The document discusses geo-tagging and spatial indexing of text-specified data, focusing on recognizing and resolving geographic locations in natural language descriptions. It details the processes of geo-tagging, such as comma group recognition and fuzzy geo-tagging, as well as different types of spatial indexing, including various indexing schemes like r-trees and kd-trees. Future work includes enhancing fuzzy geo-tagging methods and improving spatial index efficiency for better computational performance.

1. Geo-tagging & Spatial Indexing
of Text-Specified
Data
Speaker: Shiv Shakti Ghosh
(shivu@drtc.isibang.ac.in)
MSLIS – II yr
DRTC
INDIAN STATISTICAL INSTITUTE
BANGALORE
_

2. Focus of a GSE
• Geographic context embedded
in natural language
descriptions
• Place names ambiguous and
confused with names of
organizations, people ,
buildings and streets
• Interpretation of spatial
relationships(‘near’, ‘north’)
• Geo-relevance ranking
Where is ‘A’ located?
What is happening at ‘B’?
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3. Shiv Shakti Ghosh 3

4. Overview
1. Geo-tagging
 Steps for Geo-tagging
 Comma group Recognition
 Comma group Resolution
 Fuzzy Geo-tagging
 Examples of Geo-Spatial Resources
2. Spatial Indexing
 Types
 Pure text index
 Spatial primary index
 Text primary spatio-textual index
 Text index with spatial post processing
 R-tree
3. Related Work
4. Future Work
5. References
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5. Geo-tagging
• Enabling the spatial indexing of un-structured or semi-structured
text
• Recognizing textual references to locations known as toponyms and
resolving them
# Systems using geo-tagging are being constructed for processing text in a
wide variety of domains, web pages, blogs, encyclopedia articles, news
articles, tweets, spreadsheets.
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6. Steps for geo-tagging
• Comma group recognition-finding all textual
references to geographic locations(toponym)
• Comma group resolution-choosing the correct
location interpretation for each toponym
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7. Comma group Recognition
• Toponym Recognition
-Tokenization(Stanford NLP package)
-POS tagging(Tree tagger package)
-NER(Stanford NER package)
* Lat/Long Assignment
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8. Comma group Resolution
1. Prominence location interpretation
2. Proximity location interpretation
3. Sibling location interpretation
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9. Fuzzy Geo-tagging
• Final default sense assignment is removed
• Assignment of weight to each member of the set of possible
interpretations
• Summing of weights across all the articles
• Formation of single toponym with summed weight of
interpretations
• Addition of this toponym to previously resolved set of toponyms.
• Coverage of new set calculated and accordingly toponym is
accepted or rejected.
# Process yields satisfactory results only when we are tagging using
local lexicons.
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10. Examples of Geo-Spatial Resources
• GeoNames: It includes geographical data such as place names in various
languages, latitude, longitude, altitude and population collected from
several data sources. It currently contains over 8 millions geographical
names for around 7 millions unique places.
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11. Examples of Geo-Spatial Resources
Contd..
# FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS
Resource: Geopolitical ontology – Czech_Republic_the
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12. Examples of Geo-Spatial Resources
Contd..
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13. Spatial Indexing
• Multi-dimensional indices have been proposed for managing spatial
data, including grid indices, quad-trees, R-trees, kd-trees
• Hybrid indexing schemes that combine spatial indexing schemes
built using spatial attribute based on grid scheme and text indexing
• Geographic context is encoded in
the form of a document footprint Fd
• Formation of Query footprint Qd
• Search engine finds those documents whose footprints intersect
query footprint
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14. Types
Pure text index
Spatial primary index
• Text primary spatio-textual index
• Text index with spatial post-processing
• R-tree , Quad tree, K-d tree
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15. Pure Text Index
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16. Spatial primary index(ST)
• Each spatial cell
has a text index.
• Retrieve document
ids for query terms
lying in cells
intersected by
query footprint.
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17. Text primary spatio-textual index
bengal {D1,D2,D3,D7,D8,D9,D11,D13 }
bengal {R1(D1, D7);R2(D3, D11, D13); R3(D2); R4(D8,D9, D11)}
• For each term , store
spatial index of documents
containing the term.
• For each text query term ,
retrieve ids of documents
lying in spatial cells
intersecting the query
footprint.
• Cell1(DocList1);
Cell2(DocList2); ……..
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18. Text index with
spatial post processing
• Access text index with concept terms.
• Access spatial index with query footprint.
• Merge results- find intersection.
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19. R-tree
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20. Related Work
• Research project engaged in design of a search engine to find
documents and datasets relating to places /regions referred in a
query
• Funded through the EC Fifth Framework Programme
• A collaborative effort with six European partners
• NewsStand monitors RSS feeds from thousands of online news
sources and retrieves articles within minutes of publication
• Extracts geographic locations from articles using a custom-built
geo-tagger, and groups articles into story clusters using a fast
online clustering algorithm
• Retrieve stories based on both topical and geographic significance
• A system for extracting, querying, and visualizing textual
references to geographic locations in unstructured text documents
• Use of geo-tagger, GNIS(for US based entities), GNS(for non-US
based entities) , NLP tools
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21. Future Work
• Application of idea of fuzzy geo-tagging to geo-tagging
with comma groups
• To allow looser interpretations of prominence and
proximity for comma group interpretations
• Application of distance functions for calculating
relevance (eg; greater the overlap greater is the
relevance)
• To reduce storage cost of spatial indexes
• Improvement of response time
• Computational costs associated with having each
document associated with several different geographic
scopes
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22. References
1. Lieberman, Michael D. and Samet, Hanan and Sankaranayananan, Jagan.(2010) Geotagging:
Using Proximity, Sibling, and Prominence Clues to Understand Comma Groups
2. Lieberman, Michael D. and Samet, Hanan and Sankaranayananan, Jagan.(2010)Geotagging
with Local Lexicons to Build Indexes for Textually-Specified Spatial Data
3. Vaid, Subodh and Jones, Christopher B. and Joho, Hideo and Sanderson, Mark.
(2011)Spatio-Textual Indexing for Geographical Search on the Web
4. Martins, Bruno and Silva, Mário J. and Andrade, Leonardo.(2011)Indexing and Ranking in
GeoIR Systems
5. Lieberman, Michael D. and Samet, Hanan and Sankaranayananan, Jagan and Sperling,
Jon(2010)STEWARD: Architecture of a Spatio-Textual Search Engine
6. Teitler, Benjamin E. and Panozzo, Daniele and Lieberman, Michael D. and Samet, Hanan
and Sankaranayananan, Jagan and Sperling, Jon(2008)NewsStand: A New View on News
7. GeoNames. http://geonames.org/ Accessed 24 September 2014.
8. NewsStand. http://newsstand.umiacs.umd.edu/ Accessed 24 September 2014.
9. STEWARD: Spatio-Textual Search Engine. http://steward.umiacs.umd.edu/
Accessed on 24 September 2014.
10. Czech_Republic_the http://www.fao.org/countryprofiles/geoinfo/geopolitical/resource/
Czech_Republic_the Accessed 24 September 2014.
11. Giunchiglia, Fausto and Maltese, Vincenzo and Farazi, Feroz and Dutta, Biswanath .
Geowordnet: A Resource for Geo-spatial Applications
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23. Thank
Any Questions
you