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NeOn-GPT: A Large Language Model-Powered Pipeline for Ontology Learning
Published on Jun 17, 202433 Views
We address the task of ontology learning by combining the structured NeOn methodology framework with Large Language Models (LLMs) for translating natural language domain descriptions into Turtle synt
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Chapter list
NeOn-GPT: A Large Language Model-Powered Pipeline for Ontology Learning00:00
Motivation00:44
Methodology01:11
Prompt Pipeline – Specification of ontology requirements02:29
Prompt Pipeline – Conceptual Modelling - 103:32
Prompt Pipeline – Conceptual Modelling - 203:45
Prompt Pipeline – Conceptual Modelling - 303:59
Prompt Pipeline – Conceptual Modelling - 404:25
Prompt Pipeline – Conceptual Modelling - 504:38
Prompt Pipeline – Conceptual Modelling - 605:03
Prompt Pipeline – Formal Modelling - 105:12
Prompt Pipeline – Formal Modelling - 206:03
Prompt Pipeline – Formal Modelling - 306:26
Prompt Pipeline – Formal Modelling - 406:48
Prompt Pipeline – Formal Modelling - 507:21
Prompt Pipeline – Formal Modelling - 607:49
Prompt Pipeline – Formal Modelling - 708:20
Workflow08:52
Workflow – Ontology Draft Generation08:58
Workflow – Syntax Validation09:21
Workflow – Consistency Check09:24
Workflow – Pitfall Resolution09:26
Results – Structural Assessment - 109:32
Results – Structural Assessment - 210:08
Results – Structural Assessment - 310:16
Results – Structural Assessment - 410:27
Results – Structural Assessment - 510:36
Results – Post-inference Step Assessment11:04
Conclusion - 111:36
Conclusion - 212:20
Prior Knowledge and Emerging Challenges12:42
References13:30
Thank You!13:34