A System for Exploring People on the Web
Using Subject Headings
Yoshitaka Shirakawa

Masayuki Shimokura

Takashi Hirata

Graduate School of Informatics
Osaka Metropolitan University
Osaka, Japan
xuanweiyue@gmail.com

Graduate School of Informatics
Osaka Metropolitan University
Osaka, Japan
st23953q@st.omu.ac.jp

Faculty of Liberal ArtsThe Open University of JapanTokyo, Japan
takash-h@sea.plala.or.jp

Harumi Murakami
Graduate School of Informatics
Osaka Metropolitan University
Osaka, Japan
harumi@omu.ac.jp
Abstract—We investigated methods of assigning National Diet
Library Subject Headings (NDLSH) to people for searching andidentifying them on the web. We used pattern-matching andcombined the following: (a) the top ten pages, (b) 100 characters
before and after a person’s name, (c) no synonyms, and (d) thedocument frequency and assigned ten NDLSHs to each person.
We developed a prototype system that visually explores NDLSHs
and people through network representation.
Index Terms—NDLSH, web people search, exploratory search,visualization by network

I. I NTRODUCTION
Seeking information about individuals is a very popular task
in web searches. Unfortunately, finding/identifying a person on
the web is difficult, especially when digging through similar
or identical names. When searching for information withwhich to identify a person, he/she must be characterized bya label. In this research, NDLSH, the National Diet Library
Subject Headings (https://id.ndl.go.jp/information/download/)
are assigned to people as labels. NDLSH’s subject headings
are the most authoritative in Japan. By assigning NDLSHs
to a person on the web, well-formed keywords are assigned
and exploratory searches can be conducted using broader,narrower, and related terms.
Shimokura and Murakami [1] previously investigated methods that assined NDLSH to people on the web and identified
one method with the best top-ranked results. However, tofind and identify people, multiple subject headings must be
assigned to them. In this paper, we present methods thatassign multiple NDLSHs to people on the web and develop
a prototype system that visually explores NDLSH-connectedpeople through network representation.
Below we describe our method that assigns multiple NDLSHs to people in Section 2 and our prototype system in Section
3.

II. A PPROACH
A. Previous work
Shimokura and Murakami [1] investigated methods forassigning NDLSHs to people on the web and evaluated themusing the highest results. Our study builds on this previouswork, which we discuss next.
1) Dataset: They used 20 Japanese names as queries froma related work [2] and obtained 50 web pages per query fromthe Google Custom Search API. They manually classified
these pages into different people and identified 80 separate
individuals. NDLSHs were assigned to HTML files for eachperson.
2) Methods: First, they extracted headings with variantterms (hereafter synonyms) and deleted the following: thosewith two or fewer single-byte alphanumeric characters, thosewith only one double-byte character, those containing (...),and those containing “--(two hyphens)” because they are lessimportant terms and/or relatively useless for text-matching.
Since longer strings provide more specific meaning, thisprevious work counted the terms (headings and synonyms)
from among the longer ones in the HTML documents without
tags under the following conditions: (a) and (b). For example,
when processing the “artificial intelligence” character string
in a document, the term “artificial intelligence” is counted but
not the word “intelligence.” After counting the terms (headingsand synonyms), the scores of the headings are calculated.
They prepared the following four types of combination
conditions:(a) Search rankings of web pages: five different patterns of
the top 1, 3, 5, 10, and all the pages for each person.
(b) Positions in HTML documents: nine patterns of title
only, full text, and the 20, 40, 60, 80, 100, 150, or 200
characters before and after a person’s name.
(c) Synonyms: three patterns without synonyms, using synonyms with identical weights as the headings, and usingsynonyms with a half of the headings.

(d) Document frequency of the headings and synonyms:
three patterns, doing nothing, multiplying document
frequency (df) / total number of used documents (N),and multiplying the total number of used documents (N)/ document frequency (df).
When these conditions are combined, they become 5 × 9 ×3 × 3 = 405 patterns.
A heading with the highest score is assigned to the corresponding person. If no heading is assigned, the answer
becomes “none”.
3) Evaluation of top results: They manually selected themost appropriate headings for each person (79 out of 80people) as correct answers and evaluated whether the assigned
headings are identical as the answers using five measures,including correctness (number of correct headings assigned/ number of people).
The best pattern for the whole group of 80 people was “(a)the top ten pages, (b) the 100 characters before and after theperson’s name (total 200 characters), (c) a half weight forsynonyms, and (d) df/N”. The pattern’s correctness was 26.3%(21/80).
We call this pattern the “highest best” and use it as thestarting point of our research.
B. Assigning multiple headings
To identify people, multiple subject headings must be assigned to them. In this paper, we present methods that assign
multiple NDLSHs to people on the web.
First, we investigated the highest best pattern. We manuallyevaluated the “relatedness” between the assigned headings andpeople by five values: 5: very related; 4: slightly; 3: neutral;
2: not very ; 1: unrelated.
Table I shows examples of the top ten results. Katsumi
Tanaka 00 is a database researcher, Seiko Hishinuma 00 isa veterinarian in a fictional manga, and Susumu Goto 00 is abioinformatics researcher. Both appropriate and inappropriate
headings are found in the results. Veterinary medicine (5thrank) for Seiko Hishinuma 00 and bioinformatics (3rd rank)
for Susumu Goto 00 are examples of such appropriate andcorrect answers.
We scrutinized the headings that both showed relatedness
scores of 1 and appeared more than once; consequently wefound that the major reasons for the occurrence was the useof synonyms. For example, “English (people)” was extracted
since “English (language)” in web pages was selected as aheading “English (people)” using synonyms.
As a result of our observations, we adopted a pattern that
stopped using synonyms from the highest best pattern, that is,
the “(a) top ten pages, (b) 100 characters before and after
the person’s name (a total of 200 characters), (c) without
synonyms, and (d) df/N.” We call this pattern “no synonyms.”
Table II shows examples of the top ten results of theno-synonym pattern. In it, we eliminated the inappropriate
headings caused by using synonyms and reduced the numberof inappropriate headings with value 1. Most unintelligible
strings consisting of katakana characters were deleted. ForTABLE I
H IGHEST BEST PATTERN
Katsumi Tanaka 00
database [5]university [4]information processing [5]academicorganization [3]engineering [4]object [2]tara [1]ten [1]
edit [2]LAN [3]average [3.0]

Seiko Hishinuma 00
animal [4]hamu [1]itou [1]Susumu Goto 00
chemistry [4]university [4]bioinformatics [5]doctor [4]education [4]veterinary medicine[5]public health [4]histidine [4]pseudonym [2]time, place [2]skiing [3]average [3.0]medicine [4]life science [5]database [4]English [1]Japanese [2]science [4]average [3.7]Note 1: (a) top ten pages, (b) 100 characters before and after person’s name,(c) a half weight for synonyms, and (d) df/N.
Note 2: [] denotes relatedness values. Italics denotes unintelligible strings
consisting of Japanese katakana characters.
TABLE IIN O - SYNONYM PATTERN
Katsumi Tanaka 00
database [5]university [4]information processing [5]engineering [4]object [2]technology [4]edit [2]research institute [3]explosion [2]student [2]average [3.3]Seiko Hishinuma 00
animal [4]veterinary
medicine[5]public health [4]Susumu Goto 00
chemistry [4]university [4]skiing [3]company [3]constitution [3]university [4]student [4]novel [3]doctor [4]average [3.7]life science [5]medicine [4]bioinformatics [5]database [4]creature [3]life [3]gene [4]average [4.0]education [4]Note 1: (a) top ten pages, (b) 100 characters before and after the person’s
name, (c) no synonyms, and (d) df/N.
Note 2: [] denotes relatedness values.

example, no terms have a value of 1 and no unintelligible
strings consist of katakana characters in Table II.
We evaluated two patterns, “highest best” and “no synonyms,” by cumulative relatedness (the average relatedness
values from the highest to the corresponding ranks) (Fig. 1).
The no-synonym pattern improved from the highest rank to
the 10th rank and its values exceeded 3.
When assigning multiple NDLSHs to people, the nosynonym pattern (the one with identical conditions other thansynonyms and without using them) outperformed the highest
best pattern. We allocated the top ten headings of the nosynonym pattern, based on a related work [3] that assigned thetop five NDC numbers with cumulative relatedness exceeding
3 to people.
III. P ROTOTYPE S YSTEM
We developed “People on the Web (PoW)”, a prototype
system for searching people on the web by visually exploringare assigned, are displayed. When a user selects Susumu Goto00, who is one of the persons to whom “database” is assigned,
his assigned headings are displayed in the right area, andsuch detailed headings as “bioinformatics” can be checked. Inaddition, these headings, which are displayed from Susumu
Goto 00 in the window and Noriko Nomura 13 to whom“gene” is assigned, will be displayed.
In this way, we can look for people by searching for related
headings from the entered keywords or other persons throughheadings related to them.

Fig. 1. Evaluation by cumulative relatedness

NDLSHs. People can be sought while exploring broader,narrower, and related heading terms. We assigned the top ten
NDLSHs to 80 people in the dataset using the no-synonympattern.
A. Facility
Entering a keyword in the search box produces a NDLSH
list that includes the keyword. When the user selects aNDLSH, it appears in the main window. Double-clicking on
the NDLSH displays a context menu. Selecting “NDLSH”
displays broader, narrower, and related terms of the heading,and selecting “people” displays people (icons) to whom theheading is assigned. When users select a person icon, they cansee the headings assigned to her in the right area. Doubleclicking on a person icon and selecting “subject” from thecontext menu shows the NDLSHs assigned to him. The objects(NDLSH and people) can be moved, enlarged, reduced, etc.
B. Example of Use

IV. R ELATED W ORK
To the best of our knowledge, no research assigns library
subject headings to people on the web except our previous work (e.g. [1]). The methods for assigning controlledterms like subject headings to documents can be divided into
with/without machine learning. Our research based on patternmatching falls into the latter classification.
In our previous work, we assigned people location information [4], vocation-related information [5], and libraryclassification numbers [3]. In this paper, we investigate amethod that assigns NDLSHs to the results of web searches tohelp users select and identify people on the web. Our prototype
system was developed by modifying our previous work [6], asystem that visually explored subject headings.
V. C ONCLUSIONS
We investigated methods of assigning multiple NDLSHs topeople for searching and identifying them on the web. We
developed a prototype system that visually explores NDLSHs
and connected people through network representation. Future
work will experiment with different datasets, explore various
methods using machine-learning, and evaluate the effectiveness of our prototype system.
Acknowledgments: This work was supported by JSPS KAKENHI Grant Number 19K12718.
R EFERENCES

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informationretrieval

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Susumu Goto 00
Katsumi Tanaka 00
Noriko Nomura 13
genechemistry
university
education
life science
medicinebioinformatics
databasecreaturelife
gene

Susumu Goto 00

Fig. 2. Screen example

Figure 2 shows a use example. NDLSHs were searched in an
exploratory manner from the heading “information retrieval.”
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