Assigning Vocation-Related Information to Person Clusters for Web People
Search Results
Hiroshi Ueda1) Harumi Murakami2) Shoji Tatsumi1)
1)
Graduate School of Engineering, Osaka City University
2)
Graduate School for Creative Cities, Osaka City University
Abstract
Distinguishing people with identical names is
becoming more and more important in searches on theWeb. This research assigns useful labels to help users
select person clusters that are separated into different
people from the result of person searches on the Web.
We proposed a method to label person clusters withvocation-related information (VRI). VRI includes
broader terms that may not be considered vocations aswell as terms that are useful to infer vocations, notonly those rigorously defined as vocations. Our method
is comprised of two processes: (a) extraction of VRI
candidates using HTML structure and heuristics, and(b) VRI generation using term frequency, clusteringsynonyms, and calculation using a Web search engine.
Experimental results revealed the usefulness of our
proposed method.

1. Introduction
Finding information about people on the Web is oneof the most popular search activities. According to [1],30% of queries in Web searches include person names.
Person name disambiguation, or distinguishing people
with identical names, is becoming more and more
important in Web searches. Most research of personname disambiguation concentrates on automaticallyseparating Web pages for different people using
clustering algorithms. However, if the list of search
results is merely “person 1, person 2, …, and so on,”users have difficulty determining which person clusters
they should select.
This research assigns useful labels to help users
select person clusters that are separated into different
people from the result of person searches on the Web.
Our hypothesis argues that information about
vocation is important to identify different people. We
propose a method to label person clusters withvocation-related information (VRI) that includes
broader terms that may not be considered vocations
and terms useful to infer vocations, not only thoserigorously defined as vocations. In addition, detailed
information is more useful than a single vocation term.
For example, Professor of Graduate School ofEngineering, Osaka City University is often more
useful than just a single vocation term (researcher) to
identify a person. The examples in this paper weretranslated from Japanese into English for publication.
Table 1: People search results for “Suguru
Egawa.”
Person Clusters
Suguru Egawa 1
Suguru Egawa 2

Number of
Web Pages
589
126

Suguru Egawa 3

1

Generated VRI by
our Method
baseball player
scholar of Russian
literaturecommentator

Table 1 shows the example results for the person
clusters for query “Suguru Egawa (Suguru is a given
Japanese name and Egawa is a family name)” using
our method. For the first cluster (Suguru Egawa 1),
which includes 589 Web pages, VRI “baseball player ”
was generated.
Below, in Section 2 we explain our method. The
experimental results are described in Section 3. We
discuss our method’s usefulness and related work in
Section 4.

2. Method
We propose a method to extract vocation-related
information (VRI) from Web pages in person clusters.
VRI is information related to vocation, which is more
useful for identifying people. In this paper, a person’sname is composed of surname and given names.
We selected three types of VRI: (1) vocation, (2)organization and position, and (3) publication title androle. Examples of vocation types are researcher,lawyer, and doctor. Examples of organization andposition types are Professor of Graduate School ofEngineering, Osaka City University and Vice presidentof ABC Corporation. Examples of publication title androle types are author of Dragon Ball and translator ofHarry Potter and the Philosopher's Stone.
The method is comprised of two processes: (a)extraction of VRI candidates using HTML structureand heuristics, and (b) VRI generation using term
frequency, clustering synonyms, and calculation usinga Web search engine.
An overview of our method is displayed in Figure 1.
Person Cluster (Web Pages)
Generated as a result of
Person searches on the Web
Extraction of VRI Candidates
Extraction of Nouns
Noun / ISBN
Extraction of VRI Candidates
Vocation

Position Role Organization

Publication

Generation of VRI
MixingClustering Synonyms
Vocation
Candidate
Cluster

Organization
Candidate
ClusterISBN

Calculating using a
Web Search Engine
final candidate
Judging Types(a) Vocation(b) Position

Getting Titles(c) Role

Organization

Vocation

Title

unifying

unifying

Organization+Position

Title+Role

Vocation-Related Information (VRI)

Figure 1: Overview of method.

2.1. Extraction of VRI candidates
This process identifies nouns that are related topersons from Web pages using HTML structures andcandidates from these nouns using heuristics.
2.1.1. Extraction of nouns. First, our method extracts
nouns when person names are contained in <p>, <tr>,
first or second rows in <table>, <title>, <h1>, <h2>,
and <h3>.
Second, our method concentrates on the location of
person names and extracts the following: (a) nouns inbrackets () that appear immediately after person names
and (b) nouns that appear immediately before or after
person names.

Finally, ISBN numbers are extracted using regularexpressions and added as nouns to extract publication
title term candidates in Section 2.1.2.
2.1.2. Extraction of VRI Candidates. Our method
judges nouns to identify VRI candidates using
heuristics. Since this process concentrates on Japanese,
it must be modified for other languages.
(1) Vocation term candidates
17 heuristics were selected that concentrate on noun
suffixes, including the following examples: a noun that
ends with “SHI” (suffix that expresses professionals) isa vocation term candidate; a noun comprised of morethan three characters and ends with “dash” (suffix that
expresses professionals) is a vocation term candidate.
(2) Organization term candidates
Organization term candidates are judged by two steps.
In the first step, NExT (a Japanese NE tool) is used. A
noun (a) which ends with terms which are composed ofmore than one character defined as “organization” in
NExT and (b) whose number of characters is more than“the number of terms identified in process (a) plusone.” In the second step, 14 heuristics were described.
An example of heuristics is as follows: a noun whichstarts with “KABU” (abbreviation of corporation) andcomprises of more than five characters is anorganization term candidate.
(3) Position term candidates
15 heuristics were selected that concentrate on noun
suffixes, including the following examples: a noun that
ends with “IN” (suffix that expresses being a memberof) is a position term; a noun comprised of more than
three characters and ends with “professor” is an
position term candidate.
(4) Publication title term candidates
ISBN numbers are treated as publication title term
candidates.
(5) Role term candidates
Three heuristics are created: nouns that end with“author”, “editor”, or “translator” are identified as roleterm candidates.

2.2. VRI generation
The process of VRI generation engenders VRIs
from candidates. Our method is based on term
frequency, clustering synonyms, and calculation usinga Web search engine.
2.2.1. Clustering synonyms. Our method mixes
vocation candidate terms, position candidate terms, androle candidate terms and clusters synonyms into
vocation candidate clusters.
It also clusters synonyms into organization
candidate clusters from organization term candidates.

Figure 2 displays the process of generating vocation
candidate clusters.
The process of generating organization candidate
clusters is only different in Step 2 from that of vocation
candidate clusters. Suffix is changed to prefix.
Step 1. Select the shortest candidate and generate acluster from it.
Step 2. Compare suffixes of candidates that do notbelong to any cluster by pattern-matching. If matched,
the candidate is included in the cluster.
Step 3. Repeat Steps 1 and 2 until all candidates areprocessed.

Figure 2: Generating vocation candidate
clusters.
2.2.2. Calculation using a web search engine. Firstly,
our method selects clusters in which the number ofterm frequency is maximum from vocation candidate
clusters. If there is one candidate term in the clusters,
the candidate is selected as the final candidate. If there
is more than one candidate term, one candidate term isselected from the following process. We use a Web
search engine to calculate the weight of the candidate
based on Mori’s method using a Jaccard index [2]. The
weight of candidate J(n,v) between person name n andcandidate v is calculated as follows:J ( n, v ) =| N ∩V || N | + |V | − | N ∩V |(1)

|N| is the number of Web search results when a query is n. |V|is the number of Web search results when a query is v. |N∩
V| is the number of Web search results when a query is n andv.

The highest candidate is selected as the final
candidate.
Second, we identify the type of final candidate,
perform a different process based on the type, andgenerate a VRI. There are three types in the final
candidate: vocation term, position term, and role term.
If the final candidate is a vocation term, it directly
becomes a VRI.
If it is a position term, whether it includes
organizational information must be determined using amethod described in Section 2.1.2. If the position term
already includes organizations like professor of Osaka
City University, it becomes a VRI. If it does notcontain organizational information, the subsequent
process is done.
Our method obtains an organization term candidatefrom organization candidate clusters and selects
clusters in which the number of term frequency ismaximum from organization candidate clusters. If
there is one organization term candidate in the clusters,
it is unified with the final candidate (position term) andbecomes a VRI. If there is more than one organization
term candidate, one organization term candidate is
selected by the following process. We use a Web
search engine to calculate the weight of the candidate
based on Mori’s method using a Jaccard index [2]. The
weight of candidate J(n,o,v) between person name n,organization candidate o, and position candidate v is
calculated as follows:

J (n, o, v ) =| N ∩ O ∩V || N ∩ O | + |V | − | N ∩ O ∩V |(2)

|N∩V| is the number of Web search results when a query isn and v. |O| is the number of Web search results when aquery is o. |N∩O∩V| is the number of Web search results
when a query is n and o and v.

The highest candidate is selected as an organization
candidate, unified with the position candidate, andbecomes a VRI.
If the final candidate is a role term, our method
selects one publication title term from the publication
title term candidates. We select the ISBN number thatis closest to the person’s name and get the title of theISBN number using Amazon Web Service (AWS). The
publication title and final candidate (role term) are
combined and become a VRI.
2.2.3. Example. Figure 3 illustrates the process of VRI
generation when a person is a Cultural Affairs
Department journalist, Tozai newspaper.
If we take maximal frequencies from the process
results of extracting VRI candidates (vocation,
position, and role term candidates), voice actor would
become the answer because its frequency is maximal(6). However, that is wrong.
Our method of clustering synonyms creates
vocation candidate clusters. A vocation candidate
cluster whose seed is journalist and with sevencandidates becomes the maximal frequency (11).
Seven vocation candidate terms are examined by
calculation using a Web search engine, and Cultural
Affairs Department journalist becomes the final
candidate. Since it is judged to be a position, we needto get an organization term.
If we take maximal frequencies from the process
results of the extracted VRI candidates (organization
term candidates), gourmet club or microcomputer club
would become the answer since their frequency ismaximal (6). However, they are wrong.
Our method of clustering synonyms creates
organization candidate clusters. Three organization

candidate clusters become the maximal frequency.
Four organization term candidates are examined by
calculating with a Web search engine, and Tozai
newspaper becomes the organization term.
Finally, Tozai newspaper and Cultural Affairs
Department journalist are combined and Cultural
Affairs Department journalist, Tozai newspaper isgenerated.
The advantage of our method is the ability togenerate correct and detailed VRIs, including Cultural
Affairs Department journalist, Tozai newspaper
instead of such wrong and simple information as voiceactor.
Process Vocation
Clustering synonyms

Calculation(journalist)(lazy journalist)(“rookie” newspaper journalist)[11](Cultural Affairs Department journalist)(Cultural Affairs Department journalist, Tozai
newspaper)(Cultural Affairs Department journalist, Tozai
News)(coworker journalist)(Deputy General Manager)(Deputy General Manager Tomii)(Deputy General Manager, Cultural Affairs
Department, Tozai News)* (voice actor)[ 6](Cultural Affairs
Department journalist)[0.01036](coworker journalist)[0.00404](journalist)[0.00318](Cultural Affairs
Department journalist,
Tozai newspaper)[0.00028](Cultural Affairs
Department journalist,
Tozai News)[0.00021](lazy journalist)[0.00004](“rookie” journalist)[0.00003][ 6](person in charge)[ 4](gourmet)[ 3]

Cultural Affairs
Department journalist

230 people were found in Web search results (google
Web APIs) for twelve person name queries. The
average number of different people was nineteen andthe average number of web pages was 475.

3.2. Experiment 1
3.2.1. Method. Three person names were selected for
queries from the dataset. They are researchers and thenumber of web pages is intermediate. The above threemethods (a, b, c) were applied to the search results ofthe three queries. For 32 people, VRIs were generated
with all three methods.
Three subjects evaluated whether the generated
VRIs expressed the vocations of the people as follows.
- First, they manually extracted vocation terms or
VRIs by checking all Web pages divided into
32 people.
- Second, they evaluated the related value to thegenerated VRIs by referring to the above
manual results as follows: 2 (The term is thevocation of the designated person), 1 (The term
is not the vocation, but is related to thedesignated person’s vocation), or 0 (The term is
not related to the vocation of the designatedperson at all).
We judged the data with the related values of 2 and
1 to calculate precision.

Process Organization
Clustering synonyms

Calculation(Tozai newspaper)[ 6](Cultural Affairs Department, Tozai newspaper)* (gourmet club)* (microcomputer club)[ 6](Cultural Affairs Department, Tozai News)[ 4](beautiful car club)(Tozai newspaper)[0.03245](Cultural Affairs
Department, Tozai
newspaper)[0.00622](gourmet club)[0.00141](microcomputer club)[0.00138]

[ 6]

[ 4]

Tozai newspaper
VRI=
Cultural Affairs Department journalist, Tozai newspaper

Note: *: VRI candidates whose term frequencies are maximal
without clustering synonyms. []: the number of frequency of
VRI candidates in a cluster.

Figure 3: Examples of VRI generation.

3. Experiment3.1. Dataset
Twelve person names with different people out of
20 person names used in [5] were selected as queries.

Table 2: Results of experiment 1.

Relatedvalue
Precision(a)Ourmethod

(b)
Web
pages

(c) TF only

1.19

0.36

1.09

71.1%

24.4%

68.9%

3.2.2. Results and discussion. The results are shownin Table 2. For the average related values, our method
was highest (1.19) compared to Web pages (0.36) and
TF only (1.09). For the average precision, our method
was 71.1% (64/90), Web pages was 24.4% (22/90),
and TF only was 68.9% (62/90); our method washighest.
Our method is the best among the three methods inrelated values and precision. The comparison between
our method and Web pages shows the usefulness of
our method of extracting VRI candidates using HTMLstructure and heuristics. The results of our result and
TF only were similar. When the number of VRI
candidates was small, both methods tended to generate
the same terms. When the number of VRI candidates
was large, they tended to generate different terms.

3.3. Experiment 2

4. Related work and discussion

Experiment 2 investigated the usefulness of our
method compared to related work. We compared (a)our method with methods (b) and (c). (b) extracts titles
by pattern-matching. In the sentence, “David Lee is apainter,” painter is extracted as a title. The mostfrequent title is treated as a vocation. (b) is asimulation of [3]. (c) is a manual method. The subject
searches for the profile pages for the designated personand extracts the most suitable term as a VRI from theprofile pages by checking Web pages. Hereafter wecall (b) Title and (c) Profile.

This research assigns useful labels to help users
select person clusters that are separated into different
people from the result of person searches on the Web.
Wan assigns titles to person clusters using a method
similar to Title in Experiment 2 [3]. We assign VRIs,
which are more useful than titles.
Much work (e.g., [4, 5, 6, 7]) separates Web pages
into person clusters [8], however, it seldom assigns
labels to person clusters.
Technically, our idea is based on [2] in the sensethat it uses not only term frequency but also a Web
search engine to calculate terms.
Our work is also related to such clustering searchengines as Vivisimo, which usually assign one label toa web page cluster to help users select a cluster basedon such information as term frequency and URLs.
TSUBAKI [9] clusters synonyms like our work, but it
does not use Web search engines to calculate weights.
The main advantage of our method is to label VRIs
to person clusters based on the context, not vocations
based on term frequencies. For example, if a person is
on a faculty staff as both a researcher and a teacher,
our method outputs Osaka City University professor,
or computer science researcher based on the context inwhich the person appears on the Web, instead ofdisplaying a simple vocation word like researcher orteaching staff. In addition, no special vocation
dictionary is needed, which is another benefit of our
method.
The experimental results revealed the following. (1)The precision and recall of our method were 84.7%,
and 66.7%, respectively. (2) Our method has the best
related values, precision, and recall among theautomatic methods. (3) Our method features muchbetter recall against a manual method using profile
pages. The above results suggest our method’s
usefulness.
Future work includes the following. First, we needto improve the extraction of VRI candidates. Second,
when the number of Web pages is small, newalgorithms should be included. Third, our method
sometimes generates former vocations, which may be
acceptable if the person is famous; however, users
generally want current vocations. We need to explore
algorithms to identify times and evaluate which is
better to present to users.

3.3.1. Method. All twelve person names were used forthe queries. Three subjects evaluated the VRIs as inExperiment 1. Related values and precision were
calculated as in Experiment 1. Next, recall was definedand examined as follows:
recallnumber of persons whose
VRIs were relevant=
numbers of persons whose
relevant VRIs were extracted manually(3)

3.3.2. Results and discussion. The results are shownin Table 3. For the average related values, our method
was 1.41, Title was 1.17, and Profile was 1.76. For
precision, our method was 84.7% (94/111), Title was
66.7% (4/6), and Profile was 90.5% (19/21).
Concerning recall, our method was 66.7% (94/141),
Title was 2.8% (4/141), and Profile was 13.5%(19/141).
Table 3: Results of experiment 2.

Relatedvalue
Precision
Recall(a)Ourmethod(b) Title(c) Profile

1.41

1.17

1.76

84.7%
66.7%

66.7%
2.8%

90.5%
13.5%

Overall, our method outperformed Title. Although
our method was inferior to Profile in related values andprecision, it was much better in recall.
The above results suggest the usefulness of our
method as an automatic method. It also outperformed
Profile in terms of recall.

5. Conlusion
We proposed a method to label person clusters withvocation-related information.

The main advantage of our method is to label VRIs
to person clusters based on the context, not vocations
based on term frequencies. In addition, no special
vocation dictionary is needed. This is another great
benefit of our method.
The experimental results revealed the following. (1)The precision and recall of our method were 84.7%,
and 66.7%, respectively. (2) Our method has the best
related values, precision, and recall among theautomatic methods. (3) Our method features muchbetter recall against a manual method using profile
pages. The above results suggest our method’s
usefulness.

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