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


{{:OntologySummit2024/Theme}}
== Description ==
 
The summit will survey current techniques that combine neural network machine learning with symbolic methods, especially methods based on ontologies and knowledge graphs.
 
Ontologies are representations of a knowledge domain. They define the concepts, relationships, properties, axioms and rules within that domain, providing a framework that enables a deep understanding of that subject area. Knowledge graphs are structured representations of semantic knowledge that are stored in a graph. Ontologies and knowledge graphs are used to enable machine reasoning and semantic understanding, allowing a system to draw inferences and to derive new information and relationships between entities.
 
Neural network and other machine learning models, such as LLMs, are trained on large corpora, learning the patterns and connections between words and images. Hence, although their “knowledge base” is broad, it is also sometimes incorrect and/or biased, and don't explicitly understand the semantics or relationships in that content.
 
Consequently, neural network and traditional AI techniques are complementary.  The Fall Series of the summit explored the similarities and distinctions between ontologies and LLMs, as well as how they can be used together.  The Main Summit Series will examine the more general topic of neuro-symbolic techniques, especially how one can leverage the complementary benefits of neural networks and of ontologies and knowledge graphs.


== Fall Series on Ontologies and Large Language Models: Related but Different ==
== Fall Series on Ontologies and Large Language Models: Related but Different ==


Fall Series Co-Chairs: [[AndreaWesterinen|Andrea Westerinen]] and [[MikeBennett|Mike Bennett]]
Fall Series Co-Chairs: [[AndreaWesterinen|Andrea Westerinen]] and [[MikeBennett|Mike Bennett]]
=== Description ===
Ontologies and Large Language Models (LLMs) such as OpenAI's GPT-4 represent two different but related concepts within the fields of artificial intelligence and knowledge representation.
Ontologies are representations of a knowledge domain. They define the concepts, relationships, properties, axioms and rules within that domain, providing a framework that enables a deep understanding of that subject area. Ontologies are used to enable machine reasoning and semantic understanding, allowing a system to draw inferences and to derive new information and relationships between entities.
On the other hand, LLMs are machine learning models that aim to generate human-like responses (including text and images) based on an input (“prompt”). They are trained on a large corpora of (mostly online) text, learning the patterns and connections between words and images. Hence, although their “knowledge base” is broad, it is also sometimes incorrect and/or biased. LLMs generate new content based on their training data, but don't explicitly understand the semantics or relationships in that content.
This mini-summit explores the similarities and distinctions between ontologies and LLMs, as well as how they can be used together.
In addition, the success of LLMs has generated much interest in AI and machine learning. This can be leveraged to promote the benefits of, and increase awareness of, the value of ontologies.


=== Schedule ===
=== Schedule ===
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Main Series Chair [[KenBaclawski|Ken Baclawski]]
Main Series Chair [[KenBaclawski|Ken Baclawski]]
=== Description ===
* Track A. Foundations and Architectures
* Track B. Large Language Models, Ontologies and Knowedge Graphs
* Track C. Applications
* Track D. Risks and Ethics


=== Schedule ===
=== Schedule ===

Revision as of 08:52, 16 February 2024

Ontology Summit 2024

The 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.

Purpose

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 2024 Communiqué, with expanded supporting material provided on the web and in journal articles.

Process and Deliverables

Similar to our last seventeen summits, this Ontology Summit 2024 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. This year began with a Fall Series in October and November; the main summit will begin in February.

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

Description

The summit will survey current techniques that combine neural network machine learning with symbolic methods, especially methods based on ontologies and knowledge graphs.

Ontologies are representations of a knowledge domain. They define the concepts, relationships, properties, axioms and rules within that domain, providing a framework that enables a deep understanding of that subject area. Knowledge graphs are structured representations of semantic knowledge that are stored in a graph. Ontologies and knowledge graphs are used to enable machine reasoning and semantic understanding, allowing a system to draw inferences and to derive new information and relationships between entities.

Neural network and other machine learning models, such as LLMs, are trained on large corpora, learning the patterns and connections between words and images. Hence, although their “knowledge base” is broad, it is also sometimes incorrect and/or biased, and don't explicitly understand the semantics or relationships in that content.

Consequently, neural network and traditional AI techniques are complementary. The Fall Series of the summit explored the similarities and distinctions between ontologies and LLMs, as well as how they can be used together. The Main Summit Series will examine the more general topic of neuro-symbolic techniques, especially how one can leverage the complementary benefits of neural networks and of ontologies and knowledge graphs.

Fall Series on Ontologies and Large Language Models: Related but Different

Fall Series Co-Chairs: Andrea Westerinen and Mike Bennett

Description

Ontologies and Large Language Models (LLMs) such as OpenAI's GPT-4 represent two different but related concepts within the fields of artificial intelligence and knowledge representation.

Ontologies are representations of a knowledge domain. They define the concepts, relationships, properties, axioms and rules within that domain, providing a framework that enables a deep understanding of that subject area. Ontologies are used to enable machine reasoning and semantic understanding, allowing a system to draw inferences and to derive new information and relationships between entities.

On the other hand, LLMs are machine learning models that aim to generate human-like responses (including text and images) based on an input (“prompt”). They are trained on a large corpora of (mostly online) text, learning the patterns and connections between words and images. Hence, although their “knowledge base” is broad, it is also sometimes incorrect and/or biased. LLMs generate new content based on their training data, but don't explicitly understand the semantics or relationships in that content. This mini-summit explores the similarities and distinctions between ontologies and LLMs, as well as how they can be used together. In addition, the success of LLMs has generated much interest in AI and machine learning. This can be leveraged to promote the benefits of, and increase awareness of, the value of ontologies.

Schedule

Main Series on Neuro-Symbolic Techniques for and with Ontologies and Knowledge Graphs

Main Series Chair Ken Baclawski

Description

  • Track A. Foundations and Architectures
  • Track B. Large Language Models, Ontologies and Knowedge Graphs
  • Track C. Applications
  • Track D. Risks and Ethics

Schedule

Resources

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