Learning and Language Models		** '''Title:''' DeepOnto: A Python Package for Ontology Engineering with Deep Learning and Language Models
	** '''Abstract:''' Integrating deep learning techniques, particularly language models (LMs), with knowledge representations like ontologies has raised widespread attention, urging the need for a platform that supports both paradigms. However, deep learning frameworks like PyTorch and Tensorflow are predominantly developed for Python programming, while widely-used ontology APIs, such as the OWL API and Jena, are primarily Java-based. To facilitate seamless integration of these frameworks and APIs, we present [https://krr-oxford.github.io/DeepOnto/ DeepOnto], a Python package designed for ontology engineering with deep learning. The package encompasses a core ontology processing module founded on the widely-recognized and reliable OWL API, encapsulating its fundamental features in a more “Pythonic” manner and extending its capabilities to incorporate other essential components including reasoning, verbalization, normalization, projection, taxonomy, and more. Building on this module, DeepOnto offers a suite of tools, resources, and algorithms that support various ontology engineering tasks, such as ontology alignment and completion, by harnessing deep learning methods, primarily pre-trained LMs.		** '''Abstract:''' Integrating deep learning techniques, particularly language models (LMs), with knowledge representations like ontologies has raised widespread attention, urging the need for a platform that supports both paradigms. However, deep learning frameworks like PyTorch and Tensorflow are predominantly developed for Python programming, while widely-used ontology APIs, such as the OWL API and Jena, are primarily Java-based. To facilitate seamless integration of these frameworks and APIs, we present [https://krr-oxford.github.io/DeepOnto/ DeepOnto], a Python package designed for ontology engineering with deep learning. The package encompasses a core ontology processing module founded on the widely-recognized and reliable OWL API, encapsulating its fundamental features in a more “Pythonic” manner and extending its capabilities to incorporate other essential components including reasoning, verbalization, normalization, projection, taxonomy, and more. Building on this module, DeepOnto offers a suite of tools, resources, and algorithms that support various ontology engineering tasks, such as ontology alignment and completion, by harnessing deep learning methods, primarily pre-trained LMs.
	* [https://bit.ly/46VaEOu Slides]		** [https://bit.ly/46VaEOu Slides]
			* [https://bit.ly/3Q3o0Be Video Recording]

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

Forum: /* Agenda */

2023-10-25T15:56:51Z

Agenda

← Older revision		Revision as of 15:56, 25 October 2023
Line 24:		Line 24:
	** '''Title:''' Stardog Voicebox: LLM-Powered Question Answering with Knowledge Graphs		** '''Title:''' Stardog Voicebox: LLM-Powered Question Answering with Knowledge Graphs
	** '''Abstract:''' Large Language Models (LLMs) and Generative AI technologies have caused a shift in all areas of information technology but especially for question answering use cases. Leveraging LLMs for question answering can help fully democratize enterprise analytics and data access. However, using LLMs with enterprise data bring significant challenges around security, privacy, accuracy, and explainabilty. In this talk we will present Stardog [https://www.stardog.com/categories/voicebox/ Voicebox] which leverages an open-source foundational LLM to build, manage, and query knowledge graphs using ordinary language. The answers to user questions directly come from the knowledge graph providing complete traceability and access control. Stardog Voicebox combines statistical reasoning in the form of LLMs with logical reasoning in knowledge graphs providing a powerful hybrid reasoning system with a natural language interface.		** '''Abstract:''' Large Language Models (LLMs) and Generative AI technologies have caused a shift in all areas of information technology but especially for question answering use cases. Leveraging LLMs for question answering can help fully democratize enterprise analytics and data access. However, using LLMs with enterprise data bring significant challenges around security, privacy, accuracy, and explainabilty. In this talk we will present Stardog [https://www.stardog.com/categories/voicebox/ Voicebox] which leverages an open-source foundational LLM to build, manage, and query knowledge graphs using ordinary language. The answers to user questions directly come from the knowledge graph providing complete traceability and access control. Stardog Voicebox combines statistical reasoning in the form of LLMs with logical reasoning in knowledge graphs providing a powerful hybrid reasoning system with a natural language interface.
			* [https://bit.ly/3MeGrSy Slides]
	* '''Yuan He''', Key contributor to [https://krr-oxford.github.io/DeepOnto/ DeepOnto], a package for ontology engineering with deep learning		* '''Yuan He''', Key contributor to [https://krr-oxford.github.io/DeepOnto/ DeepOnto], a package for ontology engineering with deep learning
	** '''Title:''' DeepOnto: A Python Package for Ontology Engineering with Deep Learning and Language Models		** '''Title:''' DeepOnto: A Python Package for Ontology Engineering with Deep Learning and Language Models
	** '''Abstract:''' Integrating deep learning techniques, particularly language models (LMs), with knowledge representations like ontologies has raised widespread attention, urging the need for a platform that supports both paradigms. However, deep learning frameworks like PyTorch and Tensorflow are predominantly developed for Python programming, while widely-used ontology APIs, such as the OWL API and Jena, are primarily Java-based. To facilitate seamless integration of these frameworks and APIs, we present [https://krr-oxford.github.io/DeepOnto/ DeepOnto], a Python package designed for ontology engineering with deep learning. The package encompasses a core ontology processing module founded on the widely-recognized and reliable OWL API, encapsulating its fundamental features in a more “Pythonic” manner and extending its capabilities to incorporate other essential components including reasoning, verbalization, normalization, projection, taxonomy, and more. Building on this module, DeepOnto offers a suite of tools, resources, and algorithms that support various ontology engineering tasks, such as ontology alignment and completion, by harnessing deep learning methods, primarily pre-trained LMs.		** '''Abstract:''' Integrating deep learning techniques, particularly language models (LMs), with knowledge representations like ontologies has raised widespread attention, urging the need for a platform that supports both paradigms. However, deep learning frameworks like PyTorch and Tensorflow are predominantly developed for Python programming, while widely-used ontology APIs, such as the OWL API and Jena, are primarily Java-based. To facilitate seamless integration of these frameworks and APIs, we present [https://krr-oxford.github.io/DeepOnto/ DeepOnto], a Python package designed for ontology engineering with deep learning. The package encompasses a core ontology processing module founded on the widely-recognized and reliable OWL API, encapsulating its fundamental features in a more “Pythonic” manner and extending its capabilities to incorporate other essential components including reasoning, verbalization, normalization, projection, taxonomy, and more. Building on this module, DeepOnto offers a suite of tools, resources, and algorithms that support various ontology engineering tasks, such as ontology alignment and completion, by harnessing deep learning methods, primarily pre-trained LMs.
			* [https://bit.ly/46VaEOu Slides]

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