2016 5th IIAI International Congress on Advanced Applied Informatics

Books Cited in Wikipedia: Possibility to Use their
Nippon Decimal Classification Categories for Book
Recommendation
Keita Tsuji
Faculty of Library, Information and Media Science, University of Tsukuba
1–2 Kasuga, Tsukuba City, Ibaraki-ken, Japankeita@slis.tsukuba.ac.jp
Abstract—This paper investigated the effectiveness ofdeveloping a book recommendation system based on books citedin Wikipedia articles, focusing on their Nippon DecimalClassification (NDC). Among 95,194 articles, 28,154 cited books
showing ISBNs in their bibliographies. In many cases, all NDCsof books cited in each article were identical and thus consistent.
Such articles can be used for automatic assignment of NDCs.

various NDC categories. Therefore, the present research
investigated whether objections (A) and (B) hold true.
Henceforth, the NDC category is referred to as “NDC” forbrevity.

Keywords—NDC, Nippon Decimal Classification, Wikipedia(1) We downloaded the dump of Japanese Wikipedia fromhttps://dumps.wikimedia.org/jawiki/ (as of February 21,2015).
(2) We extracted pages whose namespace (ns) tag was “0,”which means that the page was “Main/Article” (henceforthreferred to as “articles”).
(3) From pages extracted in (2), we extracted pages containing“References.”(4) We extracted pages where “References” contained
bibliographies of books showing their ISBNs.
(5) We searched for correct and consistent bibliographies ofbooks by using the ISBNs in OpenSearch, provided by theNational Diet Library of Japan.
(6) We eliminated bibliographies of books that did not contain
triple-digit NDCs (i.e., which consisted of Main Class,
Division, and Section) or titles.
(7) We counted the number of NDCs (only Main Class andDivision) in all bibliographies obtained through (6).
(8) We counted the number of NDCs (only Main Class andDivision) in each Wikipedia article, identifying the mostpopular NDC among them and counting its frequency inthe bibliography. For instance, if an article contained fivebooks and their NDCs were 324, 324, 324, 325 and 367,
NDC “32” (Main Class and Division of NDC 324 and 325.
Incidentally, 32 representing “Law” and 36 representing“Society”) was most popular, and its frequency was four.

I.

II. METHOD
We obtained and investigated data in the following steps:

INTRODUCTION

Wikipedia is now an important source of information for
university students. It has been shown that they use Google
first and then read Wikipedia articles even in university
libraries. Actually, students who search for books and readthem are relatively few [1]. In this situation, the present
researchers are now developing a system that recommends
books to users based on Wikipedia articles they are reading inlibraries (the system will be added onto web browsers inlibraries’ desktop PCs). We hope that students reading
Wikipedia articles will become interested in these librarybooks and learn deeply by reading them.
Regarding book recommendation, Nippon DecimalClassification (NDC) categories are found to be effective clues
for determining which books should be recommended [2][3][4].
More specifically, machine learning methods, such as theSupport Vector Machine, can more effectively find relevantbooks by using NDC book categories of interest to users.
Therefore, if we could assign NDC categories to Wikipedia
articles, they might be used for effective book
recommendations. While some studies have focused on
automatically assigning NDC categories, for instance, toreference records in libraries [5], relatively few have focusedon Wikipedia articles.
As a first step toward automatic assignment of NDC
categories to Wikipedia articles, the present research
investigated NDC book categories cited in Wikipedia. We
believe that Wikipedia articles fall into the same (or similar)
NDC categories as those of books cited in articles. For instance,
if article X cites many books whose NDC categories are “324,”
X is likely to belong to the “civil law” category. On this basis,
we can automatically assign NDC categories to Wikipedia
articles. However, this idea cannot be used if (A) many articles
do not cite books, or (B) each article tends to cite books with
978-1-4673-8985-3/16 $31.00 © 2016 IEEE
DOI 10.1109/IIAI-AAI.2016.247

III. RESULTS
The Japanese Wikipedia totaled 2,689,050 pages, and thenumber of articles obtained through Step (2) was 1,104,962.
The number of articles obtained through (3) and (4) were
95,194 and 28,154, respectively. The number of ISBNs
contained in 28,154 articles, and thus used in step (5), was66,295 (including duplicates). The number of bibliographies
obtained through (7) was 61,173 (including duplicates). The

1196

number of articles that contained at least one such bibliography
was 26,879. Therefore, NDCs were obtained from 28.2% (=26,879 / 95,194) of articles

references (77.1%). Therefore, we might be able to use suchreferences for automatic assignment of NDCs without acomplicated process. For instance, Wikipedia page “Fermat’s
Last Theorem” cited 17 books, and all their NDCs (Main Classand Division) were “41” (Mathematics). In such cases, we cansimply and safely assign NDC “41” to “Fermat’s LastTheorem.”
IV. CONCLUSIONS

Regarding the possible objection (A), 28.2% of articles
cited books from which we could obtain NDCs. In the present
study, we focused only on books cited with ISBNs, because
citations in “References” without ISBNs are (a) sometimes
journal articles, or (b) in various formats, which makes it
difficult to extract bibliographic items automatically andcorrectly (for instance, automatically distinguishing titles from
author/publisher names), to input them into OpenSearch.
However, provided such methods are established, a sufficientquantity of books appears to be cited in Wikipedia that could
be used for automatic assignment of NDCs to articles.

To develop a book recommendation system, weinvestigated NDCs of books cited in Wikipedia articles.
Results indicated that a sufficient number of articles can beused for automatic assignment of NDCs. On the basis of thisfinding, we will subsequently develop a system thatrecommends books to Wikipedia readers in conjunction withNDCs.
REFERENCES

Results of Steps (7) and (8) are shown in TABLEs I and II,respectively. In TABLE I, for instance, the most frequently
observed NDC Main Class is “2” (History), and the
combination of Main Class and Division is “21” (JapaneseHistory). Their frequencies are 16,438 and 6,328, respectively.

[1]

[2]

We can see in TABLE II that frequencies of the mostpopular NDC in each reference sometimes equal the number ofbooks cited in references. For instance, concerning 2,795 (=350 + 1,082 + 1,363) references in which three books werecited, in 1,363 references, the frequency of the most popular
NDC was also three. In other words, all three NDCs wereidentical in 1,363 references. However, many references
consisted of various NDCs. For instance, again concerning the2,795 references in which three NDCs were obtained, in 350
references, frequency of the most popular NDC was one. Thatmeans three NDCs differed from each other in these 350
references. Therefore, regarding objection (B), we should
consider the variety of NDCs in references when using themfor automatic assignment of NDCs. However, it is worth notingthat 20,722 (= 14,295 + 3,836 + 1,363+…+ 25 + 17 + 7)
references consisted of just one NDC, among 26,879

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[4]

[5]

FREQUENCY OF NDCS OF BOOKS CITED IN WIKIPEDIATABLE I.
㻺㻰㻯[3]T. Anbiru et al., “Information seeking behavior,” Proceedings of theSpring Meeting of the Japan Society of Library and Information Science,
2010, pp. 87–90. (text in Japanese).
K. Tsuji, F. Yoshikane, S. Sato, and H. Itsumura, “BookRecommendation Using Machine Learning Methods Based on Library
Loan Records and Bibliographic Information,” International Journal ofAcademic Library and Information Science, vol. 3, no. 1, 2015, pp. 7–23.
K. Tsuji, F. Yoshikane, S. Sato, and H. Itsumura, “BookRecommendation Using Machine Learning Methods Based on Library
Loan Records and Bibliographic Information,” Proceedings of the 5thInternational Conference on E-Service and Knowledge Management(ESKM 2014), 2014, pp. 76–79.
K. Tsuji et al., “Book Recommendation based on Library Loan Recordsand Bibliographic Information,” Proceedings of the 3rd International
Conference on Integrated Information (IC-ININFO 2013), 2013, 8p. (NoPagination).
S. Arai and K. Tsuji, “Automatically Assigning NDC Categories toReference Service Records by Using Machine Learning Methods,”
Journal of Japan Society of Information and Knowledge, vol. 25, no.1,2015, pp. 23–40. (text in Japanese).

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NUMBER OF BOOKS CITED IN ONE ARTICLE AND FREQUENCY OF THE MOST POPULAR NDC SHOWN IN THEM㻝㻝 㻝㻠㻘㻞㻥㻡㻞 㻞㻘㻜㻝㻞㻟㻟㻡㻜㻠㻣㻥㻡㻝㻞㻢㻢㻣㻝㻤㻜㻥㻜㻝㻜㻜㻝㻝㻜㻝㻞㻜㻝㻟㻜㻝㻠㻜㻝㻡㻗㻞䠉㻟㻘㻤㻟㻢㻝㻘㻜㻤㻞㻟㻣㻢㻝㻢㻢㻢㻜㻝㻥㻠㻞㻝㻝㻜㻜㻜㻟䠉䠉㻝㻘㻟㻢㻟㻠㻜㻣㻝㻥㻠㻝㻝㻟㻡㻥㻞㻤㻝㻡㻣㻝㻝㻜㻝㻠䠉䠉䠉㻡㻢㻠㻝㻡㻞㻝㻝㻜㻡㻡㻟㻝㻝㻠㻥㻝㻝㻝㻠㻞㻡䠉䠉䠉䠉㻞㻣㻠㻤㻡㻡㻜㻞㻣㻞㻡㻝㻡㻝㻜㻠㻢㻞㻲㼞㼑㼝㼡㼑㼚㼏㼥㻌㼛㼒㻌㼠㼔㼑㻌㻹㼛㼟㼠㻌㻼㼛㼜㼡㼘㼍㼞㻌㻺㻰㻯㻢㻣㻤㻥䠉䠉䠉䠉䠉䠉䠉䠉䠉䠉䠉䠉䠉䠉䠉䠉䠉䠉䠉䠉㻝㻠㻝䠉䠉䠉㻠㻜㻣㻞䠉䠉㻞㻝㻟㻟㻠㻝䠉㻝㻟㻝㻣㻞㻣㻠㻝㻝㻝㻝㻠㻤㻝㻠㻣㻝㻠㻡㻤㻣㻠㻤㻢㻟㻞㻟㻢㻞㻟㻞㻞㻞㻝㻥1197㻜䠉䠉䠉䠉䠉䠉䠉䠉䠉㻞㻟㻣㻟㻠㻡㻝㻝䠉䠉䠉䠉䠉䠉䠉䠉䠉䠉㻞㻟㻠㻠㻝㻝㻞䠉䠉䠉䠉䠉䠉䠉䠉䠉䠉䠉㻞㻡㻤㻥㻝㻟䠉䠉䠉䠉䠉䠉䠉䠉䠉䠉䠉䠉㻝㻣㻟㻝㻠䠉䠉䠉䠉䠉䠉䠉䠉䠉䠉䠉䠉䠉㻣