-
Zhang, J.; Dimitroff, A.: Internet search engines' response to Metadata Dublin Core implementation (2005)
0.00
0.0022682408 = product of:
0.036291853 = sum of:
0.036291853 = weight(_text_:internet in 4652) [ClassicSimilarity], result of:
0.036291853 = score(doc=4652,freq=2.0), product of:
0.07947389 = queryWeight, product of:
2.9522398 = idf(docFreq=6276, maxDocs=44218)
0.026919862 = queryNorm
0.45665127 = fieldWeight in 4652, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
2.9522398 = idf(docFreq=6276, maxDocs=44218)
0.109375 = fieldNorm(doc=4652)
0.0625 = coord(1/16)
-
Zhang, J.; Jastram, I.: ¬A study of the metadata creation behavior of different user groups on the Internet (2006)
0.00
0.0016038885 = product of:
0.025662215 = sum of:
0.025662215 = weight(_text_:internet in 982) [ClassicSimilarity], result of:
0.025662215 = score(doc=982,freq=4.0), product of:
0.07947389 = queryWeight, product of:
2.9522398 = idf(docFreq=6276, maxDocs=44218)
0.026919862 = queryNorm
0.32290122 = fieldWeight in 982, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
2.9522398 = idf(docFreq=6276, maxDocs=44218)
0.0546875 = fieldNorm(doc=982)
0.0625 = coord(1/16)
- 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.
-
Zhang, J.; Wolfram, D.; Wang, P.: Analysis of query keywords of sports-related queries using visualization and clustering (2009)
0.00
0.0011456347 = product of:
0.018330155 = sum of:
0.018330155 = weight(_text_:internet in 2947) [ClassicSimilarity], result of:
0.018330155 = score(doc=2947,freq=4.0), product of:
0.07947389 = queryWeight, product of:
2.9522398 = idf(docFreq=6276, maxDocs=44218)
0.026919862 = queryNorm
0.23064373 = fieldWeight in 2947, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
2.9522398 = idf(docFreq=6276, maxDocs=44218)
0.0390625 = fieldNorm(doc=2947)
0.0625 = coord(1/16)
- 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.
-
Zhang, J.; An, L.; Tang, T.; Hong, Y.: Visual health subject directory analysis based on users' traversal activities (2009)
0.00
9.721032E-4 = product of:
0.015553651 = sum of:
0.015553651 = weight(_text_:internet in 3112) [ClassicSimilarity], result of:
0.015553651 = score(doc=3112,freq=2.0), product of:
0.07947389 = queryWeight, product of:
2.9522398 = idf(docFreq=6276, maxDocs=44218)
0.026919862 = queryNorm
0.1957077 = fieldWeight in 3112, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
2.9522398 = idf(docFreq=6276, maxDocs=44218)
0.046875 = fieldNorm(doc=3112)
0.0625 = coord(1/16)
- 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.
-
Li, D.; Luo, Z.; Ding, Y.; Tang, J.; Sun, G.G.-Z.; Dai, X.; Du, J.; Zhang, J.; Kong, S.: User-level microblogging recommendation incorporating social influence (2017)
0.00
8.100861E-4 = product of:
0.012961377 = sum of:
0.012961377 = weight(_text_:internet in 3426) [ClassicSimilarity], result of:
0.012961377 = score(doc=3426,freq=2.0), product of:
0.07947389 = queryWeight, product of:
2.9522398 = idf(docFreq=6276, maxDocs=44218)
0.026919862 = queryNorm
0.16308975 = fieldWeight in 3426, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
2.9522398 = idf(docFreq=6276, maxDocs=44218)
0.0390625 = fieldNorm(doc=3426)
0.0625 = coord(1/16)
- Theme
- Internet
-
Geng, Q.; Townley, C.; Huang, K.; Zhang, J.: Comparative knowledge management : a pilot study of Chinese and American universities (2005)
0.00
5.527352E-4 = product of:
0.008843763 = sum of:
0.008843763 = product of:
0.026531288 = sum of:
0.026531288 = weight(_text_:k in 3876) [ClassicSimilarity], result of:
0.026531288 = score(doc=3876,freq=2.0), product of:
0.09609793 = queryWeight, product of:
3.569778 = idf(docFreq=3384, maxDocs=44218)
0.026919862 = queryNorm
0.27608594 = fieldWeight in 3876, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.569778 = idf(docFreq=3384, maxDocs=44218)
0.0546875 = fieldNorm(doc=3876)
0.33333334 = coord(1/3)
0.0625 = coord(1/16)
-
Zhang, J.; Wolfram, D.; Wang, P.; Hong, Y.; Gillis, R.: Visualization of health-subject analysis based on query term co-occurrences (2008)
0.00
3.948108E-4 = product of:
0.006316973 = sum of:
0.006316973 = product of:
0.018950919 = sum of:
0.018950919 = weight(_text_:k in 2376) [ClassicSimilarity], result of:
0.018950919 = score(doc=2376,freq=2.0), product of:
0.09609793 = queryWeight, product of:
3.569778 = idf(docFreq=3384, maxDocs=44218)
0.026919862 = queryNorm
0.19720423 = fieldWeight in 2376, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.569778 = idf(docFreq=3384, maxDocs=44218)
0.0390625 = fieldNorm(doc=2376)
0.33333334 = coord(1/3)
0.0625 = coord(1/16)
- Abstract
- A multidimensional-scaling approach is used to analyze frequently used medical-topic terms in queries submitted to a Web-based consumer health information system. Based on a year-long transaction log file, five medical focus keywords (stomach, hip, stroke, depression, and cholesterol) and their co-occurring query terms are analyzed. An overlap-coefficient similarity measure and a conversion measure are used to calculate the proximity of terms to one another based on their co-occurrences in queries. The impact of the dimensionality of the visual configuration, the cutoff point of term co-occurrence for inclusion in the analysis, and the Minkowski metric power k on the stress value are discussed. A visual clustering of groups of terms based on the proximity within each focus-keyword group is also conducted. Term distributions within each visual configuration are characterized and are compared with formal medical vocabulary. This investigation reveals that there are significant differences between consumer health query-term usage and more formal medical terminology used by medical professionals when describing the same medical subject. Future directions are discussed.
-
Zhang, L.; Liu, Q.L.; Zhang, J.; Wang, H.F.; Pan, Y.; Yu, Y.: Semplore: an IR approach to scalable hybrid query of Semantic Web data (2007)
0.00
3.948108E-4 = product of:
0.006316973 = sum of:
0.006316973 = product of:
0.018950919 = sum of:
0.018950919 = weight(_text_:k in 231) [ClassicSimilarity], result of:
0.018950919 = score(doc=231,freq=2.0), product of:
0.09609793 = queryWeight, product of:
3.569778 = idf(docFreq=3384, maxDocs=44218)
0.026919862 = queryNorm
0.19720423 = fieldWeight in 231, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.569778 = idf(docFreq=3384, maxDocs=44218)
0.0390625 = fieldNorm(doc=231)
0.33333334 = coord(1/3)
0.0625 = coord(1/16)
- Source
- Proceeding ISWC'07/ASWC'07 : Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference. Ed.: K. Aberer et al
-
Zhang, J.; Zeng, M.L.: ¬A new similarity measure for subject hierarchical structures (2014)
0.00
3.7992393E-4 = product of:
0.006078783 = sum of:
0.006078783 = product of:
0.018236348 = sum of:
0.018236348 = weight(_text_:22 in 1778) [ClassicSimilarity], result of:
0.018236348 = score(doc=1778,freq=2.0), product of:
0.09426877 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.026919862 = queryNorm
0.19345059 = fieldWeight in 1778, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=1778)
0.33333334 = coord(1/3)
0.0625 = coord(1/16)
- Date
- 8. 4.2015 16:22:13
