How Do Humans Distinguish Different People with
Identical Names on the Web?: A Cognitive Science
Approach
Harumi Murakami

Yuki Miyake

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

Aeon Delight Co., Ltd.
4-1-2, Shonohayama, Suzuka,
Mie 513-0834 Japan+81-59-375-0666

harumi@media.osaka-cu.ac.jp

kakarot2007d@yahoo.co.jp

ABSTRACTThis research investigates how humans distinguish different
people with identical names on the web to improve web people
search. We asked subjects to classify 20 pages of web peoplesearch results for each of 20 person names and analyzed theirdecision processes through questionnaire, protocol analysis, andinterview. We found that keywords, vocations, works (for a real
person, works are those made by the individual and, for a fictional
person, works are those in which the individual appears), facial
images, and the names of related people are important for
distinguishing individuals. We proposed a model for
distinguishing individuals and a knowledge-structure model basedon the experiment’s results.

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

General Terms
Human Factors, Algorithms, Design

Keywords
Web People Search, Person Name Disambiguation,
Distinguishing Individual Model, Knowledge-Structure Model

1. INTRODUCTION
The popularity of web people searches continues to rise as thenumber of people about whom the web provides information
increases. Finding information about people on the web is one of
the most common activities of Internet users [1]. Person namedisambiguation, or distinguishing people with identical names, isbecoming more and more important in web searches. There ismuch research that separates web pages automatically. Currently,
the accuracy of person name disambiguation is not sufficient.
We investigate how humans distinguish different people withidentical names on the web to improve web people search.
Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies are
not made or distributed for profit or commercial advantage and that
copies bear this notice and the full citation on the first page. To copy
otherwise, or republish, to post on servers or to redistribute to lists,requires prior specific permission and/or a fee.
CIKM’12, October 29–November 2, 2012, Maui, HI, USA.
Copyright 2012 ACM 978-1-4503-1156-4/12/10...$15.00.

We describe the method and results of our experiment in Sections
2 and 3. We propose a model for distinguishing individuals and aknowledge-structure model in Section 4. We discuss thesignificance of our research in Section 5.

2. METHOD
2.1 Overview
We used 14 subjects (9 males and 5 females with an average ageof 25).
We collected 400 web pages (HTML files) comprised of 20 web
people-search results for each of the 20 Japanese person names
used in a related work [2]. At least one of the individual names isfamous enough for the authors to identify; however, most are notknown to the subjects. We identified which page belongs to which
person by referring to Wikipedia, web pages, and so forth. A totalof 58 individuals were found in the 400 web pages. We developed
a site for the experiment.
We asked the subjects to classify the above 20 web pages for one
person name using our developed site. For each person name, weassigned two subjects. We analyzed their decision processes byquestionnaire, protocol analysis, and interview.

2.2 Procedure
For each person's name, the procedure of the experiment is asfollows.
(1) We first conduct a questionnaire survey to obtain knowledge
about the name (whether the subject knows at least one
individual with that name).
(2) The subject distinguishes the 20 web people-search results.
He or she assigns one or more individual numbers to a web
page while speaking out loud about what he or she thinks.
The individual for the first result is number one. The
procedure is recorded and analyzed by protocol analysis.
(3) After distinguishing pages, the subject answers thequestionnaire survey. The two instructions are listed below:(a) Rank and write down ten or fewer terms that areincluded in either titles, snippets, URLs, or web pages
that you think are important for distinguishing
individuals. These are called discriminative keywords.
(b) Write one term that you think is the most important forcharacterizing the individuals. You do not have to
extract this term from the titles, snippets, URLs, or web
pages. These are called characteristic keywords.

When the subject participates in an experiment of another person's
name, steps (1)-(3) above are repeated. When all experiments arecompleted for the subject, we conduct a final interview.

3. RESULTS AND ANALYSIS
3.1 Accuracy
We check whether the assigned person number per one page isright to determine accuracy, which was 81% (See Table 1).
Table 1. Number of individuals and accuracy.
Accuracy
Accuracy
NaNaNo.
No.
me
me
S1
S2
S1
S2
1
2 0.90 1.00
11
6 0.55 0.25
2
4 1.00 0.90
12
2 0.90 0.90
3
4 0.85 0.85
13
1 1.00 0.65
4
5 1.00 0.75
14
1 1.00 1.00
5
2 0.70 0.65
15
1 1.00 1.00
6
1 0.90 1.00
16
1 1.00 1.00
7
1 0.95 1.00
17
1 0.65 0.95
8
8 0.70 0.55
18
1 0.95 1.00
9
6 0.85 0.65
19
1 0.50 1.00
10
9 0.50 0.50
20
1 0.15 0.90
Total
58
0.81
Note: Name: person name; No.: number of individuals; S: subject.

3.3.3

We classified these terms into eight categories (keywords,
vocations, works, related person names, histories, images, URLs,
and place names) manually. The categories were set by analyzing
data. Although we instructed the subjects to provide descriptive
terms, some subjects answered with non-terms, such as photos or
URLs. For the former, we created an image category and, for thelatter, a URL category. Terms not classified into the sevencategories (vocations, works, related person names, histories,
images, URLs, and place names), were classified into keywords.
Figure 1 shows the result of this classification.
For the discriminative keywords, the top four were keywords (175,53%), vocations (72, 22%), works (27, 8%), and related personnames (24, 7%). 175 keywords were classified into those related
to vocations (128, 73%), works (12, 7%), hobbies (8, 5%), sites (6,3%), personalities (2, 1%), attributes (2, 1%), and others (17,10%). 72 vocations included 27 organizations and 12 positions.
For the characteristic keywords, the top four were keywords (59,48%), vocations (44, 35%), works (8, 6%), and related personnames (8, 6%). 59 keywords were classified into those related to
vocations (35, 59%), hobbies (7, 12%), works (5, 8%), sites (4,7%), attributes (3, 5%), personalities (2, 3%), and others (3, 5%).
44 vocations included ten organizations and four positions.

3.2 Known and Unknown Names

200

Among the 40 results (20 person names * two subjects) of thequestionnaire, eight results (20%) were for known names.

180
160

For real person names, the subjects answered that they did notknow these individuals directly, but knew of them through media
such as TV. For one fictional person name (a comic book
character), a subject answered that he was aware of this individual
by reading the comic.

120

We found that the rate of browsing sites differs between known
and unknown names. 35% of subjects browsed sites for known
names, while 64% for unknown names.

175

140
100
80

59

60

3.3.1

Overview

We counted the written discriminative and characteristic
keywords in the results of our questionnaire survey. The numberof discriminative keywords was 329 and that of characteristic
keywords was 124.

3.3.2

ExampleFor example, for the questionnaire result for individual 1(formally a well-known baseball player, now a sportscommentator) for person name 1, the discriminative keywords
were baseball, Yomiuri (name of a baseball team), monster(nickname), comment, Kobayashi (rival person's name), blank(known for blank day of contract), ball, Egawaru (coined-term),
and nine (number of players on a baseball team). One
characteristic keyword for the individual was baseball.

72
44
27

40

24
8

20

8

13

9

1

5 3

4 0

Related Histories Images
Peson
Names

URLs

Place
Names

1

0

Keywords Vocations Works

We think that, for known names, the subjects knew something
about at least one individual of that name and they identified that
person by looking at the titles or snippets of the page.

3.3 Discriminative and Characteristic
Keywords

Classification of Discriminative andCharacteristic Keywords

Discriminative

Charactristic

Figure 1. Keywords for overall results.

3.3.4

Classification of Discriminative andCharacteristic Keywords of Real or
Fictional IndividualsThis included 54 real individuals (121 written results) and four
fictional individuals (seven written results). We classified thediscriminative and characteristic keywords by real and fictional
individuals. Figure 2 shows the numbers of keywords, vocations,works, and related person names for the real and fictional
individuals.
For real individuals, the works are those made by that individual.
For fictional individuals, the works are those in which that
individual appears. For real individuals, the related person names
are those of other real individuals and, for fictional individuals,
the person names are those of other characters in the works inwhich the fictional individual appears.

162

180
160
140
120
100
80
60
40
20
0

14

3.5 Interview

13

12
10

57

8

67

5

6

44
21

4

20

4

7

6
4

4

2

2

1

0

0

Keywords

Vocations

Discriminative

Works

RelatedPeson Names

Keywords

Characteristic

Vocations

Discriminative(a) Real individuals

Works

RelatedPeson Names

Characteristic(b) Fictional individuals

Figure 2. Keywords of top four categories for real andfictional individuals.
For the real individuals, the results are similar to those of theoverall results. For both discriminative and characteristic
keywords, the top two are keywords and vocations followed byworks and related person names. 78% (127/162) and 61% (35/57)
were related to vocations for discriminative and characteristic
keywords, respectively.
The results for the fictional individuals differed from the realindividuals. The top four of the discriminative keywords are
keywords, works, vocations, and related person names. The top
three characteristic keywords are works, keywords, and related
person names with no vocation. 46% (6/13) and 100% (2/2) are
related to works for discriminative and characteristic keywords,
respectively.
Overall, from our questionnaire investigation, we can conclude
that keywords are most important for distinguishing individuals,
followed by vocations, then works, and then related person names.
When the individual is a real person, information related to theperson's vocation(s) is important. When the individual is fictional,
information related to works in which the character is included isimportant.
As some subjects spoke little during the experiment, we analyzed
20 data (20 person names * 1 subject). We extracted 242 elements
and classified them into about the same eight categories as in theprevious sections. See Figure 3.
120

98

55
30

40
20

23
2

(1) Facial images(a) There are facial images on the web sites in the top three
results for the person name (web pages).
(2) Contents(b) Vocations (including organizations) or personal
histories are written in snippets or listed on web sites.
(c) Achievements are listed on web sites.
(d) Keywords about vocations, personal histories, orachievements are located in titles, snippets, or web sites.
(e) There is a Wikipedia page.
(3) Quantity of information(4) Ease of separation(g) There are less than six individuals who share the same
name.
(h) There is no individual who is similar in vocations or
fields.

11

4. COGNITIVE MODELS

20
30
Keywords Vocations

From the experimental results of unknown person names, weanalyzed the features of individuals who are easy to identify. We
identified eight features and classified them into four categories(facial images, content, quantity of information, and ease of
separation).

Here, we define personal histories as events, including yearly
information such as birth date.

8060

3.6 Features of People Who Are Easy to
Identify(f) There are more than two search results.

3.4 Protocol Analysis

100

Characteristic answers included: “I browsed web sites because
there was no knowledge about unknown people to identify
individuals.” “I imagine something when I distinguishindividuals.” “It is important to identify individuals whether I get
information about faces, vocations, personal histories,
achievement, and organizations.” “I can identify people when I
find one notable achievement.” “Facial images are importantbecause I could obtain much information about the person.”

Works

Related
PersonNames

Histories

Images

Sites

Place
Names

Figure 3. Results of protocol analysis.
The importance of keywords, vocations, and works is the same asin our questionnaire analysis.
We found that checking face images is important (22 out of 23
images) to distinguish individuals, which we did not ascertain
through the questionnaire analysis. We also found that checking
sites that include the linked pages is important. When there islittle information about an individual, for instance, he or she
simply posted on news or blog sites, information about the sites isimportant to distinguish the individual. In addition, when the
person is an author and the result is an online book site (e.g.,
Amazon), these sites provide important clues.

Based on the experiments, we propose a model for distinguishing
individuals (Figure 4) and a knowledge-structure model (Figure 5).

4.1 A Model for Distinguishing Individuals
The human tendency is to check a people search list from the top
down. A person will observe the title, snippet, or URLs of the list
and judge whether he can identify the individual using aknowledge-structure. When he can identify the individual, heseparates the individual. When he cannot identify the individual
or he wants to check the content further, he browses the web site.
On browsing the site, he obtains information about the individual
and a knowledge-structure for the individual is created or
modified. He sometimes selects linked web pages to obtain more
information. After that, he separates the individual by checking
the knowledge-structure models for individuals (when he has
already created other knowledge-structure models).
The above process is repeated until the list is processed.

List of People
Search Results

Repeatuntil Endof ListIdentify
Individual?

Title, Snippets, URL(b) NoBrowse Site(c) Yes but
Want to Check

Get Information
about Individuals(a) YesCreate/Modify
Knowledge‐
Structure

Separate
Individual

Figure 4. A model for distinguishing individuals.

4.2 A Knowledge-Structure Model
A knowledge-structure model is knowledge about the individualthat separates him. A knowledge-structure model is divided into
two parts: knowledge about the individual and the site.
Knowledge about the individual has two parts: facial images andtext content. Text contents are divided into real and fictional
individuals constructed from knowledge about the individual andrelationships with other individuals. The knowledge on a realindividual includes keywords, vocations, works, and personal
histories, while the knowledge on a fictional individual includes
keywords and vocations.
Knowledge about a site includes structure of the sites, URLs, andlinked pages.

Work
Related
PersonName
KeywordWork

Vocation

History
Real Individual

Related
PersonName

Fictional Individual

Site Structure
Knowledge
about Site

6. CONCLUSIONS
We investigated how humans distinguish different people withidentical names on the web to improve web people search. We
asked subjects to classify 20 pages of web people-search resultsfor each of 20 person names and analyzed their decision processes
through questionnaire, protocol analysis, and interview. We foundthat keywords, vocations, works, facial images, and the names ofrelated people are important for distinguishing individuals. We
proposed a model for distinguishing individuals and a knowledgestructure model based on the experiment’s results.

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

[1] Artiles, J., Gonzalo, J., and Sekine, S. 2007. The SemEval2007 WePS Evaluation: Establishing a benchmark for the Web
People Search Task. In SemEval-2007 Proceedings of the FourthInternational Workshop on Semantic Evaluations. 64-69.

Facial Image

Vocation

We think our findings are useful (a) to consider person namedisambiguation algorithms, (b) to develop a person search userinterface, and (c) to develop a personal web to be distinguishedfrom other individuals.

8. REFERENCES

Knowledge
about Individual

Keyword

The following are our paper's main contributions. First, weinvestigated how humans identify different people with identicalnames on the web to obtain knowledge that is helpful for people
search. We found that keywords, vocations, works (for an actual
person, works are those made by the individual and, for a fictional
person, works are those in which the individual appears), facial
images, and the names of related people are important for
distinguishing individuals. Second, we proposed a model for
distinguishing individuals and a knowledge-structure model basedon the experiment’s results.

Linked Page
URL

Figure 5. A knowledge-structure model.

5. RELATED WORK AND DISCUSSION
The WePS-2 conducted a competitive evaluation on person
attribute extraction on web pages [3]. As named entity recognition(NER) is used in most approaches, Artiles et al. investigatedwhich document features contribute to person namedisambiguation and reported that NER only makes a small
contribution [4].
There is research that assigns labels to distinguish people. Wan etal. assigned titles (similar to vocations in this research) [5], Ueda
et al. assigned vocation-related information including vocations,organizations, and works [6], and Mori et al. assigned keywords
to person clusters [7].

[2] Sato, S., Kazama, K., Fukuda, K., and Murakami, K. 2005.
Distinguishing between People on the Web with the Same First
and Last Name by Real-world Oriented Web Mining. IPSJ
Transactions on Databases. 26, 26-36, in Japanese.
[3] Artiles, J., Gonzalo, J. and Sekine, S. Artiles. 2009. WePS 2
Evaluation Campaign: overview of the Web People Search
Clustering Task J. In 2nd Web People Search Evaluation
Workshop WePS 2009, 18th WWW Conference.
[4] Artiles, J., Amigo, E, and Gonzalo, J. 2009. The role of namedentities in Web People Search. In Proceedings of the 2009
Conference on Empirical Methods in Natural Language
Processing. 534-542.
[5] Wan, X., Gao, J., Li, M., and Ding, B. 2005. Person
Resolution in Person Search Results: WebHawk. In Proceedingsof the 14th ACM International conference on Information andknowledge management. 163-170.
[6] Ueda, H., Murakami, H., and Tatsumi, S. 2009. Assigning
Vocation-Related Information to Person Clusters for Web People
Search Results. In Proceedings of the 2009 Global Congress onIntelligent Systems. 4, 248-253.
[7] Mori, J., Matsuo, Y., and Ishizuka, M. 2005. Personal
Keyword Extraction from the Web. Journal of Japanese Society
for Artificial Intelligence. 20, 337-345, in Japanese.