Ontology engineering with Large Language Models

被引:6
|
作者
Mateiu, Patricia [1 ]
Groza, Adrian [1 ]
机构
[1] Tech Univ Cluj Napoca, Dept Comp Sci, Cluj Napoca 400114, Romania
来源
2023 25TH INTERNATIONAL SYMPOSIUM ON SYMBOLIC AND NUMERIC ALGORITHMS FOR SCIENTIFIC COMPUTING, SYNASC 2023 | 2023年
关键词
D O I
10.1109/SYNASC61333.2023.00038
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
We tackle the task of enriching ontologies by automatically translating natural language (NL) into Description Logic (DL). Since Large Language Models (LLMs) are the best tools for translations, we fine-tuned a GPT-3 model to convert NL into OWL Functional Syntax. For fune-tuning, we designed pairs of sentences in NL and the corresponding translations. This training pairs cover various aspects from ontology engineering: instances, class subsumption, domain and range of relations, object properties relationships, disjoint classes, complements, or cardinality restrictions. The resulted axioms are used to enrich an ontology, in a human supervised manner. The developed tool is publicly provided as a Protege plugin.
引用
收藏
页码:226 / 229
页数:4
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