Improving Ontology Requirements Engineering with OntoChat and Participatory Prompting
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866911026826444800 |
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| author | Zhao, Yihang Zhang, Bohui Hu, Xi Ouyang, Shuyin Kim, Jongmo Jain, Nitisha de Berardinis, Jacopo Meroño-Peñuela, Albert Simperl, Elena |
| author_facet | Zhao, Yihang Zhang, Bohui Hu, Xi Ouyang, Shuyin Kim, Jongmo Jain, Nitisha de Berardinis, Jacopo Meroño-Peñuela, Albert Simperl, Elena |
| contents | Past ontology requirements engineering (ORE) has primarily relied on manual methods, such as interviews and collaborative forums, to gather user requirements from domain experts, especially in large projects. Current OntoChat offers a framework for ORE that utilises large language models (LLMs) to streamline the process through four key functions: user story creation, competency question (CQ) extraction, CQ filtration and analysis, and ontology testing support. In OntoChat, users are expected to prompt the chatbot to generate user stories. However, preliminary evaluations revealed that they struggle to do this effectively. To address this issue, we experimented with a research method called participatory prompting, which involves researcher-mediated interactions to help users without deep knowledge of LLMs use the chatbot more effectively. This participatory prompting user study produces pre-defined prompt templates based on user queries, focusing on creating and refining personas, goals, scenarios, sample data, and data resources for user stories. These refined user stories will subsequently be converted into CQs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_15256 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Ontology Requirements Engineering with OntoChat and Participatory Prompting Zhao, Yihang Zhang, Bohui Hu, Xi Ouyang, Shuyin Kim, Jongmo Jain, Nitisha de Berardinis, Jacopo Meroño-Peñuela, Albert Simperl, Elena Human-Computer Interaction Artificial Intelligence Past ontology requirements engineering (ORE) has primarily relied on manual methods, such as interviews and collaborative forums, to gather user requirements from domain experts, especially in large projects. Current OntoChat offers a framework for ORE that utilises large language models (LLMs) to streamline the process through four key functions: user story creation, competency question (CQ) extraction, CQ filtration and analysis, and ontology testing support. In OntoChat, users are expected to prompt the chatbot to generate user stories. However, preliminary evaluations revealed that they struggle to do this effectively. To address this issue, we experimented with a research method called participatory prompting, which involves researcher-mediated interactions to help users without deep knowledge of LLMs use the chatbot more effectively. This participatory prompting user study produces pre-defined prompt templates based on user queries, focusing on creating and refining personas, goals, scenarios, sample data, and data resources for user stories. These refined user stories will subsequently be converted into CQs. |
| title | Improving Ontology Requirements Engineering with OntoChat and Participatory Prompting |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2408.15256 |