Inhaltszusammenfassung

This paper introduces a novel approach to dynamic ontology creation, leveraging Natural Language Processing (NLP) to automatically generate ontologies from textual descriptions and transform them into OWL (Web Ontology Language) and SWRL (Semantic Web Rule Language) formats. Unlike traditional manual ontology engineering, our system automates the extraction of structured knowledge from text, facilitating the development of complex ontological models in domains such as fitness and nutrition. T...This paper introduces a novel approach to dynamic ontology creation, leveraging Natural Language Processing (NLP) to automatically generate ontologies from textual descriptions and transform them into OWL (Web Ontology Language) and SWRL (Semantic Web Rule Language) formats. Unlike traditional manual ontology engineering, our system automates the extraction of structured knowledge from text, facilitating the development of complex ontological models in domains such as fitness and nutrition. The system supports automated reasoning, ensuring logical consistency and the inference of new facts based on rules. We evaluate the performance of our approach by comparing the ontologies generated from text with those created by a Semantic Web technologies expert and by ChatGPT. In a case study focused on personalized fitness planning, the system effectively models intricate relationships between exercise routines, nutritional requirements, and progression principles such as overload and time under tension. Results demonstrate that the proposed approach generates competitive, logically sound ontologies that capture complex constraints» weiterlesen» einklappen

Autoren

Klassifikation

DFG Fachgebiet:
Geographie

DDC Sachgruppe:
Informatik