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July 28-30, 2016; IEEE IRI, Pittsburgh, PA, USA
Madhav [email protected]
Dr. Chris [email protected]
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An Automatic Approach for Discovering and Geocoding Locations in Domain-
Specific Web Data
Information Retrieval and Data Science
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OUTLINE• Introduction• Overview of ecosytem• Data Flow
• GeoTopic Identification• Identifying location names• Lucene Geo Gazetteer
• Geonames Features• Evolution of Ranking Algorithm
• Evaluation and results• Challenges• Conclusion and Future Work
Information Retrieval and Data Science
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Introduction
Information Retrieval and Data Science
• AIM – Discover geospatial locations from WWW data culled from diverse domains
• Locations can be present in text or metadata of a file
• We present the overall approach and then explains in detail the Lucene Geo Gazetteer derived from the Geonames.org dataset
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Overview of ecosytem
Information Retrieval and Data Science
• WWW is home to a rich diversity of and large volumes of data across many domains and disciplines
• In MEMEX we have collected web page and textual data from many domains including Materials Research, and Autonomous Robots and other domains
• There is no single field or textual pattern for discerning location information in all domains
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Example dataset
Information Retrieval and Data Science
Source Science Topic Location-related field Notes
AMD
Agriculture, Atmosphere, Biologi- cal Classification, Biosphere, Cli- mate Indicators, Cryosphere, Hu- man Dimensions, Land Surface, Oceans, Paleoclimate, Solid Earth, Terrestrial Hydrosphere, Spectral Engineering, Sun-Earth Interac- tions
1.Location Keywords field in Metadata 2.Sometimes mentioned in the title and Summary fields 3.Spatial coordinates provided
1.Continent level and Geo- graphic Region 2.Island, sometimes specific 3.Multi-values 4.Not missing in Location Key- words
ACADIS
Agriculture, Atmosphere, Biologi- cal Classification, Biosphere, Cli- mate Indicators, Cryosphere, Hu- man Dimensions, Land Surface, Oceans. Paleoclimate, Solid Earth, Terrestrial Hydrosphere
1.Location(s) field in Metadata 2.Location mentioned in De- scription field, often in the first few sentences or last few sen- tences. Sometimes missing. 3.Longitude and Latitude pro- vided in the Metadata. 4.Sometimes mentioned in the title
1. Province/State level or Geo- graphic Region 2. Multi-values3. Not missing in Location
Locations mentioned in the NSF TREC-DD-Polar dataset
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Data Flow
Information Retrieval and Data Science
Crawled website data
Extract text and metadata
Discover location entities
Find location in gazetteer
Lucene Geo Gazetteer
Information Retrieval and Data Science
Lucene Geo Gazetteer
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Uses the indexed Geonames.org dataset to match location names with latitude and longitude.
Indexes Geonames.org dataset to a lucene based index
Ranks matched results based on certain features.
Information Retrieval and Data Science
Geonames Features• Name - Legal name of a location. • Alternate names - All other names by which a loca- tion is known.
Alternate names provide a CSV of all pronunciations and synonyms for a location.
• Feature class - A high level bucket for similar locations. This bucket distinguishes continent and countries from cities and villages.
• Feature code - A more granular bucket for locations to identify it as a country, state, capital, city.
• Population, Latitude, Longitude, Country code, Admin1 code, Admin2code
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Information Retrieval and Data Science
Evolution of Ranking AlgorithmThe naive version –1. Search input phrase in name, alternate name stored index and
get top N results. 2. Calculate edit distance between input phrase and name,
alternate name. 3. Rank results in order of lowest edit distance between input
phrase and name, alternate name fields.
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Information Retrieval and Data Science
Evolution of Ranking AlgorithmThe naive version results and observations–• Poor results. 1/50 locations gave correct result
• There are many location with similar names. String “Pasadena” have 250 and “Portland” have 897 matches in dataset.
• Popular places have greater number of alternate names
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Information Retrieval and Data Science
Evolution of Ranking AlgorithmFirst improvement –1. Add multiple sorting criteria in lucene query in order “feature
class”, “feature code” and “population”2. Fetch top N results from this criteria3. Add edit distance between input phrase and name and all
alternate names
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Information Retrieval and Data Science
Evolution of Ranking AlgorithmObservations –• More weightage to name as it is legal name for a location e.g.
China is stored with “People’s Republic of China” and alternate name have ”... Cayina,Chaina,China,Chine, ..”
• We can not just rely on edit distance.• Edit distance between “Los Cabos” and “Los Angeles” == 6• Edit distance between “Los Angeles County” and “Los Angeles” == 7
• Results sorted only by population are very relevant and query time is improved
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Information Retrieval and Data Science
Evolution of Ranking AlgorithmOne more try –Pass 1
1. Get top N * 3 results from lucene index sorted by population for input phrase. 2. Sort results by feature code. 3. Pass top N results.
Pass 2 4. Assign a score if input phrase is exactly a token in name. e.g. “Los Angeles” in
“Los Angeles County” 5. Assign a lesser score if input phrase is partly a token in name e.g. India in
Indiana 6. Calculate edit distance of input phrase with all alternate names.7. Assign weight as a function of count of alternative names.
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Information Retrieval and Data Science
Evaluation and ResultsWe tested our improved algorithm on a diverse set of 50,000+ locations with countries, states, districts, cities, towns and villages from DARPA MEMEX and from the NSF Polar domains.
Our ground truth data comparisons resulted in a total overall accuracy of 94%
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Information Retrieval and Data Science
Challenges• Limitations with opensource Geonames.org data set. More
features will help –• Popular name – “Argentina” for “Argentine Republic“• Area of a location
• No one best result.• Pasadena in both California and Texas
• Huge and diverse data set which have data ranging from parks to continents. Improved performance if data set is reduced to eg. continents, countries, state, counties, capitals and cities
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Information Retrieval and Data Science
Conclusion and Future Work• Early results from this effort are promising but a number of
avenues of future work remain unexplored. • Automatically augment Geonames.org with crowd sourced
popular place names • Geospatial coordinate data beyond points, e.g., bounding
boxes, and other shapes, to allow for more meaningful and spatially directed queries.
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Information Retrieval and Data Science
ACKNOWLEDGMENTS• This work was supported by the DARPA XDATA/Memex
program.
• NSF Polar Cyber infrastructure award numbers PLR-1348450 and PLR-144562 funded a portion of the work.
• Effort supported in part by JPL, managed by the California Institute of Technology on behalf of NASA.
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• Lucene Geo Gazetteer: https://github.com/chrismattmann/lucene-geo-gazetteer• Geonames dataset: https://www.geonames.org • Apache Tika: https://tika.apache.org/ • Apache OpenNLP: https://opennlp.apache.org/ • Apache Lucene: http://lucene.apache.org• Memex: http://memex.jpl.nasa.gov/
More about us on github:
18Information Retrieval and Data Science
THANK YOU
Madhav Sharansmadha
Dr. Chris Mattmannchrismattmann