Investigating Industry--Academia Collaboration in Artificial Intelligence: PDF-Based Bibliometric Analysis from Leading Conferences
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909820792078336 |
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| author | Yamauchi, Kazuhiro Katsurai, Marie |
| author_facet | Yamauchi, Kazuhiro Katsurai, Marie |
| contents | This study presents a bibliometric analysis of industry--academia collaboration in artificial intelligence (AI) research, focusing on papers from two major international conferences, AAAI and IJCAI, from 2010 to 2023. Most previous studies have relied on publishers and other databases to analyze bibliographic information. However, these databases have problems, such as missing articles and omitted metadata. Therefore, we adopted a novel approach to extract bibliographic information directly from the article PDFs: we examined 20,549 articles and identified the collaborative papers through a classification process of author affiliation. The analysis explores the temporal evolution of collaboration in AI, highlighting significant changes in collaboration patterns over the past decade. In particular, this study examines the role of key academic and industrial institutions in facilitating these collaborations, focusing on emerging global trends. Additionally, a content analysis using document classification was conducted to examine the type of first author in collaborative research articles and explore the potential differences between collaborative and noncollaborative research articles. The results showed that, in terms of publication, collaborations are mainly led by academia, but their content is not significantly different from that of others. The affiliation metadata are available at https://github.com/mm-doshisha/ICADL2024. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_01593 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Investigating Industry--Academia Collaboration in Artificial Intelligence: PDF-Based Bibliometric Analysis from Leading Conferences Yamauchi, Kazuhiro Katsurai, Marie Digital Libraries This study presents a bibliometric analysis of industry--academia collaboration in artificial intelligence (AI) research, focusing on papers from two major international conferences, AAAI and IJCAI, from 2010 to 2023. Most previous studies have relied on publishers and other databases to analyze bibliographic information. However, these databases have problems, such as missing articles and omitted metadata. Therefore, we adopted a novel approach to extract bibliographic information directly from the article PDFs: we examined 20,549 articles and identified the collaborative papers through a classification process of author affiliation. The analysis explores the temporal evolution of collaboration in AI, highlighting significant changes in collaboration patterns over the past decade. In particular, this study examines the role of key academic and industrial institutions in facilitating these collaborations, focusing on emerging global trends. Additionally, a content analysis using document classification was conducted to examine the type of first author in collaborative research articles and explore the potential differences between collaborative and noncollaborative research articles. The results showed that, in terms of publication, collaborations are mainly led by academia, but their content is not significantly different from that of others. The affiliation metadata are available at https://github.com/mm-doshisha/ICADL2024. |
| title | Investigating Industry--Academia Collaboration in Artificial Intelligence: PDF-Based Bibliometric Analysis from Leading Conferences |
| topic | Digital Libraries |
| url | https://arxiv.org/abs/2510.01593 |