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1 DEIM Forum 2014 A8-1, ,, 1. Google 1 Yahoo 2 Bing 3 Web Web BM25 [1] HITS [2] PageRank [3] Web 1 [4] 1http:// 2http:// 3http:// O = {t o1, t o2,, t on } O t a O t a t oi O t a

2 [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]Noda [15] Wikipedia 4 (Wikipedia 1 2) 3 Wikipedia 2 1 Wikipedia Nadamoto [14] SNS 1 Wikipedia Wikipedia Liu [12] 2 Web 4http://ja.wikipedia.org/ Web Web Web Mejova [13] WebJaccard WebDiceWebOverlap NGDWWebPMI Web Bollegala [6] 2 [7] 1 Web 1 [8] Wikipedia [9] [10] [11]Strube [9] Wikipedia 2 2 Gabrilovich [10] Wikipedia Milne [11] 2 Wikipedia Wikipedia Wikipedia Wikipedia 2 Milne Strube Gabrilovich Gabrilovich 3. (t 1, t 2) t 1 t 2 c c T c = {t o1, t o2,, t on, } t a t oi T c t a t oi T c c T c t a t oi T c t a 1.

3 WLM 1 Milne [11] t 1 t 2 Wikipedia t 1 t 2 t 1 t 2 TF-IDF s t s t ( ) W w(s t) = log if s B t, 0 otherwise. (1) B t B t t W Wikipedia t 1 t 2 1 s t 1 t 2 t 1 t 2 F t1 F t2 t F t1 F t2 t 1 t 2 sim forward (t 1, t 2) t 1 t 2 sim backward (t 1, t 2 ) = log (max ( B t 1, B t2 )) log ( B t1 B t2 ). log ( W ) log (min ( B t1, B t2 )) (2) 2 t 1 t 2 wlm(t 1, t 2 ) wlm(t 1, t 2) = 1 2 sim forward(t 1, t 2) sim backward(t 1, t 2) WebPMI (3) 2 Web 2 WebPMI [6] [5] WebPMI t 1 t 2 0 if hit(t 1, t 2 ) < = c web pmi(t 1, t 2 ) = ( ) hit(t log 1 t 2 )/N 2 otherwise. (hit(t 1 )/N) (hit(t 2 )/N) hit(t) t Web Clue Web 09 5 Bollegala [6] c = 5 N Web Clue Web 09 5http://lemurproject.org/clueweb09/ (4)

4 N = 67, 337, 717 WebJaccard WebDiceWebOverlap NGD WebPMI [5], [6] Noda 3 Web 2 [16] t 1 t 2 noda(t 1, t 2 ) = hit(t1 t2)2 hit(t 1 ) hit(t 2 ). (5) WebPMI t oi t a rel pop(t oi, t a ) rel pop(t oi, t a ) = rel(t oi, t a ) pop(t oi ) (6) rel(t oi, t a) 4. 1 pop(t oi ) Wikipedia t oi t oi Wikipedia [17] t oi t oi [18] [19] ALAGIN 6 Wikipedia t oi t oi 1 HITS [2] t oi t oi t oj coordinate(t oi, t oj ) https://alaginrc.nict.go.jp/hyponymy/index.html biased PageRank [20] biased PageRank biased PageRank Biased PageRank T c = {t o1, t o2,, t on } (i, j) coordinate(t oj, t oi ) n 1 (i, j) a i,j a i,j = coordinate(t oj, t oi ) t ok T c coordinate(t oj, t ok ). (7) t oi t a rel crd(t oi, t a) rel crd l+1 (t oi, t a ) =α 1< = j< = n + (1 α) a i,j rel crd l (t oi, t a ) rel(t oi, t a ) t oj T c rel(t oj, t a) biased PageRank 12α 0 < = α < = 1 α α (8) 4. 4 t oi t a biased PageRank 4. 2 rel pop crd l+1 (t oi, t a ) =α 5. 1< = j< = n + (1 α) a i,j rel pop crd l (t oi, t a ) rel pop (t oi, t a ) t oj T c rel pop(t oj, t a). (9) 2 1

5 C MVP WLM Wikipedia 1 Wikipedia Wikipedia 1 W 1, 342, Wikipedia http:// 1 Web WLM WebPMI Noda 4. 2 WLM+pop WebPMI+pop Noda+pop 4. 3 WLM+BPR WebPMI+BPR Noda+BPR 4. 4 WLM+pop+BPR WebPMI+pop+BPR Noda+pop+BPR Biased PageRank

6 2 α ****** original 10%5%1% pop pop+bpr BPR pop+bpr 3 Biased PageRank original WLM WebPMI Noda Biased PageRank WLM WebPMI Noda 2 pop 2 BPR WLM Noda 2 pop+bpr 1 biased PageRank 6

7 3 Noda Noda+BPR α = WebPMI WebPMI+pop+BPR α = Noda Noda+BPR α = Noda 10 Noda Noda+BPR { } 55 { } 3 { } 136 { } 1 { } 28 { } 1 { } 190 { } WebPMI WebPMI+pop+BPR α = Bollegala [6] Gabrilovich [10] c m t a c m t a c m O m = {o 1, o 2,, o m} t a unexp(c, O m, t a) = (1 rel pop cred(o i, t a)) (10) o i O m m = 2 rel pop cred(o i, t a) 5. Noda+pop+BPR

8 { } 2 { } 10 Wikipedia 2 Wikipedia J03993 [1] S. E. Robertson and S. Walker: Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval, Proc. of ACM SIGIR 1994, pp (1994). [2] J. M. Kleinberg: Authoritative sources in a hyperlinked environment, J. ACM, 46, pp (1999). [3] S. Brin and L. Page: The anatomy of a large-scale hypertextual web search engine, Proc. of WWW 2007, pp (1998). [4] K. Tsukuda, H. Ohshima, M. Yamamoto, H. Iwasaki and K. Tanaka: Discovering unexpected information on the basis of popularity/unpopularity analysis of coordinate objects and their relationships, Proc. of SAC 2013, pp (2013). [5] G. Lu, P. Huang, L. He, C. Cu and X. Li: A new semantic similarity measuring method based on web search engines, W. Trans. on Comp., 9, 1, pp (2010). [6] D. Bollegala, Y. Matsuo and M. Ishizuka: A web search engine-based approach to measure semantic similarity between words, IEEE Trans. on Knowl. and Data Eng., 23, 7, pp (2011). [7] M. Sahami and T. D. Heilman: A web-based kernel function for measuring the similarity of short text snippets, Proc. of WWW 2006, pp (2006). [8] H.-H. Chen, M.-S. Lin and Y.-C. Wei: Novel association measures using web search with double checking, Proc. of ACL-44, pp (2006). [9] M. Strube and S. P. Ponzetto: Wikirelate! computing semantic relatedness using wikipedia, Proc. of AAAI 2006, pp (2006). [10] E. Gabrilovich and S. Markovitch: Computing semantic relatedness using wikipedia-based explicit semantic analysis, Proc. of IJCAI 2007, pp (2007). [11] D. Milne and I. Witten: An effective, low-cost measure of semantic relatedness obtained from wikipedia links, Proc. of WIKIAI 2008, pp (2008). [12] B. Liu, Y. Ma and P. S. Yu: Discovering unexpected information from your competitors web sites, Proc. of ACM SIGKDD 2001, pp (2001). [13] Y. Mejova, I. Bordino, M. Lalmas and A. Gionis: Searching for interestingness in wikipedia and yahoo!: answers, Proc. of WWW 2013, pp (2013). [14] A. Nadamoto, E. Aramaki, T. Abekawa and Y. Murakami: Content hole search in community-type content, Proc. of WWW 2009, pp (2009). [15] Y. Noda, Y. Kiyota and H. Nakagawa: Discovering serendipitous information from wikipedia by using its network structure, Proc. of ICWSM 2010, pp (2010). [16],,,, web, DBSJ Letters, 5, 2, pp (2006). [17],,,, wikipedia, p. to appear (2013). [18],, Web,., 47, 19, pp (2006). [19],,, Web (WebDB Forum 2013) (2013). [20] T. H. Haveliwala: Topic-sensitive pagerank, Proc. of WWW 2002, pp (2002).

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DEIM Forum 2012 D2-1 606 8501 150 0002 2-15-1 28F E-mail: {tsukuda,ohshima,tanaka}@dl.kuis.kyoto-u.ac.jp, {miyamamoto,hiwasaki}@d-itlab.co.jp 1 Wikipedia Wikipedia HITS 1. Web Web Web 1 3 Wikipedia 2 Web