Identifying related journals through log analysis

Lu, Zhiyong; Xie, Natalie; Wilbur, W. John

doi:10.1093/bioinformatics/btp529

Cited by 14 publications

(12 citation statements)

References 5 publications

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“…We can utilize them to validate the results of the content-similarity-based approach, as journals are manually We then analyze the results of the content-similarity-based approach with respect to the results of usage-based approach. We re-run the algorithm of the usage-based approach developed earlier (Lu, Xie, & Wilbur, 2009). Table 2 shows the average scores of CC, KTCC, F1 and NDCG with two different truncated positions 5 (NDCG@5) and 20 (NDCG@20) for journals with different number of published papers.…”

Section: Resultsmentioning

confidence: 99%

“…Journal of Data and Information Science retrievals in the form of MEDLINE records that were examined by the user during that session (Lu, Xie, & Wilbur, 2009). Let A represent a journal and t A (s i ) denote the number of clicks through events that represent articles from journal A:…”

Section: Research Papermentioning

confidence: 99%

“…Another journal clustering approach based on article usage information has been proposed (Lu, Xie, & Wilbur, 2009). The idea is based on the hypothesis that if articles in two journals are often read by the same set of users, the two journals are likely to be related.…”

Section: Introductionmentioning

confidence: 99%

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Mining Related Articles for Automatic Journal Cataloging

Mao

2016

Journal of Data and Information Science

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Purpose: This paper is an investigation of the effectiveness of the method of clustering biomedical journals through mining the content similarity of journal articles.Design/methodology/approach: 3,265 journals in PubMed are analyzed based on article content similarity and Web usage, respectively. Comparisons of the two analysis approaches and a citation-based approach are given. Findings:Our results suggest that article content similarity is useful for clustering biomedical journals, and the content-similarity-based journal clustering method is more robust and less subject to human factors compared with the usage-based approach and the citation-based approach.Research limitations: Our paper currently focuses on clustering journals in the biomedical domain because there are a large volume of freely available resources such as PubMed and MeSH in this field. Further investigation is needed to improve this approach to fit journals in other domains. Practical implications:Our results show that it is feasible to catalog biomedical journals by mining the article content similarity. This work is also significant in serving practical needs in research portfolio analysis. Originality/value:To the best of our knowledge, we are among the first to report on clustering journals in the biomedical field through mining the article content similarity. This method can be integrated with existing approaches to create a new paradigm for future studies of journal clustering.

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Section: Resultsmentioning

confidence: 99%

Section: Research Papermentioning

confidence: 99%

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Mining Related Articles for Automatic Journal Cataloging

Mao

2016

Journal of Data and Information Science

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“…Profiling journals, and measuring how different journals relate to each other, are only two of the aspects that have been examined. For example, journals can be characterized and related according to their citation patterns [1] – [2] , topical similarity [3] , keyword similarity [4] , user behavior during PubMed weblog sessions [5] and even the degree of interlocking editorships [6] . Cordier [7] proposed relating journals according to the number of authors that they share, though this idea has not yet been implemented nor tested.…”

Section: Introductionmentioning

confidence: 99%

Three Journal Similarity Metrics and Their Application to Biomedical Journals

D’Souza

Smalheiser

2014

PLoS ONE

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In the present paper, we have created several novel journal similarity metrics. The MeSH odds ratio measures the topical similarity of any pair of journals, based on the major MeSH headings assigned to articles in MEDLINE. The second metric employed the 2009 Author-ity author name disambiguation dataset as a gold standard for estimating the author odds ratio. This gives a straightforward, intuitive answer to the question: Given two articles in PubMed that share the same author name (lastname, first initial), how does knowing only the identity of the journals (in which the articles were published) predict the relative likelihood that they are written by the same person vs. different persons? The article pair odds ratio detects the tendency of authors to publish repeatedly in the same journal, as well as in specific pairs of journals. The metrics can be applied not only to estimate the similarity of a pair of journals, but to provide novel profiles of individual journals as well. For example, for each journal, one can define the MeSH cloud as the number of other journals that are topically more similar to it than expected by chance, and the author cloud as the number of other journals that share more authors than expected by chance. These metrics for journal pairs and individual journals have been provided in the form of public datasets that can be readily studied and utilized by others.

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“…Both of the studies [34,35] reported semantic categorization of PubMed queries, proportion of users against number of queries, proportion of queries against number of terms in a query, and many other interesting statistical metrics. This month-long dataset has also been analyzed for: (1) identifying the journals that are related to user search queries [39] and (2) creating a database of queries that is used for automatically producing query suggestions in response to the original user’s input [40]. Both of the datasets used in Herskovic et al (2007) [29] and Doğan et al (2009) [34] are publicly available from the NLM.…”

Section: Introductionmentioning

confidence: 99%

A Study on Pubmed Search Tag Usage Pattern: Association Rule Mining of a Full-day Pubmed Query Log

Mosa

Yoo

2013

BMC Med Inform Decis Mak

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BackgroundThe practice of evidence-based medicine requires efficient biomedical literature search such as PubMed/MEDLINE. Retrieval performance relies highly on the efficient use of search field tags. The purpose of this study was to analyze PubMed log data in order to understand the usage pattern of search tags by the end user in PubMed/MEDLINE search.MethodsA PubMed query log file was obtained from the National Library of Medicine containing anonymous user identification, timestamp, and query text. Inconsistent records were removed from the dataset and the search tags were extracted from the query texts. A total of 2,917,159 queries were selected for this study issued by a total of 613,061 users. The analysis of frequent co-occurrences and usage patterns of the search tags was conducted using an association mining algorithm.ResultsThe percentage of search tag usage was low (11.38% of the total queries) and only 2.95% of queries contained two or more tags. Three out of four users used no search tag and about two-third of them issued less than four queries. Among the queries containing at least one tagged search term, the average number of search tags was almost half of the number of total search terms. Navigational search tags are more frequently used than informational search tags. While no strong association was observed between informational and navigational tags, six (out of 19) informational tags and six (out of 29) navigational tags showed strong associations in PubMed searches.ConclusionsThe low percentage of search tag usage implies that PubMed/MEDLINE users do not utilize the features of PubMed/MEDLINE widely or they are not aware of such features or solely depend on the high recall focused query translation by the PubMed’s Automatic Term Mapping. The users need further education and interactive search application for effective use of the search tags in order to fulfill their biomedical information needs from PubMed/MEDLINE.

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Identifying related journals through log analysis

Cited by 14 publications

References 5 publications

Mining Related Articles for Automatic Journal Cataloging

Mining Related Articles for Automatic Journal Cataloging

Three Journal Similarity Metrics and Their Application to Biomedical Journals

A Study on Pubmed Search Tag Usage Pattern: Association Rule Mining of a Full-day Pubmed Query Log

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