Vitae and Map Display System
for People on the Web
Harumi Murakami1 , Chunliang Tang1 , Suang Wang1 , and Hiroshi Ueda2
1

Graduate 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
ATR Creative Inc.
2-2-2, Hikaridai, Seika, Soraku, Kyoto 619-0288 Japan

Abstract. We present a system that displays a curriculum vitae witha map to understand people. Our method is based on the following processes: (1) creating curriculum vitae using related work [1], (2) extracting
the names of places where the person studied and worked from the vitae, (3) getting such location information as latitudes, longitudes, andaddresses from the place names using Google Maps API, and (4) displaying a vitae along with a map using Google Maps JavaScript API.
We developed a prototype and evaluated our algorithms that extract
place names and convert them into location information from web search
results for 56 person names.
Key words: web people search, curriculum vitae, resume, map display,
location information

1

Introduction

Due to an increase in the number of people about whom the web can provide
information, the popularity of web searches that identify people continues torise. Such detailed and organized information might provide valuable insight
into search targets.
In this paper, we develop an interface that extracts such detailed information
as personal histories and organizes it into a vitae with a map to understandpeople. We obtain important, relatively long-term location information for the
person and focus on such life stages as schools and workplaces.
Extracting schools and workplaces from web search results is difficult. We
must solve the following main problems: (a) finding an “event-time-place” tuple
for the person, (b) removing redundant information, and (c) obtaining location
information that matches the extracted place names.
For (a) and (b), we use related work [1], which creates a curriculum vitae
when a person’s name is input and gives web search results. We create a vitae
and extract place names (schools and workplaces) from it. For (c), we use Google
Maps API to obtain location information from the extracted place names.

2Harumi Murakami et al.

Below, we explain our approach and give examples of our implemented prototype in Section 2 and describe our experiments in Section 3. We discuss thesignificance of our research in Section 4. The examples in this paper were translated from Japanese into English for publication.

2

Approach

2.1

Overview

Our approach is based on the following processes: (1) creating curriculum vitae
using related work [1], (2) extracting the names of places where the person
studied and worked from the vitae, (3) getting location information from theplace names using Google Maps API, and (4) displaying a vitae that includes
location information along with a map using Google Maps JavaScript API.
Figure 1 shows an overview of our approach.

Input

Person nameWeb
Creating curriculum vitae

Section 2.2

Sections 2.4 and 2.5
Extracting schools and work places
Section 2.6

Getting location information

Section 2.6

Displaying a vitae and a map
OutputFig. 1. Overview

2.2

Creating Vitae

The related work [1] is based on the following: (1) extracting event sentences (acharacter string that includes at least one time and one event) using heuristics

Vitae and Map Display System for People on the Web3

and filtering them, (2) judging whether the event sentences are related to theperson by SVM mainly using the patterns of HTML tags, (3) classifying thesesentences into four categories (background, education, history, and awards) by
SVM, and (4) clustering the event sentences including both identical times andevents. The history categories contain event sentences except for the background,
education, and award categories. Finally, (5) such background information asdate and place of birth is generated.
Extracting Event Sentences First, sentences are extracted from web pages by
cutting-sentence heuristics and event sentences are selected from those sentencesusing sentence-extraction heuristics that include the time (or judging year) listed
below.
– The following cutting-sentence heuristics segment character strings:• those ending with period• those included in tr, li, h1-h5, title, p, or div tags without periods• those ending with br tags without periods– The following sentence-extraction heuristics extract sentences that include
time (judging year):• those including four sequential numbers (e.g., 2000)• those including an apostrophe with two sequential numbers (e.g. ’00)
• those including names of Japanese calendars with two numbers with year
Next, 21 stop words (e.g., copyright, post) and 20 stop patterns (e.g., expressing such detailed times as 2008 12-31 12:00:00) were used to filter unnecessary
event sentences.
Judging Relation to a Designated Person Event sentences are judged on
the degree to which they are related to a designated person using SVM.
Five person names including politicians and researchers were used as queries
and 200 search results (web pages) for each person name (i.e., 5 * 200 = 1,000)
were obtained from Google Web APIs. 7,211 event sentences were obtained
through the event-sentence-extraction process. They were judged manually and2,266 correct and 4,474 incorrect training data were obtained.
We learned 31 patterns for SVM as the training data. Example patterns
include: “The event sentence includes both the family and given names,” “thenearest h1 to h5 tags include the family and given names,” “the number ofnouns inside the title tag,” and “the first tr tag in the table tag includes thefamily name.”
Classifying Event Sentences into Four Categories Using the one-versusrest method of SVM, event sentences, which are judged to be related to a designated person, are classified into four categories: background, education, history,
and awards.

4Harumi Murakami et al.

We obtained 500 web pages that were expected to include profile information
for the training data: 200 for the query “personal history site:ja.wikipedia.org”,
200 for the query “personal history inurl:profile”, and 100 for the query “almamater site:read.jst.go.jp”. After the event-sentence-extraction process, 14,974
event sentences were obtained and classified into the four categories. We morphologically analyzed the event sentences and calculated the tf-idf values. The
idf values were the number of event sentences. The tf-idf values and the numberof terms were used to form SVM patterns. The non-linear SVM was used foreach category.
Clustering Event Sentences Including Both Identical Times and Events
Next we group event sentences with the same meanings. For example, we mustcombine the following sentences: “A entered X university in 1982” and “In April
1982, A enrolled at Univ X.” We must also distinguish the combined sentencesfrom such examples as, “A graduated from X university in 1986.” Since thesesentences share many words, standard clustering algorithms may not be able todistinguish them.
We focus on time and cluster event sentences. The tf-idf, cosine, and singlepath methods were used for the clustering. The following is the algorithm.
Step 1 Event sentences are obtained that include “year, month, and day.” Each
year-month-day cluster is generated, and event sentences are clustered in
year-month-day clusters.
Step 2 Event sentences, which include “year and month” and years/months
that are identical with the existing clusters, are clustered by the single-path
method.
Step 3 Event sentences, which include “year” and years identical with the existing clusters, are clustered by the single-path method.
Step 4 Each event sentence, which includes a “year and month” and does notbelong to any existing clusters, becomes a year-month cluster. The clusters
with identical years/months are clustered by the single-path method. Eventsentences that include “year” and years identical with the generated clusters
are clustered by the single-path method.
Step 5 Each event sentence, which includes a “year” and does not belong to any
existing clusters, becomes a year cluster. The clusters with identical years
are clustered by the single-path method.
Generating Background Information We extracted the dates of birth anddeath from the background category by simple heuristics.
– date of birth: select a cluster that includes the most frequent “date of birth,”“birthday,” or “being born” and extract a date as “date of birth” from thecluster.
– date of death: select a cluster that includes the most frequent “date of death,”or “being dead” and extract a date as “date of death” from the cluster.
– place of birth: extract the most frequent prefecture name in the cluster fromwhich the date of birth was extracted.

Vitae and Map Display System for People on the Web

5

Example of Created Vitae Figure 2 shows the process for creating a vitae
for Naoto Kan, a former Japanese prime minster. On the left is shown a createdvitae from the implemented related work [1]. The background, education, andhistory categories are displayed. The education and history categories include
event sentence clusters.
When a user selects [+], the clustered event sentences are displayed. When auser selects “L”, the original web page containing the event sentence is displayed.
Since the amount of event sentence clusters in a history category is large, morethan 11 clusters are hidden. When a user selects “more,” the hidden clusters will
be displayed.
[Background]

Education
[A]
[B]
[C]
[D]
[E]
[F]
! Removed (merged to F)[G]! Removed (merged to G)

Work! Removed (other’s workplace)! Removed (old place name and no
Google Maps API match)[H]! Removed (merged to H)! Removed (merged to H)[I]! Removed (no place name)! Removed (old place name and no
Google Maps API match)[J]! Removed (no place name)[K]

Created vitae by related work

Event sentences on final vitae

Fig. 2. Creating vitae for Naoto Kan

2.3

Issues of Using Vitae Created by Related Work

There are three main issues for extracting place names from the created vitae:
extracting (1) schools from the education category, (2) schools from the history
category, and (3) workplaces from the history category.
(1) Extracting schools from the education category is the easiest among thethree since the schools themselves are relatively easy to extract because character strings for them are typical and such events are also typical (e.g., enter,
graduate, transfer). The main problem here is filtering the same event sentences

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Harumi Murakami et al.

as those that weren’t filtered by the previous step (creating vitae). Identifying locations from ambiguous place names (e.g., universities with multiple campuses)
is another important problem; however, this paper ignores it.
(2) Extracting schools from the history category (as workplaces) is slightly
more difficult, since such events include various positions (e.g., RA, researchers,
professors), degrees, and institutes.
(3) Extracting workplaces from the history category is the most difficult
step in our research. This is why the related work used the “history” category
instead of the “work” category. Working history is ambiguous, and workplaces
are ambiguous as well. For example, when a politician joins a party, is thistreated as working history? If he is elected, is this working history? If so, what
are appropriate working places, party offices, private offices, or parliament, etc.?
Extracting schools and workplaces from event sentences are described in Sections 2.4 and 2.5.
2.4

Extracting Schools

The process for extracting schools is listed below.
Step 1 Event sentences are extracted that contain the following: “(a piece of)school,” or “(a piece of) graduation.”
Step 2 Morphological analysis was performed on the event sentences.
Step 3 School candidates were extracted from the event sentences.
3-a When nouns are judged to be organizations or locations in the event
sentences:
The following nouns are connected: those starting from being judged to
be organizations or locations and those ending at these noun phrases: (a)“graduate school” when it contains “graduate school;” (b) “university”
or “college” when it contains “university” or “college” but not “school;”(c) otherwise, “school.”e.g.) “Ube Municipal Kotohira Primary School”
3-b When no noun exists that is judged to be an organization or a locationin the event sentences:
The following nouns are connected (with backtracking) starting at thesenoun phrases: “graduate school,” or “school.”e.g.) “Azabu High School”
Step 4 Identical school candidates were filtered.
4-a The school candidates are grouped into three categories if they containthe following terms: (1) “enter,” (2) “transfer,” or (3) “graduation,” or“graduate.”
4-b The character strings of school candidates in each category are compared, and when all the characters of a school candidate are included inother school candidates, the shorter candidate is removed.
e.g.) “Tokyo Metoropolitan Koyamadai High School” and “Metoropolitan Koyamadai High School” are compared, and the latter is removed.

Vitae and Map Display System for People on the Web

2.5

7Extracting Workplaces

The process for extracting workplaces is listed below.
Step 1 Event sentences are extracted that contain the following keywords: “elected,”“position,” “resignation” (for politicians), “university,” “school,” “research”(for researchers), “join,” “traded,” “retirement” (for athletes), “role,” “being
in charge,” (for entertainers), “employed,” “joining a company,” “leaving acompany” (for company workers).
Step 2 Morphological analysis was performed on the event sentences.
Step 3 Event sentences are removed that do not belong to the designated person. When a noun is judged to be a “family name” that is different from thedesignated person with Japanese particles that link a subject with nouns,
the event sentence is removed.
Step 4 When one or more nouns are judged to be organizations in the event
sentences:
4-a When there is only one noun, it is extracted.
e.g.) “House of Representatives”
4-b With more than one noun, the nearest one to the keyword is extracted.
Step 5 When no noun is judged to be an organization in the event sentences:
5-a When “company limited” or “Co. Ltd.”are found, the nouns before andafter the character strings are connected.
e.g.) “Mikasakosan Co. Ltd.” and “Kurehaboseki Company Limited”
5-b When “minister” is found, a minister’s name is extracted from a minister dictionary.
e.g.) “Transportation Minister”
5-c When “mayor” or “governor” and nouns judged to be locations arefound, the nearest noun and “city” (for mayor) or “prefecture” (for governor) are connected.
e.g.) “Akune City”
Step 6 When “university,” “school,” or “research” (for researchers) are foundin the event sentences:
6-a The same process as in Section 2.4 was performed. For 3-b, the nounphrases of the starting point of the backtracking were “graduate school”
and “research center.”e.g.) “Graduate School of Information Science and Technology, Nara
Institute of Science and Technology” and “National Institute of Informatics”
2.6

Getting Location Information and Displaying Vitae and Maps

Each place name obtained in Sections 2.4 and 2.5 was converted to location
information (latitudes, longitudes and addresses) using the Google Maps API
v3.
Next, the vitae created in Section 2.2 was modified. We extracted the event
sentence clusters that include the above place names and location information

8

Harumi Murakami et al.

(Fig. 2 (right)). Seven out of the nine event sentence clusters in the education
category are extracted as A to G. The seventh event sentence clusters are filteredsince they are combined with the previous one. For the history category, many
event sentence clusters were removed. For example, the first-event sentence cluster was removed since there was no workplace for the designated person. The
second-event sentence cluster was filtered since there is no workplace, or exactly
speaking, there was an old work place name without a hit in the Google MapsAPI.
Although related work [1] failed to create work categories, this research successfully identified important work history by extracting workplaces.
Finally, a vitae that includes location information along with a map is displayed using Google Maps JavaScript API v3.
2.7

Prototype

Based on the person name input, we developed a prototype that displays (1)a vitae with background information, education, and work information that includes location information in the event sentences, and (2) a map on which icons
express places included in the event sentences.
When the user selects an event sentence in the vitae or an icon on the map,the event sentence, the place name, and the address hit in the Google Maps API
are displayed.
Figure 3 shows an example for Naoto Kan. Almost all (7/9, 78%) the event
sentence clusters in the vitae’s education category are displayed in the education
category, and the selected (19/41, 46%) event sentences in the vitae’s history
category are displayed in the work category.

Fig. 3. Prototype

Vitae and Map Display System for People on the Web

3

Experiment

3.1

Dataset9

We used the names (mostly famous) of 56 Japanese people: 17 politicians, 14
athletes, 12 entertainers, 10 researchers, 1 entrepreneur, 1 manga artist, and 1
historical figure. We obtained 50 web search results for each person name andremoved unrelated web pages. Our implemented [1] method generated 56 vitae.
We identified the correct data (place names) included in the vitae.
3.2

Experiment 1: Extracting Place Names

The following are the evaluation measures for extracting place names (schools
and workplaces):correct extracted place names
extracted place names(1)

correct extracted place names
correct place names to be extracted(2)

P recision =Recall =Note that correct place names are duplicated. For example, when person
A entered and graduated from X university, it counts as two in the education
category. If person B also entered X university, it counts as three in the education
category. If person A worked at X university, it counts as three in the education
category and one in the work category.
As stated before, since workplaces are ambiguous, we defined some guidelines
to judge them in this research.
In addition, we classified their levels of correctness. 1 is exact: extracted
place name is identical as the correct answer. 2 is partially correct: e.g., partof the correct answer, abbreviation for the correct answer. An example of level
2 includes “Marinos (soccer team name)” for “Yokohama F Marinos (complete
soccer team name).”Table 1 shows the result. The results of extracting schools are fine (Precision:
91-97% and Recall: 84-90%), however, those of the workplaces are insufficient(Precision: 65-73% and Recall: 70-90%). Since our guidelines for workplaces are
not strict enough, these figures show maximum values.
3.3

Experiment 2: Getting Location Information

We used the correct answers to get location information by the Google Maps
API v3.
We judged the top ranked results. For schools, 0.90 (53/59), and for workplaces, 0.53 (81/154) were correct.
The most common error for workplaces was the name of political parties,e.g., LDP. Party offices were sorted from north to south in the Google Maps API

10Harumi Murakami et al.
Table 1. Experiment 1 results
Precision
Recallp1
p2
r1
r2
Schools
0.91
0.97
0.84
0.90
(59/65) (63/65) (59/70) (63/70)
Work
0.65
0.73
0.70
0.90
places (154/241) (166/241) (154/233) (166/233)

and the northernmost offices were top ranked. However, most correct offices arelocated in Tokyo, in central Japan.
If school names were correctly extracted, Google Maps API returned goodresults. For workplace names, the result was poor. We need to try different
geocoding services or consider better algorithms and create dictionaries using
Google Maps API.

4

Related Work and Discussion

WePS-3 conducted a competitive evaluation on person attribute extraction on
web pages [2]. Schools were included in the extraction candidates, but not workplaces since they are ambiguous.
Murakami et al. [3] only extracted “one representative” piece of location information, which is typically the most current places from the web search results.
Our research extracts schools and workplaces about the designated person.
Other research assigns information to learn about people. Wan et al. assigned
titles (similar to vocations) [4]. Ueda et al. assigned vocation-related information
including vocations, organizations, and works [5], Mori et al. assigned keywords[6], and Murakami et al. assigned library classification numbers [7] to person
clusters. This research is part of a project to develop interfaces to select andunderstand people on the web [8].
Some research extracted event sentences from web pages. Kimura et al. [9]extracted event sentences to create personal histories and listed them. Our research creates vitae and displays schools and workplaces on a map.
Although related work [1] failed to create work categories, this research successfully identified important work history by extracting workplaces. Therefore,
we not only used the related work but also improved it.
Some geocoding services (e.g. [10]) convert place names to location information. In our survey, Google Maps API is currently the best service amongthem.
We believe that our work’s main contribution is that it displays study andwork places on a map with curriculum vitae for people on the web. To the bestof our knowledge, this is the first research to do so.
Although our research is limited to Japanese, the idea is easily applicable toother languages.

Vitae and Map Display System for People on the Web

11

Future work must improve the extraction of workplaces. We also need to consider algorithms to find exact workplaces for geocoding services. The algorithms
should be evaluated using different datasets (e.g., ordinary people).

5

Conclusions

We presented a system that displays curriculum vitae on a map to understandpeople. Our method is based on the following processes: (1) creating curriculum
vitae using related work [1], (2) extracting the names of places where the person
studied and worked from the vitae, (3) getting location information (latitudes,
longitudes and addresses) from the place names using Google Maps API, and(4) displaying a vitae along with a map using Google Maps JavaScript API.
We evaluated our algorithms that extract place names and converted them into
location information from 56 person name search results.
Acknowledgements. This work was supported by JSPS KAKENHI Grant
Number 22500219, 25330385.

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