27 The automatic extract of know-how search tag using a thesaurus 1160374 2016 2 26
IT i
Abstract The automatic extract of know-how search tag using a thesaurus In recent years, a number of organizational information sharing system using the IT technology operating in, case to improve the productivity has increased. In particular, are expected to lead to the growth of the organization by building a spiral type conversion relationship of the form rule of thumb by tacit knowledge and a third party, such as know-how is the information that has been structured as possible understanding with the individual. However, and when the long-term operation of the shared system, such as when utilized in a number of human, increases the contents registered in the shared system, a search and information recommendation efficiency decreases.in this study, we have developed a search system for performing efficient searches using search tags. It analyzes the contents of the input information, by determining the utilization of search tags, to improve the search efficiency. In addition, to evaluate the search accuracy of the proposed system. key words Know-how Information sharing Thesaurus ii
1 1 1.1.................................. 1 1.2................................. 2 2 3 2.1............................ 3 2.2.............................. 4 3 6 3.1................ 6 3.2.................... 7 3.2.1............................. 8 3.2.2............................... 8 3.2.3 tf-idf................................ 8 3.2.4................................. 9 4 11 4.1................................ 11 4.1.1............................ 11 4.1.2............................... 12 4.1.3 tf-idf................................ 12 4.1.4............................... 12 4.1.5......................... 13 4.1.6.......................... 13 4.1.7................................ 13 iii
5 15 5.1...................................... 15 5.2...................................... 15 6 17 18 19 iv
2.1 SECI.................................. 4 3.1.................................... 7 4.1................................ 12 v
4.1.................................... 14 5.1..................................... 16 5.2..................................... 16 vi
1 1.1 IT IT 2 [1] 1
1.2 1.2 2 3 4 5 2
2 2.1 IT 300 2012 85.7% [2] 2014 IT IT 433 51.3% IT 38.2% [3] 3
2.2 2.2 2 [4] SECI. 2.1 2.1 SECI SECI 4 4 4
2.2 Socialization Externalization Combination Internalization 4 5
3 IT 3 2.1 3.1 2007 9 blog 2016 12 604 2 4 IT 6
3.2 20 3.2 [5] 2.1 3.1 7
3.2 tf-idf 3.2.1 3.2.2 3.2.3 tf-idf tf-idf [6] tf term frequency idf inverse document frenqency tf 3.1 idf 3.2 D 8
3.2 T tfreq(t,d) T dfreq(t) M T D w(t,d) 3.3 tf(t, D) = log(tfreq(t, D) log(tnum(d) (3.1) M idf(t ) = log( defreq(t ) ) (3.2) w(t, D) = tf(t, D) idf(t ) (3.3) 3.2.4 tf-idf 2 idf defreq(t) idf 9
3.2 10
4 3.2 4.1 4.1.1 4.1 11
4.1 4.1 4.1.2 MeCab[7] 4.1.3 tf-idf tf-idf idf 4.1.4 12
4.1 [8] 4.1.5 4.1 tf-idf = (4.1) 4.1.6 5 4.1.7 4.1 13
4.1 CPU 4.1 1.3GHz Intel Core i5 OS CentOS 6.4 MySQL 5.6.22 Python 2.6.6 MeCab 14
5 5.1 50 10 10 8 5 1: 5: 5 5.2 5.1 5.2 15
5.2 A A/5 4 5 B B/5 5.1 1 2 3 4 5 6 7 8 9 10 0.38 0.33 0.35 0.30 0.38 0.28 0.35 0.48 0.33 0.40 0.36 0.78 0.68 0.75 0.75 0.73 0.83 0.70 0.80 0.78 0.73 0.75 5.2 1 2 3 4 5 6 7 8 9 10 0.73 0.75 0.60 0.73 0.68 0.70 0.75 0.70 0.78 0.73 0.71 0.85 0.78 0.85 0.83 0.80 0.90 0.83 0.80 0.85 0.88 0.85 10 0.36 0.71 0.75 0.85 0.14 0.39 16
6 0.14 0.39 17
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[1], 2 2006. [2] 4, http://www.chusho.meti.go.jp/pamflet/ hakusyo/h25/pdf/0ehakusyo\_part2\_chap1\_web.pdf 2016/2/24 [3] IT (2014), http://www.keyman.or.jp/at/30007495/ 2016/2/24 [4],, 1996. [5], 35 pp5-8 2009.7 [6], pp.3-6 2004. [7] MeCab http://mecab.sourceforge.net/ 2016/2/24 [8], http://pj.ninjal.ac.jp/ corpus_center/archive.html 2016/2/24 19