1 AND TFIDF Web DFIWF Wikipedia Web Web AND 5. Wikipedia AND 6. Wikipedia Web Ma [4] Ma URL AND Tian [8] Tian Tian Web Cimiano [3] [

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1 DEIM Forum 2015 B Web Web AND AND Web 1. Twitter Facebook SNS Web Web Web Web [5] Bollegala [2] Web Web 1 Google Microsoft Bing Cimiano [3] Web Web Web Web Web Web Web 1 4,730, , Web Satoh [7]

2 1 AND TFIDF Web DFIWF Wikipedia Web Web AND 5. Wikipedia AND 6. Wikipedia Web Ma [4] Ma URL AND Tian [8] Tian Tian Web Cimiano [3] [5] AND Bollegala [2] AND SVM Satoh [7] Uyar [9] Satoh Web Uyar Google Yahoo Microsoft Satoh Uyar Microsoft Bing Search API

3 Histogram of Famousness 1 Frequency log( ) Famousness Famousness Hitcount.logarithm Bing Search API MeCab [1] Web Web 20 AND 4. 1 TFIDF TFIDF(term frequency / inverse document frequency) [6] AND TFIDF TFIDF C c i D i w T F w,i D i w D C i d i DF w,i w DF w,i IDF w,i IDF w,i = log( Di DF w,i ) (1) T F w,i IDF w,i T F IDF w,i TFIDF w,i = TF w,i IDF w,i (2) T F IDF w,i c i C c i TFIDF

4 log( ) AND 2 TFIDF AND AND 13,300, Web 4. 2 DFIWF 4. 1 TFIDF AND DFIWF(document frequency / inverse web frequency) DFIWF DFIWF DFIWF Web C c i d i D = {d 1, d 2,... d n } w D DF w w W F w IW F w 1 IWF w = log( ) (3) W F w DF IW F w DFIWF w = DF w IWF w (4) DF IW F w D Web w w Web DFIWF AND AND 3 DFIWF DF WF DFIWF DF DFIWF TFIDF DFIWF 3 3 AND 5. AND 4. TFIDF DFIWF TFIDF DFIWF

5 AND Web 4. TFIDF Web Web AND AND Web AND AND T c i t j T AND h i,tj famousness i W = {w 1, w 2,, w n} famousness i = w 1 h i,t1 + w 2 h i,t2 + + w n h i,tn n (5) = (w j h i,tj ) j=1 (5) AND W W AND Leave-one-out 499 AND W W 1 AND AND 5 w i AND 0 4. Satoh [7] Web Web 6. 1 Web Web Web Web Web

6 Web Web Category c W = {w 0, w 1,..., w n} c C w i f c,i Category c = {f c,0, f c,1,..., f c,n} AND t X c Y t c AND X Y b t,c b t,c = X Y X + Y (6) t c Category c t c b t,c V t w i f t,i V t = {f t,0, f t,1,..., f t,n} c Category c t c b t,c V t t Celebrity t t C t Celebrity t = c C t (b t,c Category c) + V t (7) 7 Celebrity t Celebrity t Web 7 Web Web Web Web Web 7 Web Web Web Web Web p P age p V t w i Web p f p,i Page p = {f p,0, f p,1,..., f p,n} (8) 8 Web p P age p 7 t Celebrity t cos sim t,p sim t,p = Celebrityt Pagep Celebrity t Page p (9) Web p sim t,p Web Occurrence t Occurrence t Occurrence t = P p simt,p P (10) Occurrence t t Web Web Occurrence t t Web WebAffinity t WebAffinity t = p 1 Occurrence t + p 2 (11) p 1 p 2 Web t NewsHook t Web Wikipedia Wikipedia Wikipedia t Wikipedia 1 WikiAccess t Wikimedia WikiEdit t Wikipedia WikiAccess t WikiEdit t t NewsHook t NewsHook t = p 3 WikiAccess t +p 4 WikiEdit t +p 5 (12) 11 p 1, p 2 p 3, p 4, p 5

7 Web 4 t Famousness t HitCount t WebAffinity t NewsHook t AccumulateDuration t 4 Infobox HitCount t =Famousness t WebAffinity t NewsHook t AccumulateDuration t (14) 6. 4 Web Web Wikipedia 4 Wikipedia infobox infobox t days t AccumulateDuration t AccumulateDuration t = p 6 days t + p 7 (13) p 6 p t Web WebAffinity t NewsHook t AccumulateDuration t Web Web Web Web Web 14 Famousness t Famousness t = HitCount t WebAffinity t NewsHook t AccumulateDuration t (15) 15 WebAffinity t NewsHook t AccumulateDuration t p 1 p DFIWF 15 HitCount t DFIWF DFIWF AND 2 ( ) (DFIWF) 12 Wikipedia

8 5 Result of estimation ( ) (DFIWF) Correct Correct Estimate Estimate 5 ( ) 6 (DFIWF) 13 days t t p 1 p 7 Leave-one-Out DFIWF 6 5 DFIWF Web JSPS [1] Mecab. doc/index.html. [2] Danushka Bollegala, Yutaka Matsuo, and Mitsuru Ishizuka. Measuring semantic similarity between words using web search engines. www, 7: , [3] Philipp Cimiano, Siegfried Handschuh, and Steffen Staab. Towards the self-annotating web. In Proceedings of the 13th international conference on World Wide Web, pages ACM, [4] Qiang Ma and Masatoshi Yoshikawa. Ranking people based on metadata analysis of search results. In Sven Hartmann, Xiaofang Zhou, and Markus Kirchberg, editors, Web Information Systems Engineering - WISE 2008 Workshops, volume 5176 of Lecture Notes in Computer Science, pages Springer Berlin Heidelberg, [5] Yutaka Matsuo, Hironori Tomobe, and Takuichi Nishimura. Robust estimation of google counts for social network extraction. In AAAI, volume 7, pages , [6] GERARD SALTON. Developments in automatic text retrieval. Science, 253(5023): , [7] Koh Satoh and Hayato Yamana. Hit count reliability: how much can we trust hit counts? Web Technologies and Applications, pages , [8] Tian Tian, Soon Ae Chun, and James Geller. A prediction model for web search hit counts using word frequencies. Journal of Information Science, page , [9] Ahmet Uyar. Investigation of the accuracy of search engine hit counts. Journal of Information Science, 35(4): , Web

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