Assigning Location Information to Display
Individuals on a Map for Web People Search
Results
Harumi Murakami1 , Yuya Takamori1  , Hiroshi Ueda2 , and Shoji Tatsumi2
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
Graduate School of Engineering, Osaka City University,
3-3-138, Sugimoto, Sumiyoshi, Osaka 558-8585 Japan

Abstract. Distinguishing people with identical names is becoming more
and more important in Web search. This research aims to display person
icons on a map to help users select person clusters that are separated
into diﬀerent people from the result of person searches on the Web. We
propose a method to assign person clusters with one piece of location information. Our method is comprised of two processes: (a) extracting location candidates from Web pages and (b) assigning location information
using a local search engine. Our main idea exploits search engine rankings and character distance to obtain good location information amonglocation candidates. Experimental results revealed the usefulness of our
proposed method. We also show a developed prototype system.
Key words: location information, Web people search, map interface,character distance, information extraction

1

Introduction

Finding information about people on the Web is one of the most popular search
activities. According to [1], 30% of all queries in Web searches include person names. Person name disambiguation, or distinguishing people with identicalnames, is becoming more and more important in Web searches. Most research
into person name disambiguation concentrates on automatically separating 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 locates person icons on a map to provide a user-interface tohelp users select person clusters that are separated into diﬀerent people fromthe result of person searches on the Web. We assign one piece of representative
location information to an individual. Fig. 1 shows the aim of this research and

Currently, Jupiter Telecommunications, Co., Ltd.

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Harumi Murakami et al.

a prototype interface. When a person name is input as a query, Web pages
are classiﬁed into person clusters, and individual icons that express appropriate
locations are displayed on a map. Users can select icons to display their location
information to access searched Web pages.
In this paper, we assign one piece of location information to person clusters.

Fig. 1. Research aim

Many services extract location information from Web pages [2]. Most extract addresses using pattern-matching algorithms. For example, the following
sequence, number, avenue, city, is treated as an address. These systems extract
all such information from a Web page. However, existing systems have problems satisfying our requirements. First, they don’t judge which address is themost suitable for particular people. Second, they don’t treat multiple Web pages.
Third, they cannot extract location information when addresses are not includedin Web pages.
We propose a method to assign one piece of location information that is suitable for an individual using Yahoo! local search API [3] (hereafter Local search).
Local search is one geocoding service that returns location coordinates based onan address or landmark query. For example, when a user inputs the following as
an address query, 6-10-1, Roppongi, Minato-ku, Tokyo-to, Local search returns
10-1, Roppongi-6-chome, Minato-ku, Tokyo-to as an address, 35.65716694 as alatitude, and 139.73245194 as a longitude.
Our main ideas are as follows. First, we utilize landmarks as well as addresses
to extract location information even from Web pages without addresses. Second,
we use search engine rankings and character distance to assign suitable location
information. Third, we use Local search to convert texts included in Web pages
to location information. To obtain one piece of location information from Local

Assigning Location Information for Web People Search Results3

search, we design several mechanisms. For the zero hit problem, we introduce
two heuristics: a one-character deleting heuristic when the text is an address
type, and a formal name inference heuristic when the text is a landmark type.
A candidate list is another mechanism to avoid zero hits. For the multiple hits
problem, we introduce a calculating context heuristic to select one result.
Below, in Section 2 we explain our method. The experimental results aredescribed in Section 3. We discuss our method’s usefulness and related workin Section 4. The examples in this paper were translated from Japanese into
English for publication.

2

Our approachThis research locates person icons on a map to provide user-interfaces to help
users select person clusters that are separated into diﬀerent people from theresult of person searches on the Web. We obtain the following set of location
information for person clusters: a location label, a location address, and a latitude
and longitude pair.
Table 1 shows the example results of this research. When a person cluster isinput, location information is obtained by Local search.

Table 1. Example results
Person cluster

Location Information
Location label
Location address
Lat/LonAsako Miura 3 (B) Arise Campus, Kobe Nishi-ku, Kobe-shi, 34.6.../135.0...
GakuinUniversity Hyogo-ken(landmark type)
Asako Miura 11 (F) 13-17, Shinmachi-1- 13-17, Shinmachi-1- 40.8.../140.7...
chome, Aomori-shi, chome, Aomori-shi,
Aomori-ken (address Aomori-ken
type)

For example, for the ﬁrst cluster, Asako Miura 3 (associate professor at Kobe
Gakuin University), a location label, Arise Campus, Kobe Gakuin University, alocation address, Nishi-ku, Kobe-shi, Hyogo-ken, and a latitude and longitudeset, 34.6.../135.0... are obtained. This location information is a landmark type.
For the second cluster, Asako Miura 11, the location label and the location address are 13-17, Shinmachi-1-chome, Aomori-shi, Aomori-ken, and the location
address is 40.8.../140.... This is an address type.
A method overview is shown in Fig. 2. Our method is comprised of two steps:(a) extracting location candidates from Web pages and (b) assigning location
information using Local search.

4

Harumi Murakami et al.
Input: person cluster (HTML Files)
Top

…

Rank
10

…

BottomTop 10 Pages(1) Extract location candidates

Location Candidate List
(2) Assign location information

Landmark
No result

Infer formal nameOne result

Execute local searchMore than one result

Calculatecontext

No result (address)

Delete one-character

Output: location information

Fig. 2. Overview

2.1

Extracting location candidates

When a person cluster (HTML ﬁles obtained from person searches) is given,location candidates are extracted and a location candidate list is generated.
Determining web pages using search engine ranking One diﬃcult problem in this research is identifying which information is the most suitable forthe designated person. If we only use one Web page, one very good piece of
information may be obtained, but much good information may be overlooked. If
we use all Web pages, all information might be obtained, but selecting the best
information may become unwieldy. In this research we need to examine which
Web pages should be treated. We propose using search engine rankings to determine Web pages to extract location information. We decided to use the top
10 Web pages to extract location information based on the experimental resultsin Section 3.
Extracting address and landmark candidates When Web pages are given,
after removing HTML tags, new line codes, and spaces from HTML ﬁles, we
extract address and landmark candidates based on heuristics and morphological analysis. We used MeCab [4] for morphological analysis. Two heuristics aredescribed below:
1. Extracting address candidate heuristic
When a morphone that meets conditions (a) or (b) appears continuously
more than once, we combine these morphones into a location candidate (address type): (a) whose type is judged location, prefix, or number by MeCabAssigning Location Information for Web People Search Results

5(b) which is included in the predeﬁned term list such as: hyphen, street,avenue, north, south, east, or west.
For example, 3-3-138, Sugimoto, Sumiyoshi-ku, Osaka-shi, Osaka-fu, is extracted as an address type candidate.
2. Extracting landmark candidate heuristic
We extract a morphone whose type is judged organization as a location
candidate (landmark type).
For example, City Hall of Kyoto City, Osaka Station, The University ofTokyo are extracted as landmark types.
Again, one diﬃcult problem in this research is identifying which informationis the most suitable for the designated person. Our solution uses the character
distance between location candidates and the designated person name. Character
distance is number of characters (including spaces) between location candidates
and the designated person names. For example, an original Web page contains
Asako Miura (Kobe Gakuin University) and the distance between a location
candidate Kobe Gakuin University, and person name Asako Miura becomes 2.
Address candidates whose character distance is less than 71 and landmark candidates whose character distance is less than 31 are extracted from the top 10
Web pages.

Sorting location candidates To avoid zero hits from a Local search, we order extracted location candidates by character distance to generate a location
candidate list. When the character distance between two candidates is identical,
those extracted from higher ranked pages become higher, and those extracted
from the upper part of pages become higher.

2.2

Assigning location information

When a location candidate list is given, our method obtains one piece of location
information using Local search. First, our method gives a location candidate fromthe top of the candidate list to Local search. When the candidate is a landmark
type, a formal name inference heuristic is processed to change the abbreviation
into a formal name to hit a Local search beforehand.
When only one result is obtained from Local search, it becomes the answer.
When more than one result is obtained, our method calculates the similarity
between the candidate and obtained results, and the most similar result becomesthe answer. We call this a calculating context heuristic. When the candidate isan address type and no result is obtained, the one-character deleting heuristic
is applied to modify the candidate to hit a Local search. When no result isobtained after these processes, the next candidate in the list will be processed.
When there is no answer after all candidates in the list are processed, none (nolocation information) is the output.

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Harumi Murakami et al.

Formal name inference heuristic When a location candidate is a landmark
type, a formal name inference heuristic is applied once to change the abbreviations into formal names to get the Local search results. The formal nameinference heuristic gives the candidate to the Yahoo! Web search API [5] (hereafter Web search) and gets the title of the ﬁrst result. The title is modiﬁed to aformal name using a heuristic based on a stop list.
For example, when the candidate is Todai (abbreviation of The Universityof Tokyo), it is given to a Web search, and the title of the ﬁrst result is The
University of Tokyo Homepage. After Homepage is deleted using a stop list basedheuristic, the candidate becomes The University of Tokyo.
One-character deleting heuristic When no result is obtained for the address
type candidate, the one-character deleting heuristic is repeatedly executed todelete the last character of the candidate and to repeatedly give it to Local
search until the following stop condition is reached. The heuristic stops when
the string no longer contains ku (town), shi (city), to, do, fu, or ken (these fourterms denote prefectures).
For example, if address query 6-10-1, Roppongi, Minato-ku, Tokyo-to obtains
no result from a Local search, the next query becomes 6-10, Roppongi, Minatoku, Tokyo-to. Then the next query becomes 6, Roppongi, Minato-ku, Tokyo-to.
Calculating context heuristic When more than one result is obtained from a
Local search, our method calculates context with a calculating context heuristicthat calculates the similarity between the location candidate and the obtained
results using vector space models. The most similar result becomes the answer.
For example, when the location candidate is The University of Tokyo, it isgiven to a Local search, and such multiple results are obtained as 1) The University of Tokyo, 2) The University of Tokyo Komaba Campus, 3) The University ofTokyo Cultural Development, and so on. We need to select a result. Our method
can select an answer based on the Web page context. For example, if a person
teaches at the main campus of The University of Tokyo, 1) is selected; if s/heworks at the Komaba campus, 2) is selected.
The similarity between the candidate and the results is calculated using acosine measure, based on a vector space model. Given candidate vector c andresults vector r, similarity sim(c, ri ) is deﬁned as follows:
t
j=1 wcj wijsim(c, ri ) = 
(1)
t
t
2
2
(w
)
(w
)
cj
ij
j=1
j=1
Here t is the number of terms, wcj is the weight of tj in candidate vectorc, and wij is the weight of tj in result vector r. The terms are deﬁned as thefollowing morphones: (a) those included in the candidate list (when the candidate
is an address type), (b) those included in the original Web page with a location
candidate (when the candidate is a landmark type), and (c) those included in
the Local search results. The weights of the terms are calculated by tf-idf. The

Assigning Location Information for Web People Search Results

7

result that is most similar and larger than 0.7 becomes the answer. When no
result is larger than 0.7, the top result becomes the answer.
2.3

ExampleThere are 14 people for query Asako Miura. For the ﬁve people (Person clusters 2,
3, 6, 8, 11) with location information in Web pages, 100% (5/5) was correct. For
nine people without location information in Web pages, no location information
was assigned for eight people. In other words, 88.9% (8/9) was correct.
Asako Miura 3 is currently an associate professor at Kobe Gakuin University.
There are several Kobe Gakuin University campuses, and our method outputs
the correct answer, Arise Campus, Kobe Gakuin University, where her oﬃce
is located. This good example shows the usefulness of our method. If we useall (i.e. 100) Web pages, the Faculty of Human Science, Osaka University will
be displayed. If we use the frequency method (see Section 3.1), the Faculty of
Human Science, Osaka University will also become the answer. Osaka University
is her previous aﬃliation where she worked for many years. Kyushu University,
which she occasionally visits for conferences, was the output from 15 pages. In
this case, Kyushu University had the shortest character distance. No answer
was provided when using the address method (see Section 3.1) because the Web
pages had no address information.
Fig. 1 is an example interface. Icons indicating each person on a map anda list of location labels are displayed. Asako Miura 3 becomes Asako Miura B
because person clusters with no location information assigned are not displayedon a map. When a user selects an icon or a list, information about the person isdisplayed with the location label: Arise Campus, Kobe Gakuin University. The
user can display the original Web page that includes Kobe Gakuin University(cached) and the search results.

3

Evaluation

3.1

MethodDataset The twenty person names used in related work [6] were selected as
queries. 100 HTML ﬁles were obtained for all 20 queries from Web searches[5]. Two subjects manually classiﬁed these Web pages into diﬀerent people. 151
people were found in all 100 Web pages. For 13 person names, diﬀerent people
existed, and for seven person names, only one person existed.
The subjects extracted the location information from the person clusters bychecking all Web pages. For 79 out of 151 people (52.3%), location information
existed, and there was no appropriate location information for 72 people. The
average number of Web pages for the former 79 people was 21.3 (SD=35.8) and
4.1 for the latter 72 (SD=14.6).
The evaluation measurement was as follows. First, current places (oﬃces orhomes) were judged as appropriate location information. When there was no8Harumi Murakami et al.

current place, past places (oﬃces or homes) were treated as appropriate location information. Places the person temporarily visited do not become location
information. An address type and a landmark type are equally satisfactory.

Comparative methods To evaluate the usefulness of search engine rankings
and to determine the number of Web pages, we examined the number of Web
pages (the top, top ﬁve, 10, 15, and 100). We call these methods: (a) top page,(b) top ﬁve pages, (c) top 10 pages (our method), (d) top 15 pages, and (e) top
100 pages.
To evaluate the usefulness of a combination of search engine rankings, landmarks, and character distance, we examined a method that only extracts addresses and a method that uses frequency to calculate location information. We
used the top 10 pages because the former evaluation revealed that 10 pages was
the best. We call the former (f) the address method (top 10 pages and only
extracting addressess) and the latter (g) the frequency method (top 10 pages
and using frequency).

Evaluation Two subjects evaluated the assigned location information by theabove methods. Precision, recall, F-measure, and total were calculated as follows:
number of people whose assigned LI is correct
precision =number of people whose LI is assigned
numberof people whose assigned LI is correct
recall =number of people whose LI is included in Web pages
F − measure = 2×precision×recall
precision+recall
total = number of correct answers
number of people
With Local search, an assigned address and an address included in an original Web page sometimes diﬀer. For example, when 3-3-138, Sugimoto-cho,Sumiyoshi-ku, Osaka-shi is included in a Web page and Local search returns
Sugimoto 3-Chome, Sumiyoshi-ku, Osaka-shi, we judged this correct. Anothersuch example is Fukuoka City, which is included in a Web page; Local search
returns an address of city hall of Fukuoka City.

3.2

Results and Discussion

Eﬀect of search engine ranking Fig. 3 shows precision, recall, F-measure,and total when the number of Web pages varies. Top 10 pages (our method)
performed best in recall, F-measure, and total. The top page result was particularly inferior in recall due to the lack of information from only one page. The
result of top 100 pages was also inferior on average for the following reasons: (a)location candidates included in lower ranked Web pages tend to be unrelated todesignated people, and (b) the possibility of incorrect answers increases as thenumber of pages increases. The above results show the usefulness of our method
to use top 10 Web pages as sources to extract location information.

Assigning Location Information for Web People Search Results

1.00
0.90

0.84

0.70

0.76

0.75

0.80

0.85
0.78

0.84

0.80

0.76

0.71

0.66

0.60

Precision
0.50

Recall0.40

F-measure

0.30

Total

0.20
0.10
0.00

Top pageTop five pages Top ten pages Top 15 pages Top 100 pages(our method)

Fig. 3. Eﬀect of search engine rankings

1.00

0.85

0.90
0.80

0.78
0.70

0.70

0.56

0.60

0.51

0.50

Precision
Recall

0.35

0.40
0.30

F-measure
Total

0.20
0.10
0.00

Our method

Address

Frequency

Fig. 4. Comparison with other methods

9

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Harumi Murakami et al.

Eﬀect of landmarks and character distance Fig. 4 compares other methods when the Web pages are in the top 10. Our method greatly outperforms theother methods in all evaluations. Comparison with the address method shows our
method’s usefulness using landmarks to extract useful information to assign location information. Comparison with the frequency method shows our method’s
usefulness to use character distance for person names rather than frequency.
Answer analysis We analyze the answers in detail. Out of 62 correct answers for assigned location information, 40 (64.5%) were landmark types and 22(35.5%) were address types. This result suggests the usefulness of using landmarks. For 40 landmarks, 19 (47.5%) were universities, 6 (15.0%) were prefectural governments, 6 (15.0%) were train stations, 3 (7.5%) were high schools, and6 (15.0%) were other. Most were public institutions. For people with answers, 12
were incorrect for assigned location information. 91.2% (11/12) chose incorrect
candidates by character distance, 8.3% (1/12) chose incorrect results from Local
search by calculating similarity. Inside the former 91.2% error, target candidates
were extracted, but other candidates were chosen by character distance (91.0%,
10/11) and target candidates could not be extracted (9.0%, 1/11). A typicalextraction error was that publishers were assigned because people with many
pages often wrote books or were discussed in books. Overall, the main reason forerrors included problems of (a) character distance and (b) extracting landmarks.

4

Related Work and Discussion

Wan et al. [7] also separated Web people search results and assigned titles toperson clusters. We assign location information to display person icons on a map.
Even though much work (e.g., [8], [9], [10]) separates Web pages into person
clusters, it seldom assigns labels to person clusters.
The WWW9 WePS-2 workshop [11] evaluated a technique to extract attribute information. This task extracts 18 kinds of attribute values for target
individuals whose names appear on each of the provided Web pages. However
location is not included in the evaluation due to its ambiguity.
Our work is related to such clustering search engines as Clusty [12] whichusually assigns keywords or phrases to a Web page cluster to help users select acluster based on such information as term frequency and URLs.
Google’s alternative to search results [13] displays multiple location information included in Web search results as an experimental service. It does not display
one piece of representative location information. It mainly extracts city names
and sometimes college names. We extract addresses (including city names) andlandmarks (including college names).
Most existing systems and research extract multiple addresses contained in
Web pages. Few systems extract landmarks. [14] extracted addresses, postal
codes, and phone numbers contained in Web pages and converts them into coordinates. It does not extract landmarks, and we do not extract phone numbers.
The discrepancy reﬂects diﬀerent system aims. [14] gathered as much location

Assigning Location Information for Web People Search Results

11

information as possible, but we aim to output one good piece of location information for many people.
Morimoto et al. [15] extracted address pairs and descriptive text from Web
pages and displays them on a map using a HTML structure. Much research utilizes HTML structure to limit the range of documents to judge the relationshipbetween an object and an address. Instead of using HTML structure, we exploit
character distance to judge the strength of the relationships. Our heuristics for
extracting addresses are related to [16] because they use a Japanese morphological analysis and utilize location attached to nouns. [16] does not extract
landmarks.
Many Web sites provide a facility that converts addresses or landmarks
into coordinates with geocoding services including a Local search using simple
pattern-matching. They return the ﬁrst result (coordinates) based on an exact
match. For example, when a user inputs Hanshidai (abbreviation of Osaka City
University) it may fail because no Hanshidai exists in the geocoding databases.
Our method copes with pattern-matching problems in three steps. First, a formalname inference heuristic (landmark type) and a one-character deleting heuristic(address type) are applied. Second, we use a candidate list to try another location candidate to get a result. Third, we apply a calculating context heuristic to
identify one piece of location information from multiple results.
One advantage of our method is that no special location dictionary is needed
because such existing tools and services as morphological analysis (MeCab),
Yahoo! local search, and web search APIs are combined. A formal name inference
heuristic, a one-character deleting heuristic, and a calculating context heuristic
are powerful enough to utilize Local search to get one result. Our algorithm
is simple and easy to implement. Although our algorithms are heuristic based,
we do not need to modify or add new heuristics; therefore systems are easy tomaintain.
The experimental results revealed that our method has the best recall, Fmeasure, and total performance among comparative methods. The above results
suggest our method’s usefulness. In addition, we successfully built a prototype
interface to select a person cluster on a map. Our method is only inferior in
precision to the top page method. If we need to build an interface with fewer
incorrect icons (regardless how few), the top page method can be selected.
The limitations of our evaluation include the following. Since our evaluation
is limited to Japanese, the proposed heuristics should be adjusted to other languages. We extracted addresses and landmarks, except for those addresses andlandmarks that are not contained in the MeCab dictionary and the Local searchdatabase.
Future work includes the following. First, we need to improve the extractionof location candidates. For example, we should remove publishers by using a stop
list. Second, new algorithms should be investigated to cope with errors caused bycharacter distances. One possible solution may be to combine character distanceswith term-frequency, HTML structure, and/or syntactic analysis.

125Harumi Murakami et al.

Conclusions

We proposed a method to assign person clusters with one piece of representative
location information to display person icons on a map. The following are themain ideas of our method: (1) using landmarks as well as addresses to extract
location information, (2) using search engine rankings and character distance toassign suitable location information, and (3) using Local search to convert texts
included in Web pages to location information.
The experimental results revealed that our method has the best recall, Fmeasure, and total performance among comparative methods. The above results
suggest our method’s usefulness. In addition, we successfully built a prototype
interface to select a person cluster on a map.

References
1. Guha, R., Garg, A.: Disambiguating people in search. Stanford University (2004).
2. Okilab.jp projects, http://okilab.jp/project/location/
3. Yahoo! Local search API, http://developer.yahoo.co.jp/webapi/map/localsearch/v1/
localsearch.html
4. MeCab, http://mecab.sourceforge.net/
5. Yahoo! Web search API, http://developer.yahoo.co.jp/webapi/search/websearch/v1/
websearch.html
6. Sato, S., Kazama, K., and Fukuda, K.: Distinguishing between People on the Webwith the Same First and Last Name by Real-world Oriented Web Mining, IPSJ
Transactions on Databases, 46, 8, 26–36 (2005).
7. Wan, X., Gao, J., Li, M., and Ding, G.: Person Resolution in Person Search Results: WebHawk, CIKM2005, Proceedings of the Fourteenth ACM Conference on
Information and Knowledge Management, 163–170 (2005).
8. Bekkerman, R., and McCallum, A.: Disambiguating Web Appearances of People
in a Social Network, WWW2005, Proceedings of the Fourteenth World Wide Web
Conference, 463–470 (2005).
9. Kozareva, Z., Moraliyski, R., Dias, G.: Web People Search with Domain Ranking,Text, Speech, and Dialogue, Lecture Notes in Computer Science, 133–140 (2008).
10. Artiles, J., Gonzalo, J., and Sekine, S.: The SemEval-2007 WePS Evaluation: Establishing a Benchmark for the Web People Search Task, Proceedings of the FourthInternational Workshop on Semantic Evaluations, 64–69 (2007).
11. Task
DeﬁnitionofAttribute
Extraction
Subtaskfor
WePS-2,
http://nlp.uned.es/weps/weps2/WePS2 Attribute Extraction.pdf
12. Clusty the clustering search engine, http:// clusty.com/
13. Google Experimental Search, http://www.google.com/experimental/
14. McCurley, K. S.: Geospatial Mapping and Navigation on the Web, WWW’01, 221–
229 (2001).
15. Morimoto, H., Fujimoto, N., Nagaya, T., Idehara, H., and Hagihara, K.: A System
for Web Retrieval of Address-Related Information, IEICE Trans. D, J90-D, 2, 245–
256 (2007).
16. Arai, I., Kawaguchi, Y., Fujikawa, K., and Sunahara, H.: Geocrawler; Web Indexer
for Store Search based on Geographical Information and Evaluation Information on
Personal Web Sites, IPSJ Journal, 48, 7, 2319–2327 (2007).