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. 2013 Jan 9:13:8.
doi: 10.1186/1472-6947-13-8.

A study on PubMed search tag usage pattern: association rule mining of a full-day PubMed query log

Abu Saleh Mohammad Mosa  1 , Illhoi Yoo
Affiliations

A study on PubMed search tag usage pattern: association rule mining of a full-day PubMed query log

Abu Saleh Mohammad Mosa et al. BMC Med Inform Decis Mak. .

Abstract

Background: The 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.

Methods: A 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.

Results: The 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.

Conclusions: The 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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Figures

Figure 1
Figure 1
Sample PubMed query log. This figure presents a total of 10 sample queries from the PubMed query log file that was used in this study.
Figure 2
Figure 2
Data pre-processing steps. This figure demonstrates the data cleaning and pre-processing steps for association mining analysis.
Figure 3
Figure 3
Number of users using a different number of distinct tags per number of queries. This histogram presents the total number of users using a different number of distinct search field tags per number of queries.
Figure 4
Figure 4
Number of queries containing different number of distinct tags. This histogram presents the total number of queries containing different number of distinct tags.
Figure 5
Figure 5
Search field tag frequency in queries. This histogram shows for each of 37 search field tags the total number of queries containing either the tag only or the tag and other tag(s).
Figure 6
Figure 6
Plot of tag count against term count: (a) Scatter Plot, and (b) Boxplot. This figure includes a scatter plot diagram and a boxplot diagram presenting the number of search tags (X) against the total number of search terms (Y) used in a query. Also, a linear regression line is superimposed on both of the plot presented by a solid line.
Figure 7
Figure 7
Visualization of association rules consisting of only informational tags. This figure visualizes 16 association rules presented in Table 6 consisting of six informational tags (i.e. [LANGUAGE], [MESH TERMS], [PUBLICATION TYPE], [SUBSET], [MESH MAJOR TOPIC], and [TEXT WORD]).
Figure 8
Figure 8
Visualization of association rules consisting of only navigational tags. This figure visualizes 10 association rules presented in Table 7 consisting of six navigational tags (i.e. [PUBLICATION DATE], [JOURNAL], [PAGINATION], [ISSUE], [VOLUME], and [AUTHOR]).
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