[3Win5] Poster session 3

[3Win5-44] Utilizing Scholarly Papers for Metadata Generation of Research Data

〇Yu Watanabe¹, Koichiro Ito¹, Shigeki Matsubara^1,2 (1.Graduate School of Informatics, Nagoya University, 2.Information Technology Center, Nagoya University)

Keywords:open science, information extraction, LLM

To promote open science, publishing and sharing research data is recommended. To enhance the accessibility of research data, metadata about research data should be assigned; however, manually assigning metadata is costly. On the other hand, scholarly papers describe information about research data, i.e., meta-information, which could potentially be utilized for metadata generation. This paper explores the feasibility of obtaining meta-information by utilizing scholarly papers. We implemented two methods to obtain meta-information from text surrounding URLs that cite research data, and evaluate their performance. The first method extracts meta-information from input texts, and the second method classifies input texts. Both methods are performed using a LLM. The experimental results indicate that meta-information extraction using an LLM has low performance. By contrast, in research data classification, we confirm that the performance of our method improves by providing classification examples.