1. 6 ~ GIR

2. Motivation Former GIR :
capturing and handling geonames and associated feature
ignored other terms with important geographic connotation :
spatial relationship (in, near, on the shore of, etc)
feature type (cities, mountains, airports, etc)
there is disambiguation geonames
use a graph-ranking algorithm to analyse the captured feature and assign one single feature as the scope of each document
other partial geographic contexts of the document were ignored
incorrectly assigned scopes often lead to poor results

3. Problem Definition Rebuilt the query procesing module
all geographic information present on a query is captured
giving special attention to feature type and spatial relationship, as guides for the geographic query expansion
Using text mining methods to capture and extract disambiguate geonames from text
so that geographic scope can be inferred for each document

4. Objective
Generation of geographics signatures for both query (QSig) and documents (DSig)
DSig is generated for each document by a text mining module
QSig is generated through a geographic query expansion module
Geographic query expansion focused on feature, features type and spatial relationship
Geographic ranking improvement

5. New Architecture of Geographic IR
Topic titles as
query string

6. Geographic Ontology Using GKB 2.0 (Geographic Knowledge Base)
All modules rely on geographic ontology
support relationship between feature and feature type
a better property assignment for feature and feature type
a better control of information source
enrichment in physical domain, with the addition of new feature type
airports, circuits, and mountains, along with their instance

7. Statistic of Geographic Ontology

8. Query Processing (1) Geographic query parsing module
with the help of Geo. Ontology & manual-crafted
context rule
Split into <what, spatial relation, where>
Recognize feature and feature type
Features (ISO-19109) – an unambiguous location. It can be described by one or more placenames. For example: Paris.
Feature Types (ISO-19109) – classes of features. For example, island, mountain, lake (physical), city, continent, NUT-3 (administrative). A feature has only one feature type.
Relations – Links joining features OR feature types: part of, adjacent, capital of, etc. Examples: [Oslo] part-of [Norway], [city] part-of [country].
Example : Ship traffic in portuguese island
Ship traffic in portuguese island

9. Query Processing (2) Perform : Term Expansion
expand the thematic ~ what
Blind Relevance Feedback
Geographic Expansion
expand the geographic ~ where
based on query type
driven by spatial relationship, feature & feature type

10. Query Processing (3) Example : CLEF topic #74
Ship traffic in Portuguese island
Ship traffic : thematic part ~ what
in : spatial relationship
Portuguese : feature ~ grounded
geoname
Island : feature type
Mapped into the corresponding ontological concept

11. Geographic QE 1. Ship Traffic in Portugal 2. Ship Traffic in island
3. Ship Traffic in Portuguese island
Europe UK
Portugal 1
London
Lisbon 2 3
Isle of Wight
Isle of Man
Sao Miguel Isl
Madeira Isl.

12. Geographic QE (2) Scope of the interest :
All geographic concepts of type island that are part
of Portugal
QSig :
São Miguel, Madeira, Santa Maria, Formigas,
Terceira, Graciosa, São Jorge, Pico, Faial, Flores,
Corvo, Porto Santo, Desertas and Selvagens

13. Term Expansion (1) ~ Blind Relevance Feedback ~
Before Relevance Feedback

14. Term Expansion (2) ~ Blind Relevance Feedback ~
After Relevance Feedback

15. Text Mining (1)
Relies on a gazetteer of text pattern generated from the geographic ontology
Containing all concept represented by their feature name and respective feature type
[<feature type> <feature name>]
And
[<feature type> $ <feature name>]
parse the document for geoname
generating DSig
Example :
Lisbon Airport
Airport of Lisbon

16. Text Mining (2) Gazzeter : city $ Lisbon: 1 Lisbon city: 1
district $ Lisbon: 2
Lisbon district: 2
Street $ Lisbon: 3
Lisbon Street: 3
(...)
Lisbon: 1,2,3,(...)
LA :
5668[1.00];
2230[0.33];
4555[0.33];
4556[0.33];
4557[0.33]
LA :
5388[1.00];
5389[1.00];
5390[1.00];
12097[1.00];
6653[0.67] ID
ConfMeas
Normalized into [0,1]
Left side : text pattern
Right side : identifier of the geographic concept in ontology

17. Sidra Sidra5 : text indexing and ranking
module with geographic capabilities
based on MG4J
Generating Geo and Term Index
Based on QE Term – Query Signature
and GeoIndex – Term Index to rank
document result

18. Flow chart of searches in Sidra5

19. GeoScore Spatial Distance Similarity ~ AdjSim(s1,s2) ~ Population
~ PopSim(s1,s2) ~
10%
20%
20%
Spatial Adjacency
Similarity
~ DistSim(s1,s2) ~
50%
Ontology
Similarity
~ OntSim(s1,s2) ~
Geographic Similarity ~ GeoSim(s1,s2)
Geographic Score ~ GeoScore(s1,s2)

20. Geographic Score ~ GeoScore(s1,s2)

21. Example Computing of GeoScore

22. Document Scoring
Textual Scoring

23. Experiment Type

24. Experiment Result IR
GIR
IR / GIR
MAP Result

25. Conclusion
The best experiment setup is to generate an initial run with classic text retrieval, and use the full geographic ranking modules for the generation of the final run
GIR system is very dependent on the quality of the geographic ontology, and has some limitations in the text mining step

26. Terima kasih