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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2511.07168 |
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| _version_ | 1866918193902125056 |
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| author | Tuccari, Giusy Giulia Giammei, Lorenzo Nuzzolese, Andrea Giovanni Mongiovì, Misael Zinilli, Antonio Poggi, Francesco |
| author_facet | Tuccari, Giusy Giulia Giammei, Lorenzo Nuzzolese, Andrea Giovanni Mongiovì, Misael Zinilli, Antonio Poggi, Francesco |
| contents | Author Name Disambiguation (AND) is a long-standing challenge in bibliometrics and scientometrics, as name ambiguity undermines the accuracy of bibliographic databases and the reliability of research evaluation. This study addresses the problem of cross-source disambiguation by linking academic career records from CercaUniversità, the official registry of Italian academics, with author profiles in Scopus. We introduce LEAD (LLM-enhanced Engine for Author Disambiguation), a novel hybrid framework that combines semantic features extracted through Large Language Models (LLMs) with structural evidence derived from co-authorship and citation networks. Using a gold standard of 606 ambiguous cases, we compare five methods: (i) Label Spreading on co-authorship networks; (ii) Bibliographic Coupling on citation networks; (iii) a standalone LLM-based approach; (iv) an LLM-enriched configuration; and (v) the proposed hybrid pipeline. LEAD achieves the best performance (F1 = 96.7%, accuracy = 95.7%) with lower computational cost than full LLM models. Bibliographic Coupling emerges as the fastest and strongest single-source method. These findings demonstrate that integrating semantic and structural signals within a selective hybrid strategy offers a robust and scalable solution to cross-database author identification. Beyond the Italian case, this work highlights the potential of hybrid LLM-based methods to improve data quality and reliability in scientometric analyses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_07168 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LEAD: LLM-enhanced Engine for Author Disambiguation Tuccari, Giusy Giulia Giammei, Lorenzo Nuzzolese, Andrea Giovanni Mongiovì, Misael Zinilli, Antonio Poggi, Francesco Digital Libraries Author Name Disambiguation (AND) is a long-standing challenge in bibliometrics and scientometrics, as name ambiguity undermines the accuracy of bibliographic databases and the reliability of research evaluation. This study addresses the problem of cross-source disambiguation by linking academic career records from CercaUniversità, the official registry of Italian academics, with author profiles in Scopus. We introduce LEAD (LLM-enhanced Engine for Author Disambiguation), a novel hybrid framework that combines semantic features extracted through Large Language Models (LLMs) with structural evidence derived from co-authorship and citation networks. Using a gold standard of 606 ambiguous cases, we compare five methods: (i) Label Spreading on co-authorship networks; (ii) Bibliographic Coupling on citation networks; (iii) a standalone LLM-based approach; (iv) an LLM-enriched configuration; and (v) the proposed hybrid pipeline. LEAD achieves the best performance (F1 = 96.7%, accuracy = 95.7%) with lower computational cost than full LLM models. Bibliographic Coupling emerges as the fastest and strongest single-source method. These findings demonstrate that integrating semantic and structural signals within a selective hybrid strategy offers a robust and scalable solution to cross-database author identification. Beyond the Italian case, this work highlights the potential of hybrid LLM-based methods to improve data quality and reliability in scientometric analyses. |
| title | LEAD: LLM-enhanced Engine for Author Disambiguation |
| topic | Digital Libraries |
| url | https://arxiv.org/abs/2511.07168 |