The unification of institutional addresses applying parametrized finite-state graphs (P-FSG)

Gálvez, Carmen; Anegón, Félix de Moya

doi:10.1007/s11192-006-0156-3

Cited by 26 publications

(17 citation statements)

References 44 publications

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“…The final publication is available at Springer via http://dx.doi.org/10.1007/s11192-013-0965-0 13 insisted on the need to process manually the addresses when they do not fit into the expected structure, requiring subsequent adjustment Moya-Anegón 2006 and2007). In this article, we offer a method for the detailed identification and normalisation of addresses that does not require subsequent adjustments.…”

Section: Discussionmentioning

confidence: 99%

“…In the case of institutions, the growing amount of documents explains the increasing importance of standardisation and the need of some kind of automatic process to ensure the quality of the results in a cost-efficient way (Perianes-Rodríguez et al 2009). Moya-Anegón (2006 and2007) develop a methodology based on the finite state transducers taking the advantage of the particular segmentation of databases. Their results are reasonable, but the process is tested with University records and this institutional sector is the best normalised.…”

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confidence: 99%

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The automatic normalisation challenge: detailed addresses identification

2013

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The correct attribution of scientific publications to their true owners is extremely important, considering the detailed evaluation processes and the future investments based upon them. This attribution is a hard job for bibliometricians because of the increasing amount of documents and the raise of collaboration. Nevertheless, there is no published work with a comprehensive solution of the problem. This article introduces a procedure for the detailed identification and normalisation of addresses to facilitate the correct allocation of the scientific production included in databases. Thanks to our long experience in the manual normalisation of addresses, we have created and maintained various master lists. We have already developed an application to detect institutional sectors (issued in a previous paper) and now we analyse the details of particular institutions, taking advantage of our master tables. To test our methodology we have implemented it in a Spanish data set already manually codified (95,314 unique addresses included in the year 2008 on the Web of Science databases). This data was analysed with a full text search (FTS) against our master lists, giving optional codes for each address and choosing which one could be automatically encoded and which one should be reviewed manually. The results of the implementation, comparing the automatic versus manual codes, showed 87% automatically codified records with 1.9% of error. We should review manually only 13%. Finally, we applied the Wilcoxon non-parametric test to show the validity of the methodology, comparing detailed codes of centres already encoded with the automatically encoded ones, and concluding that their distribution was similar with a significance of 0.078.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

The automatic normalisation challenge: detailed addresses identification

2013

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“…These studies did not aim to determine spelling mistakes for organization names (Galvez & Moya-Anegón 2006a;Galvez & Moya-Anegón 2007a). On the other hand, the study on standardizing author names was designed to find different versions of an author name (Galvez & Moya-Anegón 2007b).…”

Section: Previous Studies About Data Accuracy In Citation Indexesmentioning

confidence: 99%

Standardization problem of author affiliations in citation indexes

2013

Scientometrics

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Academic effectiveness of universities is measured with the number of publications and citations.However, accessing all the publications of a university reveals a challenge related to the mistakes and standardization problems in citation indexes. The main aim of this study is to seek a solution for the unstandardized addresses and publication loss of universities with regard to this problem. To achieve this, all Turkey-addressed publications published between 1928 and 2009 were analyzed and evaluated deeply. The results show that the main mistakes are based on character or spelling, indexing and translation errors.Mentioned errors effect international visibility of universities negatively, make bibliometric studies based on affiliations unreliable and reveal incorrect university rankings. To inhibit these negative effects, an algorithm was created with finite state technique by using Nooj Transducer. Frequently used 47 different affiliation variations for Hacettepe University apart from "Hacettepe Univ" and "Univ Hacettepe" were determined by the help of finite state grammar graphs. In conclusion, this study presents some reasons of the inconsistencies for university rankings. It is suggested that, mistakes and standardization issues should be considered by librarians, authors, editors, policy makers and managers to be able to solve these problems.

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“…To obviate these difficulties, we used ad hoc software that avoids homonymy by combining author and institution and synonymy by combining author and paper, while (Gálvez & Moya-Anegón, 2006;Gálvez & Moya-Anegón, 2007b) corrected the lack of precision in institutional denominations.…”

Section: Data Refinementmentioning

confidence: 99%

Synthetic hybrid indicators based on scientific collaboration to quantify and evaluate individual research results

Perianes‐Rodríguez

Chinchilla‐Rodríguez²,

Vargas-Quesada

et al. 2009

Journal of Informetrics

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a b s t r a c tGovernmental initiatives around scientific policy have progressively raised collaboration to priority status. In this context, a need has arisen to broaden the traditional approach to the analysis and study of research results by descending to the group or even the individual scale and supplementing the output-, productivity-, visibility-and impact-based focus with new measures that emphasize collaboration from the vantage of structural analysis. To this end, the present paper proposes new hybrid indicators for the analysis and evaluation of individual research results, popularity and prestige, that combine bibliometric and structural aspects. A case study was conducted of the nine most productive departments in Carlos III University of Madrid. The findings showed hybridization to be a tool sensitive to traditional indicators, but also to the new demands of modern science as a self-organized system of interaction among individuals, furnishing information on researchers' environments and the behaviour and attitudes adopted within those environments.

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The unification of institutional addresses applying parametrized finite-state graphs (P-FSG)

Cited by 26 publications

References 44 publications

The automatic normalisation challenge: detailed addresses identification

The automatic normalisation challenge: detailed addresses identification

Standardization problem of author affiliations in citation indexes

Synthetic hybrid indicators based on scientific collaboration to quantify and evaluate individual research results

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