Search (6 results, page 1 of 1)

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Abstract

Ahlgren, Jarneving, and. Rousseau review accepted procedures for author co-citation analysis first pointing out that since in the raw data matrix the row and column values are identical i,e, the co-citation count of two authors, there is no clear choice for diagonal values. They suggest the number of times an author has been co-cited with himself excluding self citation rather than the common treatment as zeros or as missing values. When the matrix is converted to a similarity matrix the normal procedure is to create a matrix of Pearson's r coefficients between data vectors. Ranking by r and by co-citation frequency and by intuition can easily yield three different orders. It would seem necessary that the adding of zeros to the matrix will not affect the value or the relative order of similarity measures but it is shown that this is not the case with Pearson's r. Using 913 bibliographic descriptions form the Web of Science of articles form JASIS and Scientometrics, authors names were extracted, edited and 12 information retrieval authors and 12 bibliometric authors each from the top 100 most cited were selected. Co-citation and r value (diagonal elements treated as missing) matrices were constructed, and then reconstructed in expanded form. Adding zeros can both change the r value and the ordering of the authors based upon that value. A chi-squared distance measure would not violate these requirements, nor would the cosine coefficient. It is also argued that co-citation data is ordinal data since there is no assurance of an absolute zero number of co-citations, and thus Pearson is not appropriate. The number of ties in co-citation data make the use of the Spearman rank order coefficient problematic.

Date

9. 7.2006 10:22:35

Type

a

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Abstract

In this large-scale contribution, we deal with the relationship between properties of cited references of Web of Science articles and the field normalized citation rate of these articles. Using nearly 1 million articles, and three classification systems with different levels of granularity, we study the effects of number of cited references, share of references covered by Web of Science, mean age of references and mean citation rate of references on field normalized citation rate. To expose the relationship between the predictor variables and the response variable, we use quantile regression. We found that a higher number of references, a higher share of references to publications within Web of Science and references to more recent publications correlate with citation impact. A correlation was observed even when normalization was done with a finely grained classification system. The predictor variables affected citation impact to a larger extent at higher quantile levels. Regarding the relative importance of the predictor variables, citation impact of the cited references was in general the least important variable. Number of cited references carried most of the importance for both low and medium quantile levels, but this importance was lessened at the highest considered level.

Type

a

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Abstract

This paper deals with Swedish full text retrieval and the problem of morphological variation of query terms in the document database. The effects of combination of indexing strategies with query terms on retrieval effectiveness were studied. Three of five tested combinations involved indexing strategies that used conflation, in the form of normalization. Further, two of these three combinations used indexing strategies that employed compound splitting. Normalization and compound splitting were performed by SWETWOL, a morphological analyzer for the Swedish language. A fourth combination attempted to group related terms by right hand truncation of query terms. The four combinations were compared to each other and to a baseline combination, where no attempt was made to counteract the problem of morphological variation of query terms in the document database. The five combinations were evaluated under six different user scenarios, where each scenario simulated a certain user type. The four alternative combinations outperformed the baseline, for each user scenario. The truncation combination had the best performance under each user scenario. The main conclusion of the paper is that normalization and right hand truncation (performed by a search expert) enhanced retrieval effectiveness in comparison to the baseline. The performance of the three combinations of indexing strategies with query terms based on normalization was not far below the performance of the truncation combination.

Type

a

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Abstract

Algorithmic classifications of research publications can be used to study many different aspects of the science system, such as the organization of science into fields, the growth of fields, interdisciplinarity, and emerging topics. How to label the classes in these classifications is a problem that has not been thoroughly addressed in the literature. In this study, we evaluate different approaches to label the classes in algorithmically constructed classifications of research publications. We focus on two important choices: the choice of (a) different bibliographic fields and (b) different approaches to weight the relevance of terms. To evaluate the different choices, we created two baselines: one based on the Medical Subject Headings in MEDLINE and another based on the Science-Metrix journal classification. We tested to what extent different approaches yield the desired labels for the classes in the two baselines. Based on our results, we recommend extracting terms from titles and keywords to label classes at high levels of granularity (e.g., topics). At low levels of granularity (e.g., disciplines) we recommend extracting terms from journal names and author addresses. We recommend the use of a new approach, term frequency to specificity ratio, to calculate the relevance of terms.

Type

a

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Abstract

This paper investigates two relatively new measures of retrieval effectiveness in relation to the problem of incomplete relevance data. The measures, Bpref and RankEff, which do not take into account documents that have not been relevance judged, are compared theoretically and experimentally. The experimental comparisons involve a third measure, the well-known mean uninterpolated average precision. The results indicate that RankEff is the most stable of the three measures when the amount of relevance data is reduced, with respect to system ranking and absolute values. In addition, RankEff has the lowest error-rate.

Type

a

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Abstract

The Discounted Cumulated Impact (DCI) index has recently been proposed for research evaluation. In the present work an earlier dataset by Cronin and Meho (2007) is reanalyzed, with the aim of exemplifying the salient features of the DCI index. We apply the index on, and compare our results to, the outcomes of the Cronin-Meho (2007) study. Both authors and their top publications are used as units of analysis, which suggests that, by adjusting the parameters of evaluation according to the needs of research evaluation, the DCI index delivers data on an author's (or publication's) lifetime impact or current impact at the time of evaluation on an author's (or publication's) capability of inviting citations from highly cited later publications as an indication of impact, and on the relative impact across a set of authors (or publications) over their lifetime or currently.

Type

a