Assigning Library Classification Numbers to
People on the Web
Harumi Murakami1 , Yoshinobu Ura2 , and Yusuke Kataoka1
1Graduate School for Creative Cities, Osaka City University,
3-3-138, Sugimoto, Sumiyoshi, Osaka 558-8585 Japan
harumi@media.osaka-cu.ac.jp
http://murakami.media.osaka-cu.ac.jp/
2
winspire,
9-310, Hachiban-cho, Wakayama 640-8157 Japan

Abstract. To help users select and understand people during searches
for them, we present a method of assigning Nippon Decimal Classification(NDC), which is a system of library classification numbers, to people on
the web. By assigning NDC numbers to people, we can assign not only
labels to people but also build a NDC-based people-search directory.
We use a relative index in NDC, which lists the related index terms
attached to NDC. We developed a prototype based on this approach.
We evaluated the usefulness of our proposed method and directory andfound that extracting relative index terms from the titles of web pages
outperformed comparative methods.
Key words: NDC, library classification system, relative index, Web
people search, people-search directory

1

Introduction

The popularity of web people searches continues to rise as the number of people
increases about whom the web can provide information. Most people searchsystems are based on keyword search. By keyword search, which is typically asearch by a person name or a keyword, users distinguish different people fromthe 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 the person they want.
There is research that assigns labels to people. For example, Wan et al.
separated web people search results and assigned titles to person clusters [1].
Ueda et al. assigned vocation-related information to person clusters [2]. Mori etal. extracted keywords contained in web pages [3].
In this paper, we present an approach of assigning labels to people to help
users select 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, eachof which is divided into ten divisions, and each division has ten sections, and2Harumi Murakami et al.

so on. The NDC number is constructed from three digits (with other optional
digits after the decimal point.)
By assigning NDC numbers to people, we can assign labels to people andbuild a NDC-based people-search directory. For example, when we assign 312.8(Politician) to a former Japanese prime minister Naoto Kan, users can browse
300 (Social sciences: class) to 310 (Political sciences: division) to 312 (Political
history and conditions: section) and find him in the directory.
Although library classification systems were designed to classify library collections instead people, we exploit their advantages because many categorization
schemes proposed for web resources lack the rigorous hierarchical structure andcareful conceptual organization found in established schemes [4] such as libraryclassification systems. In this paper, we use NDC, which resembles DDC bothin its organization schemes and in having relative index terms. Moreover, NDC
is the most popular library classification system in Japan. This research assigns
NDC numbers to people on the web, and develops a NDC-based people-search
directory.
Below, we explain our approach in Section 2 and examples of our implemented
prototype in Section 3. Our experiments are described in Section 4. We discuss
the significance of our research in Section 5.

2

Approach

2.1

Overview

Our approach uses 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 from web pages, and (2) assigning NDC numbers to people(Figure 1).
2.2

Extracting Relative Index Terms

When HTML files of a person are given, after removing the HTML tags, we extract the relative index terms from the texts inside the title tags. When multiple
index terms can be extracted, the longest-match method is used.
We deleted the following index terms that we consider unnecessary: (a) thosethat consist of one character, and (b) 100 manually selected terms that often
appear on the web.
2.3

Assigning NDC Numbers

After the index terms are converted to NDC numbers, they are assigned basedon the following scores:

Assigning Library Classification Numbers to People on the Web

3

Person Search Results
Input

Suguru Egawa is a former professional baseballplayer and baseball commentator. Metal bats havebeen introduced to high!school baseball…
Extract relative index term
Delete stop word
Convert term to NDC
numberCalculate and assign NDC
number to people

Output:
top ranked
score

Class: 7 The Arts. Fine Arts.
Division: 78 Sports and physicaltraining
Section:783 Ball games
Exact: 783.7 Baseballformer (! stop word)
professional baseballbaseballmetal
baseballprofessional baseball 783.7
baseball 783.7
metal
524.28, 560
baseball 783.7
score (783.7) = 3/5 = 0.6score (524.28) = 1/5 = 0.2score (560) = 1/5 = 0.2

Fig. 1. Overview of the algorithm

f req(ndci )score(ndci ) = Pn
k=1 f req(ndck )(1)

Where ndc is a NDC number and n is a distinct number of NDC numbers
attached to a person.
2.4

ExampleConsider the following sentence: “Suguru Egawa is a former professional baseball player and a baseball commentator. Metal bats have been introduced inhigh-school baseball...” Former, professional baseball, baseball, metal, and baseball are extracted as index terms. Former is removed because it consists of justone Japanese character. Professional baseball and baseball are converted to 783.7(Baseball), and metal is converted to 524.28 (Metal. Alloy. Architectural hardware) and 560 (Metal engineering. Mine engineering).
The scores of 783.7 are 0.6 (3/5), 524.28 and 560 are 0.2 (1/5), respectively.
These numbers can be attached to Suguru Egawa. For the top ranked score 783.7(Baseball), its class is 700 (The arts. Fine arts), its division is 780 (Sports andphysical training), and its section is 783 (Ball games).

3

Prototype

We implemented a prototype using our proposed method. This is an example ofassigning the top five NDC numbers using the title documents in a dataset (seeSection 4).

4Harumi Murakami et al.

Figure 2 shows an initial screen of a NDC-based people-search directory.
When a user selects 780 (Sports and physical training), Figure 3 is displayed.
The upper side of the screen lists the list of the divisions of 780, and the lower sideof screen shows the list of people assigned to 780. For example, Suguru Egawa(former baseball player) and Ai Fukuhara (table tennis player) are displayed,with five NDC numbers assigned to each. When a user selects a person, information about him or her (in this case, the search result pages of the designatedperson) is displayed.

Class

DivisionFig. 2. Initial screen

Division

SectionName
Maximum of five NDC numbers assigned to a person

Bold letters indicate category (division, in this case)Fig. 3. Screen list of people

Assigning Library Classification Numbers to People on the Web

4

Experiment

4.1

Dataset5

We describe a previously developed dataset [5]. The twenty person names usedin related work [6] were selected as queries. 100 web pages (HTML files) wereobtained for each twenty queries from web searches (i.e., 20×100 = 2, 000 HTMLfiles). We manually classified these web pages into different people. 152 people
were found in all 2,000 web pages.
4.2

Experiment 1

We evaluated the usefulness of our algorithm that assigns NDC numbers topeople (person clusters).
Method We assigned NDC numbers to people (person clusters) with threemethods using the following six documents (i.e., 3 × 6 = 18): (a) Tf-idf, (b)Cosine, and (c) Our method. The six documents were (1) Title, (2) Html, (3)Snippet, (4) Kwic50, (5) Kwic100, and (6) Kwic200.
The Tf-idf and Cosine methods do not use relative index terms. We treateda document of a person cluster as a query and a NDC label as a document. The
numerator for calculating idf is the total amount of NDC numbers.
The Title is a document extracted from the title elements. The Html is entire
document. The Snippet is a document given as a result of a Yahoo! search. In
this paper, to examine co-occurrence information, we introduce concatenatedtext strings before and after person names. We call this “keyword in context(kwic)”. Kwic50 is a concatenated document of 50 Japanese characters before aperson’s name and 50 Japanese characters after it (i.e., 50 + 50 = 100 Japanesecharacters). Kwic 100 and Kwic200 are constructed in the same way except for
using 100 + 100 = 200 or 200 + 200 =400 Japanese characters. We removed theHTML tags from all documents. Figure 4 shows an example of the six documents.
We manually selected the most appropriate NDC numbers for each person(137 people out of 152). When there is no appropriate NDC number, we set it
to “none.” (i.e., 152 - 137 = 15 people.)
We checked whether the correct and assigned numbers (top ranked score) are
the same in each class level (0-9), each division level (00-99), each section level(000-999), and the exact number. For example, when the correct NDC number
is 783.7, the assigned number that starts with 7 (e.g. 700) is judged correct in
the class level, which starts with 78 (e.g. 780) is judged correct in the divisionlevel, which starts with 783 (e.g. 783) is judged correct in the section level, andonly 783.7 is judged correct at the exact level.
The following are the evaluation measures:

P recision =correct answers by the methodpeople to whom NDC was assigned by the method

(2)

6

Harumi Murakami et al.
Title

Html

<html><head><title>Baseball News</title></head><body>

Kwic50

Kwic100…
Kwic200<div>
The commissioner announced that a former Giant, Suguru Egawa (54), who iscurrently a baseball commentator, has been chosen as the next manager ofLotte…
(example) Kwic10</div>
10 + 10 = 20 Japanese…
characters
Baseball News
23 Jan 2011 … Baseball News from the net. PR. Pagetop.
Baseball Suguru Egawa said about Yuki Saito: “He will winaround 200 games.” Yuki Saito (Waseda U), Nippon Ham’s
top draft pick, …
…/blog!entry!1376.html

Snippet (fromYahoo! Web API)

Fig. 4. Six documents for evaluation

Recall =correct answers by the methodpeople to whom NDC was assigned manuallyF − measure =2 × P recision × Recall
P recision + Recall

Accuracy =correct answers
people

(3)
(4)
(5)

When calculating the Accuracy, none is judged correct when there is nocorrect NDC number for the people.
Results and Analysis Table 1 shows the average Accuracy values in Experiment 1. Our method outperformed the comparative methods, suggesting its
usefulness with relative index. For Accuracy, except for the class level, the Title
was best among all the documents.
Table 2 shows the results for the exact numbers. Our methods were better
than the comparative methods. In the six documents in our method, Title had
good Precision and Accuracy, Snippet and Kwics had good Recall, and Kwic50
slightly outperformed the Title in the F-measure.
4.3

Experiment 2

Experiment 1 showed the overall effectiveness of our method using title documents. We evaluated the precision of our algorithm from another perspective
using five-scale values.

Assigning Library Classification Numbers to People on the Web

7

Table 1. Result of Experiment 1: Accuracy
MethodTf-idfCosineDocumentMax
Max

Our method Title
Html
SnippetKwic50
Kwic100
Kwic200

Class
Division
0.44 (Title) 0.34 (Title)
0.43 (Title, 0.34 (Title)
Kwic200)
0.51
0.45
0.52
0.43
0.45
0.36
0.52
0.42
0.51
0.37
0.47
0.37

Section0.23 (Title)
0.25 (Title)

Exact
0.15 (Title)
0.17 (Title)

0.36
0.29
0.27
0.29
0.28
0.26

0.25
0.12
0.20
0.23
0.20
0.16

Note: for Tf-idf and Cosine, the maximum values were described. They all used titles.
Table 2. Result of Experiment 1: Exact level
MethodTf-idfDocument
Max

Precision
0.08 (Title)

CosineMax

0.12(Kwic200)
0.18
0.11
0.15
0.16
0.15
0.13

Our method Title
Html
SnippetKwic50
Kwic100
Kwic200

RecallF-measure
Accuracy
0.08 (Snip- 0.07 (Snip- 0.15 (Title)
pet)
pet)
0.10 (Html) 0.12
0.17 (Title)(Kwic200)
0.12
0.15
0.25
0.13
0.12
0.12
0.14
0.14
0.20
0.14
0.15
0.23
0.14
0.14
0.20
0.14
0.14
0.16

Note: for Tf-idf and Cosine, the maximum values were described.

Method We evaluated whether the assigned NDC numbers (top ranked scores)
were related to people by checking web pages by five values (5: very related; 4;slightly related 3: neutral; 2: not very related ; 1: unrelated).
Results and Analysis Table 3 shows the average values of relatedness. Title
was best again (3.41).
From the above results of Experiments 1 and 2, we consider Title was best
among six documents to extract relative index terms to assign NDC numbers topeople.
4.4

Experiment 3

We investigated how many NDC numbers should be assigned to people using
Title to develop a NDC-based people-search directory.

8

Harumi Murakami et al.
Table 3. Result of Experiment 2: Relatedness
Title Html Snippet Kwic50 Kwic100 Kwic200
3.41 2.87 2.77
3.15
3.02
2.91

Method We evaluated whether the top ten assigned NDC numbers were related
to people by checking web pages by five values (5: very related; 4: slightly related;
3: neutral; 2: not very related; 1: unrelated).
Results and Analysis Table 4 shows the cumulative relatedness for each rank
in Experiment 3. For example, the average value of top ranked numbers was
3.43, and the top and second ranked numbers was 3.23. The average value of the
top five ranked numbers exceeded 3.
We use NDC numbers not only to categorize people in a directory but also
to display labels for them. The values of the top one or two are obviously better
than the top three to five; however, if we only choose the top one or two, too
little information is provided by the labels. We analyzed the top three to fiveranked numbers and believe they are appropriate.
Table 4. Cumulative relatedness for each rank
1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th
3.43 3.23 3.17 3.12 3.04 3.00 2.98 2.94 2.91 2.89

In this paper, we extracted the top five ranked numbers to build a directory.
4.5

Experiment 4

We investigated how many people were found and the correct rates in eachcategory (division) to evaluate the category’s potential.
Method We counted how many people were found in each 100 category (division): 000-990. We also counted the correct people in each 100 category to
calculate the correct rate.
Results and Analysis Figure 5 shows the Experiment 4 result. Html was thebest for the number of people (5.47 people) and Title had the highest correct
rate (39%).

Assigning Library Classification Numbers to People on the Web
6.00

9

45%

5.47
39%

40%

5.00

Number of people

31%

4.16

33%

24%

30%

3.59

3.56

35%

3.14

25%

3.00
20%

2.18
2.00

Correct rate

33%
28%

4.00

15%
10%

1.00
5%
0.00

0%

Title

Html

SnippetNumber of people

Kwic50

Kwic100

Kwic200

Correct rate

Fig. 5. Number of people and correct rate by division

4.6

Experiment 5

We investigated the usefulness of our developed prototype using 14 subjects.
Method Our subjects were 14 undergraduate and postgraduate males whose
average age was 22.8.
Since comparing six directories is complicated for the subjects, we chose
three documents for our experiment: Title, Html, and Kwic50. We did not choose
Snippet because it showed no advantage from previous experiments, and Kwic50
was chosen from Kwics, because it seemed the best.
We asked them three questions: Q1, Q2, and Q3.
(Q1) Is the NDC number attached to the person appropriate? (3: appropriate;2: partially appropriate; 1: inappropriate).
The subjects evaluated pairs of NDC numbers and a person included in eachten category (division): 000 - 900 by checking HTML files. We used 110, 310,
and 810 for 100, 300, and 800 because there was no people in categories 100,
300, and 800. We calculated the averages for each category.
(Q2) Is the list of people appropriate for each category? (3: appropriate: 2:
partially appropriate; 1: inappropriate).
After question 1, the subjects evaluated the same ten lists in ten categories(000 - 900). We calculated the averages by each category.
(Q3) Rank the three people directory methods and explain why.
Finally, we asked the subjects for their overall comments.
Results and Analysis Figure 6 shows the question results.
For the Title, the average values for Q1 were 2.01, 2.07 for Q2 and 1.50 for
Q3. For all the questions, our prototype developed using Title was the best.

Harumi Murakami et al.
3.00

Appropriate value

3.00

2.50

2.36
2.01 2.07

1.91

2.00

2.01

2.50

2.10
1.87 1.87

2.00

Rank

10

1.50
1.50

1.50

1.00

1.00

Title

Html
Q1

Q2

Kwic50Q3

Fig. 6. Category evaluation by 14 subjects

71% (10/14) of the subjects ranked Title best for Q3. The following arecomments from two subjects who ranked Title best in Q3: “There is little uselessinformation and its system is easy to understand.” “The classification precision
is good.”5

Related Work and Discussion

5.1

Related Work

The initial idea of assigning library classification numbers to people was presented in a very short position paper [7]. In this new paper, we explain the algorithm and prototype in details and evaluate our algorithm and our developed
prototype. In addition, we discuss the similarities and differences from various
related work.
There is research that assigns labels to people. Wan et al. assigned titles(including vocations) [1], Ueda et al. assigned vocation-related information [2],Mori et al. assigned keywords to person clusters [3], and [5] extracts location
information to people. WePS-2/3 conducted competitive evaluation on person
attribute extraction on web pages [8]. No such research has assigned libraryclassification numbers to person clusters.
Some research suggests NDC numbers or other terms in libraries. Kiyota
et al. suggests LCSH subject headings and NDC numbers [9], and Ueda et al.
suggests BSH subject headings and NDC numbers according to user input. They
use web information sources as Wikipedia for matching without using relativeindex terms.
The automated subject classification of web documents is not new. Golub
reviewed approaches to automated subject classification of textual web documents in different research communities (machine learning, information retrieval,and library science) and classified them into four categories: text categorization, document clustering, document classification, and mixed approach [10].

Assigning Library Classification Numbers to People on the Web

11

Our work belongs in the document classification category because we employedwell-developed controlled vocabularies. Document classification is a library science approach.
Jenkins et al. organized web resources by DDC using simple classifiers [11].
They used a DDC thesaurus to match terms in documents. OCLC Scorpion is awell-known project that assigns DDC to web resources [12]. Our work resembles
their approach because it compares the selected terms from documents to beclassified with the terms in the vocabulary.
Frank et al. predicted Library of Congress Classifications (LCC) from LCSH
subject headings and built an LCC browsing interface for a database of scholarlyInternet resources [13]. They present a machine learning technique to assign
LCC numbers to LCSH subject headings. This work is classified into the forthcategory, a mixed approach [10]. They did not evaluate their interface.
5.2

Discussion

Our experimental results show that, among six documents, Title had the best
performance assigning NDC numbers to people on the web and developing a web
people-search directory.
We believe that our work’s main contribution is its successful assignmentof library classification numbers to people on the web for displaying labels andbuilding a people-search directory. To the best of our knowledge, this is the firstresearch that assigns library classification numbers to people on the web.
Our paper also presents the titles of web pages as good sources to form
virtual documents that represent people, which it does better than whole pages,
kwic documents, or snippets. The kwic concept resembles window size. In expert
searches, window sizes capture the proximity of terms and candidate mentions in
documents [14]. Our finding is quite different from expert searches. This reflects
the difference between the two tasks and provides new insights for web people
searches and other types of people searches.
Although our research is limited to NDC and Japanese, our approach is easily
applicable to other classification systems, such as DDC with similar organization
and relative indexes or other terminology. People are one representative entity,
and our approach can be applied to such entities as industries or place names.

6

Conclusions

To help users select and understand people, our method assigns Nippon DecimalClassification (NDC) to people on the web. We developed a prototype based on
this approach and evaluated the usefulness of our proposed method and directory. Extracting relative index terms from the titles of web pages outperformed
comparative methods.
Future work includes improving our algorithms for assigning NDC numbers
to people. Second, we need to develop other kinds of datasets (e.g., more people,
or famous/not famous people, etc.) to examine the effectiveness of the proposed
method.

12Harumi Murakami et al.

Acknowledgements. This work was supported by JSPS KAKENHI Grant
Number 22500219, 25330385.

References
1. Wan, X., Gao, J., Li, M., Ding, B.: Person Resolution in Person Search Results:
WebHawk. In: Proceedings of the Fourteenth ACM Conference on Information andKnowledge Management (CIKM 2005), pp. 163–170. ACM Press, New York (2005)
2. Ueda, H., Murakami, H., Tatsumi, S.: Assigning Vocation-Related Information to
Person Clusters for Web People Search Results. In: Proceedings of the 2009 Global
Congress on Intelligent Systems (GCIS 2009), vol. 4, pp. 248–253. IEEE Press, NewYork (2009)
3. Mori, J., Matsuo, Y., Ishizuka, M.: Personal Keyword Extraction from the Web.
Journal of Japanese Society for Artificial Intelligence. 20, 337–345 (2005)
4. Chan, L. M.: Exploiting LCSH, LCC, and DDC to Retrieve Networked Resources:
Issues and Challenges. In: Proceedings of the Bicentennial Conference on Bibliographic Control for the New Millennium, pp. 159–178. Library of Congress, Washington DC (2001)
5. Murakami, H., Takamori, Y., Ueda, H., Tatsumi, S.: Assigning Location Information
to Display Individuals on a Map for Web People Search Results. In: Song, D., Lin,
C.-Y., Aizawa, A., Kuriyama, K., Yoshioka, M., Sakai, T. (Eds.) AIRS 2009. LNCS,
vol. 5839, pp. 26–37. Springer, Heidelberg (2009)
6. Sato, S., Kazama, K., Fukuda, K.: Distinguishing between People on the Web with
the Same First and Last Name by Real-world Oriented Web Mining. IPSJ Transactions on Databases. 46, 8, 26–36 (2005)
7. Murakami, H., Ura, Y.: People Search using NDC Classification System, In: Proceedings of Fourth Workshop on Exploiting Semantic Annotations in Information
Retrieval (ESAIR 2011), pp. 13–14. ACM Press, New York (2011)
8. Artiles, J., Borthwick, A., Gonzalo, J., Sekine, S., Amigo, E.: WePS-3 Evaluation
Campaign: Overview of the Web People Search Clustering and Attribute Extraction
Tasks. In: CLEF 2010 (2010)
9. Kiyota, Y., Nakagawa, H., Sakai, S., Mori, T., Masuda, H.: Exploitation of the
Wikipedia Category System for Enhancing the Value of LCSH. In: Proceedings of
the 9th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 411, ACM Press,
New York (2009)
10. Golub, K.: Automated Subject Classification of Textual Web Document. Journalof Documentation. 62(3), 350–371 (2006)
11. Jenkins, C., Jackson, M., Burden, P, Wallis, J.: Automatic Classification of Web
Resources using Java and Dewey Decimal Classification. Computer Networks and
ISDN Systems. 30, 1-7, pp. 646–648 (1998)
12. Automatic Classification Research at OCLC, http://www.oclc.org/research/
activities/auto class.html
13. Frank, E., Paynter, G. W.: Predicting Library of Congress Classifications From
Library of Congress Subject Headings. Journal of the American Society for Information Science and Technology. 55(3), 214–227 (2004)
14. Balog, K. Fang, Y., de Rijke, M., Serdyukov, P., Si, L.: Expertise Retrieval. Foundations and Trends in Information Retrieval. 6, 2-3, 160 (2012)