People Search using NDC Classification SystemHarumi Murakami

Yoshinobu Ura

Osaka City University
3-3-138, Sugimoto, Sumiyoshi,
Osaka 558-8585 Japan+81-6-6605-3375

winspire
9-313, Hachiban-cho,
Wakayama 640-8157 Japan+81-73-425-1215

harumi@media.osaka-cu.ac.jp

ura@winspire.jp

ABSTRACTTo help users select and understand people during searches forthem, we present a method of assigning Nippon DecimalClassification (NDC), which is a system of library classification
numbers, to people on the Web. By assigning NDC numbers topeople, we can assign not only labels to people but also build aNDC-based people directory. We developed a prototype based onthis approach.

Categories and Subject Descriptors
H.3.3 [Information Storage and Retrieval]: Information Searchand Retrieval – Retrieval models.

General Terms
Design, Algorithms

Keywords
People search directory, NDC, relative index, Web people search

1. INTRODUCTION
The popularity of Web people searches continues to rise as thenumber of people increases about whom the Web can provide
information.
Most people search systems are based on keyword search. Bykeyword search, which is typically a search by a person name or a
keyword, users distinguish different people from the search
results. If the list is merely “person 1, person 2, and so on,” users
have difficulty determining which person they should select.
Appropriate labels shown with people should help users select theperson they want.
There is research that assigns labels to people. For example, Wanet al. separated Web people search results and assigned titles toperson clusters [1]. Ueda et al. assigned vocation-related
information to person clusters [2]. Mori et al. extracted keywords
contained in Web pages [3].
In this paper, we present an approach of assigning labels to people
to help users distinguish and understand people. We use Nippon
Decimal Classification (NDC), which is a library classification
system in Japan, whose organization resembles the Dewey
Decimal Classification (DDC). NDC is comprised of ten classes,
each of which is divided into ten divisions, and each division has
ten sections, and so on. The NDC number is constructed fromthree digits (with another optional digits after the decimal point.)

By assigning NDC numbers to people, we can assign labels topeople and build a NDC-based person directory. For example,when we assign 312.8 (Politician) to Japanese prime minister
Naoto Kan, users can browse 300 (Social sciences: class) to 310(Political sciences: section) to 312 (Political history andconditions: division) and find him in the NDC-based directory.
Although the NDC system was designed to classify librarycollections (mainly books) and not people, we exploit its
advantages for the following reasons: (1) There are fewestablished general classification system other than libraryclassification systems in Japan, and (2) It is the most popularlibrary classification system in Japan.
This research assigns NDC numbers to people on the Web, anddevelops a NDC-based people-search directory.

2. APPROACH
2.1 Overview
The main feature of our approach is using a relative index in NDC.
The relative index lists the related index terms attached to NDC
numbers. For example, three index terms talent, intellect,intelligence are attached to 141.1 (Intelligence). There are 29,514
index terms and 8,551 NDC9 (version 9) numbers.
Our proposed algorithms are constructed from two processes: (1)extracting relative index terms of Web pages, and (2) assigning
NDC numbers to people.

2.2 Extracting Index Terms
When HTML files of a person are given, after removing theHTML tags, we extract the index terms from the texts inside thetitle tags. When multiple index terms can be extracted, thelongest-match method is used.
We deleted the following index terms that we consider
unnecessary: (a) those that consist of one character and (b) 100
manually selected terms that often appear on the Web.

2.3 Assigning NDC Numbers
Index terms are converted to NDC Numbers, which are assigned
based on the following scores:score(ndci ) = nfreq(ndci )∑ freq(ndc )kk =1

Where ndc is a NDC number and n is a distinct number of NDC
numbers attached to a person.

2.4 Example
Copyright is held by the author/owner(s).
ESAIR’11, October 28, 2011, Glasgow, Scotland, UK.
ACM 978-1-4503-0958-5/11/10.

Consider the following sentence: “Suguru Egawa is a former
professional baseball player and a baseball commentator. He likeswine...” Former, professional baseball, baseball, and wine areextracted as index terms. Former is removed because it consistsof just one Japanese character. Professional baseball, baseball,and wine are converted to 783.7, 783.7, and 588.58. The scores of783.7 (baseball) and 588.58 (brewage) are 0.666 (2/3) and 0.333(1/3), respectively. These numbers are attached to “Suguru
Egawa.” For 783.7 (baseball), its class is 700 (The arts. Finearts), its section is 780 (Sports and physical training), its division
is 783 (Ball games).

3. PROTOTYPE
We implemented a prototype using our proposed method. We
assigned five NDC9 numbers to the 137 people (person clusters)
obtained in [4].
Figure 1 shows an initial screen of a NDC-based people-search
directory. When a user selects 780 (Sports and physical training),
Figure 2 is displayed. The upper side of the screen lists the list of
the divisions of 780, and the lower side of screen shows the list ofpeople assigned to 780. For example, Suguru Egawa (formerbaseball player) and Ai Fukuhara (table tennis player) aredisplayed, with five NDC numbers assigned to each. When a user
selects a person, information about him or her (in this case, thesearch result pages of the designated person) is displayed.

extraction on Web pages [5]. No such research has assigned
library classification numbers to person clusters.
Some research suggests NDC numbers or other terms in libraries.
Kiyota et al. suggests LCSH subject headings and NDC numbers[6], and Ueda et al. suggests BSH subject headings and NDCnumbers according to user input. They all use web information
sources as Wikipedia without using relative index terms.
We believe that our main contribution is to assign libraryclassification numbers to people to display labels and build apeople-search directory. Our research is an initial step. Its
proposed algorithms are simple, and we will improve them in thefuture. Although our research is limited to NDC and Japanese, ourapproach is easily applicable to other classification systems suchas DDC and UDC with similar organization and relative indexes
or other terminology. People are one representative entity, and ourapproach can be applied to other kinds of entities, such asindustries or place names.
Although the idea of using a library classification system itself is
not so new, we believe that the time has come to re-evaluate its
usefulness as semantic annotations.

5. SUMMARY
We presented a method that assigned NDC numbers to people on
the Web. We developed a NDC-based people search directory.

6. ACKNOWLEDMENTS
This work was supported by KAKENHI (22500219).

7. REFERENCES[1] Wan, X., Gao, J., Li, M., and Ding, B. 2005. Person
Resolution in Person Search Results: WebHawk, Proceedings ofthe Fourteenth ACM Conference on Information and Knowledge
Management (CIKM 2005), 163-170.
Figure 1. Initial screen.

[2] Ueda, H., Murakami, H., and Tatsumi, S. 2009. Assigning
Vocation-Related Information to Person Clusters for Web People
Search Results. Proceedings of the 2009 Global Congress onIntelligent Systems (GCIS 2009), 4, 248-253.
[3] Mori, J., Matsuo, Y., and Ishizuka, M. 2005. Personal
Keyword Extraction from the Web. Journal of Japanese Society
for Artificial Intelligence. 20, 337-345.
[4] Murakami, H., Takamori, Y., Ueda, H., and Tatsumi, S. 2009.
Assigning Location Information to Display Individuals on a Mapfor Web People Search Results, Proceedings of The Fifth Asia
Information Retrieval Symposium (AIRS 2009), LNCS 5839, 2637.

Figure 2. Screen list of people.

4. RELATED WORK AND DISCUSSION
There is research that assigns labels to people. Wan et al. assigned
titles [1], Ueda et al. assigned vocation-related information [2],and Mori et al. assigned keywords to person clusters [3]. WePS2/3 conducted competitive evaluation on person attribute[5] Artiles, J., Borthwick, A., Gonzalo, J., Sekine, S., and Amigó,
E. 2010. WePS-3 Evaluation Campaign: Overview of the Web
People Search Clustering and Attribute Extraction Tasks. CLEF
2010.
[6] Kiyota, Y., Nakagawa, H., Sakai, S., Mori, T., and Masuda, H.
2009. Exploitation of the Wikipedia Category System for
Enhancing the Value of LCSH (JCDL 2009), 411.