ieee iri 2016 lucene geo gazetteer

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


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Introduction


• 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


• 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


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 sentences. Sometimes missing. 3.Longitude and Latitude provided 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


Crawled website data

Extract text and metadata

Discover location entities

Find location in gazetteer

Lucene Geo Gazetteer


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.


Geonames Features• Name - Legal name of a location. • Alternate names - All other names by which a location 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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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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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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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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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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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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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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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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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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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

https://github.com/chrismattmann/lucene-geo-gazetteer

https://www.geonames.org/

https://tika.apache.org/

https://opennlp.apache.org/

http://lucene.apache.org/

http://lucene.apache.org/

http://memex.jpl.nasa.gov/