Premium
A novel approach to citation normalization: A similarity‐based method for creating reference sets
Author(s) -
Colliander Cristian
Publication year - 2015
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23193
Subject(s) - terminology , normalization (sociology) , citation , computer science , similarity (geometry) , information retrieval , set (abstract data type) , data mining , mathematics , statistics , artificial intelligence , library science , linguistics , philosophy , sociology , anthropology , image (mathematics) , programming language
A similarity‐oriented approach for deriving reference values used in citation normalization is explored and contrasted with the dominant approach of utilizing database‐defined journal sets as a basis for deriving such values. In the similarity‐oriented approach, an assessed article's raw citation count is compared with a reference value that is derived from a reference set, which is constructed in such a way that articles in this set are estimated to address a subject matter similar to that of the assessed article. This estimation is based on second‐order similarity and utilizes a combination of 2 feature sets: bibliographic references and technical terminology. The contribution of an article in a given reference set to the reference value is dependent on its degree of similarity to the assessed article. It is shown that reference values calculated by the similarity‐oriented approach are considerably better at predicting the assessed articles' citation count compared to the reference values given by the journal‐set approach, thus significantly reducing the variability in the observed citation distribution that stems from the variability in the articles' addressed subject matter.