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.

Social tagging or collaborative tagging has become a new trend in the organization, management, and discovery of digital information. The rapid growth of shared information mostly controlled by social tags poses a new challenge for social tag-based information organization and retrieval. A plausible approach for this challenge is linking social tags to a controlled vocabulary. As an introductory step for this approach, this study investigates ways of predicting relevant subject headings for resources from social tags assigned to the resources. The prediction of subject headings was measured by five different similarity measures: tf-idf, cosine-based similarity (CoS), Jaccard similarity (or Jaccard coefficient; JS), Mutual information (MI), and information radius (IRad). Their results were compared to those by professionals. The results show that a CoS measure based on top five social tags was most effective. Inclusions of more social tags only aggravate the performance. The performance of JS is comparable to the performance of CoS while tf-idf is comparable with up to 70% less than the best performance. MI and IRad have inferior performance compared to the other methods. This study demonstrates the application of the similarity measuring techniques to the prediction of correct Library of Congress subject headings.

A new collaborative approach in information organization and sharing has recently arisen, known as collaborative tagging or social indexing. A key element of collaborative tagging is the concept of collective intelligence (CI), which is a shared intelligence among all participants. This research investigates the phenomenon of social tagging in the context of CI with the aim to serve as a stepping‐stone towards the mining of truly valuable social tags for web resources. This study focuses on assessing and evaluating the degree of CI embedded in social tagging over time in terms of two‐parameter values, number of participants, and top frequency ranking window. Five different metrics were adopted and utilized for assessing the similarity between ranking lists: overlapList, overlapRank, Footrule, Fagin's measure, and the Inverse Rank measure. The result of this study demonstrates that a substantial degree of CI is most likely to be achieved when somewhere between the first 200 and 400 people have participated in tagging, and that a target degree of CI can be projected by controlling the two factors along with the selection of a similarity metric. The study also tests some experimental conditions for detecting social tags with high CI degree. The results of this study can be applicable to the study of filtering social tags based on CI; filtered social tags may be utilized for the metadata creation of tagged resources and possibly for the retrieval of tagged resources.

LC5, of 2-bromopropane was reexamined by using the OECD guideline in ICR mouse. The mice, 3 males and 3 females, were exposed to 2-bromopropane at five different concentrations for 4 h in inhalation chambers. The exposed animals were observed for 14 days. The animals which had survived and died were counted for the LC51 determination. The LC5" was calculated by using a dose-mortality curve at a 95 percent confidence level. The LC50 was 31,171 ppm, and the lowest lethal concentration (LLC) was lower than 29,528 ppm and the lethal concentration at 100% (LC,,,,)) was higher than 32,905 ppm.