Purpose -The purpose of this paper is to investigate the linking of a folksonomy (user vocabulary) and LCSH (controlled vocabulary) on the basis of word matching, for the potential use of LCSH in bringing order to folksonomies. Design/methodology/approach -A selected sample of a folksonomy from a popular collaborative tagging system, Delicious, was word-matched with LCSH. LCSH was transformed into a tree structure called an LCSH tree for the matching. A close examination was conducted on the characteristics of folksonomies, the overlap of folksonomies with LCSH, and the distribution of folksonomies over the LCSH tree. Findings -The experimental results showed that the total proportion of tags being matched with LC subject headings constituted approximately two-thirds of all tags involved, with an additional 10 percent of the remaining tags having potential matches. A number of barriers for the linking as well as two areas in need of improving the matching are identified and described. Three important tag distribution patterns over the LCSH tree were identified and supported: skewedness, multifacet, and Zipfian-pattern.Research limitations/implications -The results of the study can be adopted for the development of innovative methods of mapping between folksonomy and LCSH, which directly contributes to effective access and retrieval of tagged web resources and to the integration of multiple information repositories based on the two vocabularies. Practical implications -The linking of controlled vocabularies can be applicable to enhance information retrieval capability within collaborative tagging systems as well as across various tagging system information depositories and bibliographic databases. Originality/value -This is among frontier works that examines the potential of linking a folksonomy, extracted from a collaborative tagging system, to an authority-maintained subject heading system. It provides exploratory data to support further advanced mapping methods for linking the two vocabularies.
The authors report the findings of a study that analyzes and compares the query logs of PsycINFO for psychology and the two history databases of ABC-Clio: Historical Abstracts and America: History and Life to establish the sociological nature of information need, searching, and seeking in history versus psychology. Two problems are addressed: (a) What level of query log analysis-by individual query terms, by co-occurrence of word pairs, or by multiword terms (MWTs)-best serves as data for categorizing the queries to these two subject-bound databases; and (b) how can the differences in the nature of the queries to history versus psychology databases aid in our understanding of user search behavior and the information needs of their respective users. The authors conclude that MWTs provide the most effective snapshot of user searching behavior for query categorization. The MWTs to ABC-Clio indicate specific instances of historical events, people, and regions, whereas the MWTs to PsycINFO indicate concepts roughly equivalent to descriptors used by PsycINFO's own classification scheme. The average length of queries is 3.16 terms for PsycINFO and 3.42 for ABC-Clio, which breaks from findings for other reference and scholarly search engine studies, bringing query length closer in line to findings for general Web search engines like Excite.
Subcontracting workers were found to have a higher risk of work-related diseases and a higher absenteeism rate than parent firm workers. Our study highlights the need to protect and improve the occupational health and safety of subcontractor employees.
The purpose of the study is to test the application of the hidden Markov model (HMM) using prior knowledge in medical text classification (TC). HMM has been applied to a wide range of applications in information processing, but not so much in TC applications. The Medical Subject Heading (MeSH) is utilized for prior knowledge in the model. A prototype for an HMM-based TC model is designed, and an experimental model based on the prototype is implemented so as to categorize medical documents into MeSH. A subset of OHSUMED is used for the experiments. Our results show that the performance of our model is comparable to those reported in the literature.
BackgroundTo investigate the relationship between musculoskeletal disorders and comorbid health problems, including depression/anxiety disorder, insomnia/sleep disorder, fatigue, and injury by accident, and to determine whether certain physical and psychological factors reduce comorbid health problems.MethodsIn total, 29,711 employees were selected from respondents of the Third Korean Working Conditions Survey and categorized into two groups: Musculoskeletal Complaints or Musculoskeletal Sickness Absence. Four self-reported health indicators (overall fatigue, depression/anxiety, insomnia/sleep disorder, and injury by accident) were selected as outcomes, based on their high prevalence in Korea. We used multiple logistic regression analysis to determine the relationship between comorbid health problems, musculoskeletal complaints, and sickness absence.ResultsThe prevalence of musculoskeletal complaints and musculoskeletal sickness absence due to muscular pain was 32.26% and 0.59%, respectively. Compared to the reference group, depression/anxiety disorder and overall fatigue were 5.2–6.1 times more prevalent in the Musculoskeletal Complaints Group and insomnia/sleep disorder and injury by accident were 7.6–11.0 times more prevalent in the Sickness Absence Group. When adjusted for individual and work-related physical factors, prevalence of all four comorbid health problems were slightly decreased in both groups.ConclusionIncreases in overall fatigue and depression/anxiety disorder were observed in the Musculoskeletal Complaints Group, while increases in insomnia/sleep disorder and injury by accident were observed in the Sickness Absence Group. For management of musculoskeletal complaints and sickness absence in the workplace, differences in health problems between employees with musculoskeletal complaints and those with sickness absence as well as the physical and psychological risk factors should be considered.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.