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Zhang, J.; An, L.; Tang, T.; Hong, Y.: Visual health subject directory analysis based on users' traversal activities (2009)
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- Abstract
- Concerns about health issues cover a wide spectrum. Consumer health information, which has become more available on the Internet, plays an extremely important role in addressing these concerns. A subject directory as an information organization and browsing mechanism is widely used in consumer health-related Websites. In this study we employed the information visualization technique Self-Organizing Map (SOM) in combination with a new U-matrix algorithm to analyze health subject clusters through a Web transaction log. An experimental study was conducted to test the proposed methods. The findings show that the clusters identified from the same cells based on path-length-1 outperformed both the clusters from the adjacent cells based on path-length-1 and the clusters from the same cells based on path-length-2 in the visual SOM display. The U-matrix method successfully distinguished the irrelevant subjects situated in the adjacent cells with different colors in the SOM display. The findings of this study lead to a better understanding of the health-related subject relationship from the users' traversal perspective.
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Wolfram, D.; Zhang, J.: ¬The influence of indexing practices and weighting algorithms on document spaces (2008)
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- Abstract
- Index modeling and computer simulation techniques are used to examine the influence of indexing frequency distributions, indexing exhaustivity distributions, and three weighting methods on hypothetical document spaces in a vector-based information retrieval (IR) system. The way documents are indexed plays an important role in retrieval. The authors demonstrate the influence of different indexing characteristics on document space density (DSD) changes and document space discriminative capacity for IR. Document environments that contain a relatively higher percentage of infrequently occurring terms provide lower density outcomes than do environments where a higher percentage of frequently occurring terms exists. Different indexing exhaustivity levels, however, have little influence on the document space densities. A weighting algorithm that favors higher weights for infrequently occurring terms results in the lowest overall document space densities, which allows documents to be more readily differentiated from one another. This in turn can positively influence IR. The authors also discuss the influence on outcomes using two methods of normalization of term weights (i.e., means and ranges) for the different weighting methods.
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Gao, J.; Zhang, J.: Clustered SVD strategies in latent semantic indexing (2005)
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- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
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Zhang, J.; Jastram, I.: ¬A study of the metadata creation behavior of different user groups on the Internet (2006)
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- Abstract
- Metadata is designed to improve information organization and information retrieval effectiveness and efficiency on the Internet. The way web publishers respond to metadata and the way they use it when publishing their web pages, however, is still a mystery. The authors of this paper aim to solve this mystery by defining different professional publisher groups, examining the behaviors of these user groups, and identifying the characteristics of their metadata use. This study will enhance the current understanding of metadata application behavior and provide evidence useful to researchers, web publishers, and search engine designers.
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Zhang, J.; Nguyen, T.: WebStar: a visualization model for hyperlink structures (2005)
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- Abstract
- The authors introduce an information visualization model, WebStar, for hyperlink-based information systems. Hyperlinks within a hyperlink-based document can be visualized in a two-dimensional visual space. All links are projected within a display sphere in the visual space. The relationship between a specified central document and its hyperlinked documents is visually presented in the visual space. In addition, users are able to define a group of subjects and to observe relevance between each subject and all hyperlinked documents via movement of that subject around the display sphere center. WebStar allows users to dynamically change an interest center during navigation. A retrieval mechanism is developed to control retrieved results in the visual space. Impact of movement of a subject on the visual document distribution is analyzed. An ambiguity problem caused by projection is discussed. Potential applications of this visualization model in information retrieval are included. Future research directions on the topic are addressed.
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Zhang, J.; Zeng, M.L.: ¬A new similarity measure for subject hierarchical structures (2014)
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- Date
- 8. 4.2015 16:22:13
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An, L.; Zhang, J.; Yu, C.: ¬The visual subject analysis of library and information science journals with self-organizing map (2011)
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- Abstract
- Academic journals play an important role in scientific communication. The effective organization of journals can help reveal the thematic contents of journals and thus make them more user-friendly. In this study, the Self-Organizing Map (SOM) technique was employed to visually analyze the 60 library and information science-related journals published from 2006 to 2008. The U-matrix by Ultsch (2003) was applied to categorize the journals into 19 clusters according to their subjects. Four journals were recommended to supplement library collections although they were not indexed by SCI/SSCI. A novel SOM display named Attribute Accumulation Matrix (AA-matrix) was proposed, and the results from this method show that they correlate significantly with the total occurrences of the subjects in the investigated journals. The AA-matrix was employed to identify the 86 salient subjects, which could be manually classified into 7 meaningful groups. A method of the Salient Attribute Projection was constructed to label the attribute characteristics of different clusters. Finally, the subject characteristics of the journals with high impact factors (IFs) were also addressed. The findings of this study can lead to a better understanding of the subject structure and characteristics of library/information-related journals.
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Zhang, J.; Wolfram, D.; Wang, P.: Analysis of query keywords of sports-related queries using visualization and clustering (2009)
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- Abstract
- The authors investigated 11 sports-related query keywords extracted from a public search engine query log to better understand sports-related information seeking on the Internet. After the query log contents were cleaned and query data were parsed, popular sports-related keywords were identified, along with frequently co-occurring query terms associated with the identified keywords. Relationships among each sports-related focus keyword and its related keywords were characterized and grouped using multidimensional scaling (MDS) in combination with traditional hierarchical clustering methods. The two approaches were synthesized in a visual context by highlighting the results of the hierarchical clustering analysis in the visual MDS configuration. Important events, people, subjects, merchandise, and so on related to a sport were illustrated, and relationships among the sports were analyzed. A small-scale comparative study of sports searches with and without term assistance was conducted. Searches that used search term assistance by relying on previous query term relationships outperformed the searches without the search term assistance. The findings of this study provide insights into sports information seeking behavior on the Internet. The developed method also may be applied to other query log subject areas.
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Zhang, J.; Zhao, Y.: ¬A user term visualization analysis based on a social question and answer log (2013)
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- Abstract
- The authors of this paper investigate terms of consumers' diabetes based on a log from the Yahoo!Answers social question and answers (Q&A) forum, ascertain characteristics and relationships among terms related to diabetes from the consumers' perspective, and reveal users' diabetes information seeking patterns. In this study, the log analysis method, data coding method, and visualization multiple-dimensional scaling analysis method were used for analysis. The visual analyses were conducted at two levels: terms analysis within a category and category analysis among the categories in the schema. The findings show that the average number of words per question was 128.63, the average number of sentences per question was 8.23, the average number of words per response was 254.83, and the average number of sentences per response was 16.01. There were 12 categories (Cause & Pathophysiology, Sign & Symptom, Diagnosis & Test, Organ & Body Part, Complication & Related Disease, Medication, Treatment, Education & Info Resource, Affect, Social & Culture, Lifestyle, and Nutrient) in the diabetes related schema which emerged from the data coding analysis. The analyses at the two levels show that terms and categories were clustered and patterns were revealed. Future research directions are also included.
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Zhang, J.; Mostafa, J.; Tripathy, H.: Information retrieval by semantic analysis and visualization of the concept space of D-Lib® magazine (2002)
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- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
