The 8th International Conference on Web Information Systems Engineering (WISE2007)

1. BEIRA: A geo-semantic clustering
method for area summary
Osamu Masutani, Hirotoshi Iwasaki
Denso IT Laboratory, Inc.
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2. Summary
Background
Concept
System architecture
Evaluation
Conclusions & Future works
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3. Background – Map service
Target
- Car navigation or PND (Personal
Navigation Devices)
- GPS mobile phone
- Web-based Map Service
Major functionalities of map
service
- View maps around current position
- Search route to destination
- Search favorite POI (Point of
Interests)
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4. A scenario : A visitor to Nancy
No previous knowledge about
Nancy.
- Japanese
- A little interest about Art
He has a free time.
- No plan.
- He can’t speak French.
- He has a GPS mobile phone.
The only available information is
from mobile map service.
- He’d like to search POIs using the service.
- What is a problem ?
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5. Use cases : Searching POIs on mobile
3 ways to search
Location based search
- Nearby area
Category based search
- “Restaurant” / “Italian” / …
- “Public” / “Library” / …
Keyword based search
- “chocolate cake”, “soccer”,
“beautiful”, “calm” , …
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6. Problem in location based search
Filtering by the specified area
Sometimes results are
numerous
- In central urban area
- Broad area is chosen
Selection is very hard
- UI is limited. (especially on mobile)
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7. Problem in category based search
Filtering by specific
category
Sometimes results are
numerous
- When the user doesn’t specify museum park
detail category
Information awareness
- Once the user chose “Museum”
category, he can’t find “Place Place
Stanislas”. Stanislas
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8. Problem of keyword based search
Filtering by keyword match
Information awareness Art nouveau
- The users is required to know about
the keyword in advance
- “Art Nouveau” is good keyword to
find Nancy’s features.
- But if the user mistakes the keyword
Place
Stanislas
for “Art Deco” the result will be poor
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9. Problems
Information overload
- Numerous candidates
- Millions of POIs in mobile phone service
Information awareness
- Both fixed category and free keyword
search have the similar problem.
museum park
Solution
- Reduce the candidates
- But keep information awareness
- Clustering and summarization of
information
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10. Clustering and summarization
Similar concept
- Web search engine “Vivisimo”
- Displays clustering result and
their topic of search results
- Dynamic category
Easy to choose but
comprehensive
- There are reduced number of
candidates but has
comprehensive view
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11. Is Vivisimo enough ?
It provides only semantic
(topic) view.
- With map service
- Switching between semantic and
geographic view will be complicated
Can these two views be
combined?
- Use only map view
- Cluster = area
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12. BEIRA :Bird’s Eye Information Retrieval Application
Topic based IR through geographic
view.
- Use AOI (Area of Interest) instead of POI
- AOI consists of area(cluster) and its summary
(the word list)
Area
Art Nouveau
Summary=word list
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13. System architecture
POI database
- Address of POI
- Text of POI (guide text, reputation text etc.)
Preprocessing
- Geo-coding and Topic vector generation.
Geo-semantic clustering and summarization
Display AOI
Geographic Latitude Longitude
preprocessing
POI Geo-semantic Geo-semantic AOI
database clustering summarization
Semantic
preprocessing Topic Vector
POI ID Address text Etc…
AOI ID Area Polygon Summary
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14. Implementation
Combinations of GIS and Text mining
tools
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15. Geo-semantic clustering
Geographic clustering doesn’t reflect area topics :
Circular area
Semantic clustering doesn’t consider geographic
view : Scattered area
Geo-semantic clustering solves these problems
Semantic Clustering G/S Clustering Geographic Clustering
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16. Geo-semantic clustering
Co-clustering with geographic and
semantic features
- Geographic feature : latitude, longitude
- Semantic feature : large dimension matrix (Latent
semantic indexing)
G/S ratio R: the combination ratio
- R =Geographic bias / Semantic bias
*R *1
Geographic Features Semantic Features
POI ID Latitude longitude LSI1 LSI2 LSI3
・・・ ・・・ ・・・ ・・・ ・・・ ・・・
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17. Evaluation : geo-semantic clustering
Dataset : Cafes in Shibuya
- Text contents : restaurants evaluation web site
“asku.com”
- 272 cafes in the region (Shibuya ward).
Correct cluster data
- Generated manually
- 13 clusters in the region
- F measure
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18. Results of clustering
Geo-semantic clustering produces non-
circular area according to its topic.
Semantic Geo-semantic Geographic
R=1.0E-04 R=1.0E-02 R=1.0E+06
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19. Evaluation of clustering
We confirmed geo-semantic
clustering is better than each solo
clustering
- Intermediate ratio (0.01) is optimal.
0.6
0.5
0.4
MLSA
0.3 Tensor-Kmeans
0.2
0.1
Semantic 1.0E-04 1.0E-02
0
1.0E+00 1.0E+02
Geographic
1.0E+04 1.0E+06
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20. Area summarization
Document summarization
Term weighting : ex. TF/IDF
- The term that occurs many times in a
document is important (TF term
frequency)
- The rare term in entire document set is
important (IDF inverse document
frequency)
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21. Problem of IDF
The simple IDF cannot extract regional
characteristic word
- According to IDF , “onion” and “wedding” have same weight
- “wedding” should be regarded as more important because the
area where wedding is held should be biased.
z Normal term Place name Area term
“onion” “Dogenzaka” “Wedding”
IDF
IDF 3.08 3.51 3.04
K 4.41 54.0 9.93
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22. Location aware IDF
The geographic distribution of word
- Term occurrence in the geographic space
More condensed is regarded as more important
- Measurement : K-value (point distribution analysis method)
IDF * K
z Normal term Place name Area term
“onion” “Dogenzaka” “Wedding”
IDF
IDF 3.08 3.51 3.04
K 4.41 54.0 9.93
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23. Evaluation of location aware IDF
Evaluation measure : Extraction rate of
location names
- The area characteristic terms has similar
distribution with location name
z Normal term Place name Area term
“onion” “Dogenzaka” “Wedding”
IDF
IDF 3.08 3.51 3.04
K 4.41 54.0 9.93
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24. Evaluation of location aware IDF
Evaluation data
- All words in Shibuya area.
- Top 1,000 weighted terms
Location aware IDF (IDF*K) efficiently
extracts location name than
conventional ones 30
25
density of location name[%]
20
IDF
15 K
IDF*K
10
5
0
1 100 200 300 400 500 600 700 800 900
rank
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25. Conclusions
BEIRA attacks the issues on map
service
- Information overload
- Information awareness
Geo-semantic combination of features
and processing can be used to make
area characteristics view.
Future works
- Automatic adaptation of G/S ratio
- Evaluation on other contents
Hokkai Takashima
(1850-1931)
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26. Thank you for your attention!
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