In recent years a huge amount of publications and scientific reports has become available through digital libraries and online databases. Digital libraries commonly provide advanced search interfaces, through which researchers can find and explore the most related scientific studies. Even though the publications of a single author can be easily retrieved and explored, understanding how authors have collaborated with each other on specific research topics and to what extent their collaboration have been fruitful is, in general, a challenging task. This paper proposes a new pattern-based approach to analyzing the correlations among the authors of most influential research studies. To this purpose, it analyzes publication data retrieved from digital libraries and online databases by means of an itemset-based data mining algorithm. It automatically extracts patterns representing the most relevant collaborations among authors on specific research topics. Patterns are evaluated and ranked according to the number of citations received by the corresponding publications. The proposed approach was validated in a real case study, i.e., the analysis of scientific literature on genomics. Specifically, we first analyzed scientific studies on genomics acquired from the OMIM database to discover correlations between authors and genes or genetic disorders. Then, the reliability of the discovered patterns was assessed using the PubMed search engine. The results show that, for the majority of the mined patterns, the most influential (top ranked) studies retrieved by performing author-driven PubMed queries range over the same gene/genetic disorder indicated by the top ranked pattern.

Identifying collaborations among researchers: a pattern-based approach / Cagliero, Luca; Garza, Paolo; Kavoosifar, MOHAMMAD REZA; Baralis, ELENA MARIA. - ELETTRONICO. - 1888:(2017), pp. 56-68. ((Intervento presentato al convegno 2nd Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2017), co-located with the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2017) tenutosi a Tokyo (Japan) nel August 11, 2017.

Identifying collaborations among researchers: a pattern-based approach

CAGLIERO, LUCA;GARZA, PAOLO;KAVOOSIFAR, MOHAMMAD REZA;BARALIS, ELENA MARIA
2017

Abstract

In recent years a huge amount of publications and scientific reports has become available through digital libraries and online databases. Digital libraries commonly provide advanced search interfaces, through which researchers can find and explore the most related scientific studies. Even though the publications of a single author can be easily retrieved and explored, understanding how authors have collaborated with each other on specific research topics and to what extent their collaboration have been fruitful is, in general, a challenging task. This paper proposes a new pattern-based approach to analyzing the correlations among the authors of most influential research studies. To this purpose, it analyzes publication data retrieved from digital libraries and online databases by means of an itemset-based data mining algorithm. It automatically extracts patterns representing the most relevant collaborations among authors on specific research topics. Patterns are evaluated and ranked according to the number of citations received by the corresponding publications. The proposed approach was validated in a real case study, i.e., the analysis of scientific literature on genomics. Specifically, we first analyzed scientific studies on genomics acquired from the OMIM database to discover correlations between authors and genes or genetic disorders. Then, the reliability of the discovered patterns was assessed using the PubMed search engine. The results show that, for the majority of the mined patterns, the most influential (top ranked) studies retrieved by performing author-driven PubMed queries range over the same gene/genetic disorder indicated by the top ranked pattern.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2680605
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