SUMMARIZING AND VISUALIZINGWEB PEOPLE SEARCH RESULTS
Harumi Murakami1, Hiroshi Ueda2, Shin’ichi Kataoka1, Yuya Takamori1 and Shoji Tatsumi2
1

Graduate School for Creative Cities, Osaka City University, Japan
2
Graduate School of Engineering, Osaka City University, Japan
harumi@media.osaka-cu.ac.jp, d06tb001@ex.media.osaka-cu.ac.jp, gorennrakusaki@yahoo.co.jp
highdefensive@yahoo.co.jp, tatsumi@info.eng.osaka-cu.ac.jp

Keywords:

Web people search, Summarization, Visualization, Interface.

Abstract:

People search is one major search activity on the Web. If the list of people search results is merely “person1, person 2, . . . and so on,” users have difficulty determining which person clusters they should select. In
this paper, we present a project that summarizes and visualizes Web people search results to help users
select person clusters more easily. We explore three ways of summarizing people: (a) selecting terms fromthe extracted information, (b) combining the extracted information, and (c) obtaining information fromexternal databases referring to the extracted information. To visualize people, we present three types ofinterfaces: (a) tables, (b) two-dimensional space, and (c) map interfaces. We report the two results of theproject. (1) We investigated algorithms for distinguishing individuals with identical names and three waysof summarizing people: extracting keywords, prefectures and vocations; combining vocation-relatedinformation; and obtaining locations. (2) We developed prototypes to display separated individuals by threetypes of interfaces.

1

INTRODUCTION

People search is one major search activity on theWeb. According to (Guha and Garg, 2004), 30% ofqueries in Web searches include person names.
Person name disambiguation, or distinguishingpeople with identical names, is becoming more andmore important in Web searches. 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 helps users select person clusters
that are separated into different people from theresults of person searches on the Web.
Below, in Section 2 we explain the overview ofour project and describe the developed algorithms
and prototypes in Sections 3-6. Related work is
discussed in Section 7.

2OVERVIEW OF THE PROJECT

The aim of this project is to develop interfaces toselect and understand people on the Web.

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First, we create person clusters by distinguishing
individuals from Web people search results. Second,
we present interfaces to help users select andunderstand people.
The main feature of this research is summarizing
people. We extract various kinds of attributeinformation related to people. Instead of displaying
all extracted information like traditional information
extraction research, we present one representativepiece of information by referring to the extractedinformation.
For example for the first author, from thefollowing list, which is the one representative pieceof information?: faculty staff, researcher, professor,
Osaka City University professor, or computerscientist? We explore three ways to summarize
people: (a) selecting terms from the extracted
information, (b) combining the extracted
information, and (c) obtaining information fromexternal databases that refer to the extractedinformation.
We present three types of interfaces to visualize
people: (a) tables, (b) two-dimensional space inwhich similar individuals are displayed close
together, and (c) map interfaces. See Figure 1.

SUMMARIZING AND VISUALIZING WEB PEOPLE SEARCH RESULTS(a) Table interface
Overview

Occupation

Address

Harumi
Murakami 1

Professor ofOsaka CityUniversity

researcher

3‐3‐138,
Information
Sugimoto,
Sumiyoshi,
Osaka

KeywordHarumi
Murakami 2

Author of “…”

housewife「ガラスのシンデレラ」(c) Map
interface

Hokkaido; Lawyer

村上晴美3

因島写生大会参加

村上晴美4
司法書士
Harumi Murakami
4Osaka; researcher

Figure 2: Table interface.

Office of …; Sapporo…

Harumi Murakami 1
Information retrieval;
Professor of Osaka City…

(b) Two-dimensional space interface

Figure 1: Project overview.

3

INITIAL PROTOTYPES

We developed initial prototypes of a table and a twodimensional-space interface (Ueda et al. 2007) that
separate individuals from Google people search
results and automatically extract attributeinformation (keywords, prefectures, and vocations).
The table interface displays a list of people withattribute information (Figure 2), and the twodimensional-space interface displays person icons
with part of the extracted attribute information(Figure 3).

3.1

Algorithms

The algorithms that extract keywords, prefectures,and vocations are described here. They are based on
the type of summarization: (a) selecting terms fromthe extracted information.

3.1.1 Extracting Keywords
We extracted nouns near person names usingmorphological analysis, collocated, and ranked themwith TermExtract. For example, for the first author,
information retrieval was the top ranked keyword.

3.1.2 Extracting Prefectures
We extracted prefecture names by pattern-matching
using prefecture dictionaries from texts near theperson names. One of the most frequent prefectures
is displayed. For example, for the first author, Osaka
Prefecture was the answer.

Figure 3: Two-dimensional space interface.

3.1.3 Extracting Vocations
We extracted vocations by pattern-matching using
dictionaries from texts near the person names. The
vocation dictionaries were created from Wikipedia.
One of the most frequent vocations is displayed. For
example, for the first author, professor was theanswer.
Preliminary
experimentssuggestedthat
vocations and keywords were useful to select people.

4

DISTINGUISHING DIFFERENTPEOPLEWe investigated two approaches to separate different
people: non-hierarchical clustering and hierarchicalclustering.
First in the initial prototype, to simulate how
people separate Web pages, non-hierarchicalclustering (single path method) was examined. We
checked Web pages from the top of the search
results and added similar pages to the existingclusters. Since this approach produced too manyclusters (Ueda et al. 2007), we need to group them inthe future.
As an alternative, we examined a two-stepclustering method using person names to distinguishindividuals (Kataoka et al. 2008). The key idea wasto use person names to create initial clusters by641

ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligencecombining mechanisms. Our method was comprisedof two processes: (1) first step clustering based on
the co-occurrence of person names and (2) second
step clustering based on hierarchical clustering using
keywords. Overall, our method outperformed twobaselines and was ranked 2nd in the WePS (Artileset al. 2007) systems.
In seeking state-of-the art person namedisambiguation, since we have found it difficult toautomatically distinguish different people, wedecided to abandon person name disambiguation andconcentrate on summarizing and visualizing
manually separated person clusters. In the following
sections, we investigate two important bits ofattribute information: vocations (Section 5) andlocations (Section 6).

5

ASSIGNING
VOCATION-RELATEDINFORMATION

Although vocations are the most informative ways toselect people, defining vocations and obtaining good
vocation dictionaries was difficult (Ueda et al. 2007).
We therefore proposed a method to extract vocationrelated information (VRI) from Web pages in personclusters. VRI is information related to vocation,
which is more useful for identifying people (Ueda etal. 2009). We selected three types of VRI: (1)vocation, (2) organization and position, and (3)publication title and role.
The method is comprised of two processes: (1)extraction of VRI candidates using HTML structureand heuristics, and (2) VRI generation using term
frequency, clustering synonyms, and calculation
using a Web search engine. The method is based on
the type of summarization: (b) combining theextracted information.
For example, two pieces of extracted information,
Tozai newspaper and Cultural Affairs Department
journalist, are combined, and Cultural Affairs
Department journalist, Tozai newspaper isgenerated as a VRI.
The main advantage of our method is to label
VRIs to person clusters based on the context, notvocations based on term frequencies.
VRI can be used as one bit of attributeinformation (e.g., vocations, titles, occupations, orcompanies) and as one good source of an overviewof people.

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6ASSIGNING LOCATIONINFORMATION ON A MAP

This research (Murakami et al. 2009) aims to display
person icons on a map to help users select personclusters that are separated into different people fromthe results of person searches on the Web. We
proposed a method to assign person clusters with
one piece of location information.
Our method is comprised of two processes: (1)extracting location candidates from Web pages and(2) assigning location information using a local
search engine. The method is based on the type ofsummarization: (c) obtaining information fromexternal database referring to the extractedinformation.
For example, the location information of Arise
Campus, Kobe Gakuin University, is obtained fromYahoo! Local search using one of the extracted
terms: Kobe Gakuin University.
Our main idea exploits search engine rankingsand character distance to obtain good location
information among location candidates.

Figure 4: Map interface prototype system.

Figure 4 shows the prototype interface. When aperson name is input as a query, Web pages areclassified into person clusters, and individual icons
that express appropriate locations are displayed on amap. Users can select icons to display their location
information to access searched Web pages.
Experimental results revealed the usefulness of
our proposed method.

7

RELATED WORK

Most people search services (e.g., MyLife) provide
manually constructed database input by users oroperators. When a user types a person name as aquery, a list or table is displayed to select a person.

SUMMARIZING AND VISUALIZING WEB PEOPLE SEARCH RESULTS

Person name disambiguation is manually solved.
Ages, locations (usually states in US), titles, andcompanies are often displayed as attributeinformation.
Some services and research automaticallyexecute Web people search and display a list or atable. Zoominfo extracts and displays such attributeinformation as titles and companies. Wan et al.
(2005) also separated Web people search results andassigned titles to person clusters to provide a list
interface to select people.
We present three kinds of interfaces and explore
three types of summarization methods.
Even though much work (Artiles et al., 2007)
separates Web pages into person clusters, it seldom
assigns labels to person clusters. The WWW2009
WePS-2 workshop evaluated a technique to extract
attribute information. Although it aims to extract all
attribute information related to people, we assign
representative attribute information to person
clusters and call this summarization.
Concerning the visualization of people search,
Matsuo et al. (2006) visualizes a human network toselect people. Mori et al. (2008) presents aninterface design for people search that includes ahuman network and two-dimensional space. Our
work is different; we concentrate on summarizingpeople.
We showed a scenario for people search bynames; however, our proposal can be applied toother types of people search such as by keywords.
For example, Bill Clinton, George W. Bush, andBarack Obama are summarized and visualized by an
input query: president of US.

8CONCLUSIONSREFERENCES
Guha, V., and Garg, A., 2004. Disambiguating People inSearch. Stanford University.
Ueda, H., Murakami, H., and Tatsumi, S., 2007. A systemthat distinguishes different people with identicalnames on the Web and displays a list by attributeinformation. In The 21st Annual Conference of theJapanese Society for Artificial Intelligence. (CDROM).
Kataoka, S., Ueda, H., Murakami, H., and Tatsumi, S.,
2008. Two-step Clustering based on Person Names toIdentify Different People with Identical Names on theWeb. In The 22nd Annual Conference of the JapaneseSociety for Artificial Intelligence. (CD-ROM).
Artiles, J., Gonzalo, J., and Sekine, S., 2007. TheSemEval-2007 WePS Evaluation: Establishing aBenchmark for the Web People Search Task. In
Proceedings of the Fourth International Workshop on
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Ueda, H., Murakami, H., and Tatsumi, S., 2009. A
ssigning Vocation-Related Information to Person
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Proceedings of the 2009 Global Congress onIntelligent Systems (GCIS 2009), 4, 248-253.
Murakami, H., Takamori, Y., Ueda, H., and Tatsumi, S.,
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Wan, X., Gao, J., Li, M., Ding, B., 2005. Person resolutionin person search results: WebHawk. In CIKM2005,
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We presented a project that summarizes andvisualizes Web people search results to help users
select person clusters more easily. We explored
three ways to summarize people: (a) selecting terms
from the extracted information, (b) combining theextracted information, and (c) obtaining informationfrom external databases referring to the extracted
information. We present three types of interfaces tovisualize people: (a) tables, (b) two-dimensional
space, and (c) map interfaces. We reported theproject results.

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