Xponents

Geographic Place, Date/time, and Pattern entity extraction toolkit along with text extraction from unstructured data and GIS outputters.

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Xponents, A Toolkit for Geotagging World-wide Geography

About our nomenclature – OpenSextant is a family of projects for geotagging and other NLP and information extraction work. Xponents is an actively developed implementation of our OpenSextant mindset around geotagging: be accurate, be simple, be extensible and show the work.

Xponents is a set of information extraction libraries including to extract and normalize geographic entities, date/time patterns, keywords/taxonomies, and various patterns. For example as depicted in Figure 1 where a tourist spots in the French country side are detected and geolocated. That’s easy when there is only one such known location with that name. It becomes challenging when we try to detect such names in any language in any part of the world. Is “McDonald’s” a farm or a restaurant?
Which one is the right one – there’s thousands of restaurant locations by that name.

General topics in our geotagging workflow

Figure 1. A General Tagging and Coding Paradigm

This table below loosely portrays the scenarios in which Xponents operates – parsing and conditioning knowable geo/temporal references in text into usable data structures.

input text notional output with normalization
“Boise, ID is fun!” Place names, geocoded:
geo = { match:"Boise ID", adm1:"US.16",
lat=43.61, lon=-116.20,
feat_code:"PPL", confidence=78}
“Born on 30 DECIEMBRE 1990 … “ Normalized date/time:
date = { match="30 DECIEMBRE 1990",
date_norm="1990-12-30"}
“Epicenter at 01°44’N 101°22’E …“ Geo Coordinates:
coord = { match="01°44'N 101°22'E",
lat=1.733, lon=101.367,
pattern="DM-01"}
“The Swiss delegation…“ Keywords:
taxon = { match="Swiss", id="nationality.CHI" }
“User accessed IP 233.12.0.11” Patterns:
pattern= { match="233.12.0.11", pattern_id="IPADDRESS" }

Define your own patterns or compose your own Extractor apps. As a Java API, the following application classes implement the extraction above:

Here are some fast-tracks for applying Xponents:

A. Geotagging and everything – Deploy the Docker service as prescribed on our Docker Hub. Consult at least the Python client opensextant.xlayer as noted in the Docker page and in the Python setup below.

B. Pattern extraction – The Python library opensextant.FlexPat or its Java counterpart org.opensextant.extractors.flexpat – offer a lean and effective manner to develop a regular-expression pipeline. In either case minimal dependencies are needed. See Python setup below. Complete FlexPat overview is available at Patterns.md

C. Geotagging and everything,…. but for some reason you feel that you need to build it all yourself. You’ll need to follow the notes here in BUILD.md and in ./solr/README.md. The typical approach is to deploy the docker instance of xponents and interact with it using the Python client, opensextant.xlayer. The xponents-service.sh demonstrates how to run the REST service with or without Docker.

Video: Lucene/Solr Revolution 2017 Conference Talk

“Discoverying World Geography in Your Data”, presented at Lucene/Solr Revolution 2017 in Las Vegas 14 September, 2017. In video, at minute 29:50. This is a 12 minute talk

Methodology

The Geocoder Handbook represents the Xponents methodology to geotagging and geocoding that pertains to coordinate extraction and general geotagging, i.e., XCoord and PlaceGeocoder, respectively.

Code Examples

Using XCoord and PlaceGeocoder here are two examples of extracting geographic entities from this made-up text:

    String text = "Earthquake epicenter occurred at 39.56N, -123.45W or "+
                  "an hour west of the Mendocino National Forest ";    
    
    // INIT
    //==================
    XCoord xcoord = new XCoord();    
    xcoord.configure();        
    SolrGazetteer gaz = new SolrGazetteer();
    
    // EXTRACT
    //==================
    List<TextMatch> coords = xcoord.extract( text );
    
    for (TextMatch match : coords) {
       /* if match instanceof GeocoordMatch do something. 
       */
       print("FOUND:" + match);
       print("Near named place " + gaz.placeAt(match));
    }
    
    /* "Do something" might produce this output:  print the location found could
     *  be reverse geocoded to Arnold, CA, US
     */
     -=-=-=-=-=-=-==-=-=-=-=-=-=
        FOUND: 39.56N, -123.45W @(33:49) matched by DD-02
        Near named place Arnold (ADM1=06, CC=US, FEAT=PPL)
     -=-=-=-=-=-=-==-=-=-=-=-=-=

Now with the same text as above, the second and more complex example applies the PlaceGeocoder:

    // INIT
    //==================
    tagger = new PlaceGeocoder();
    Parameters xponentsParams = new Parameters();
    xponentsParams.resolve_localities = true;
    tagger.setParameters(xponentsParams);
    tagger.configure();
    
    /* In this example, ""Mendocino National Forest" is found and is 
     * coded as { cc=US, adm1="06",...} representing that the forest 
     * is in California ("US.06"). To actually resolve "US.06" to a named 
     * province we need the resolve_localities flag ON. There is a small 
     * performance hit which adds up if you do this for every place found.
     */      
    
    // EXTRACT
    //==================
    List<TextMatch> allPlaces = tagger.extract( text );
    for (TextMatch match : allPlaces) {    
       /* PlaceGeocoder yields many types of entities!!
          if match instanceof GeocoordMatch, PlaceCandidate, TaxonMatch, etc.
              then do something. 
       */
    }
    
    /* These examples print to stdout, but imagine saving to a database, 
     * exporting KML or a spreadsheet on the fly
     */     
    -=-=-=-=-=-=-STDOUT==-=-=-=-=-=-=
    ....
    Name:Mendocino National Forest
    Rules = [DefaultScore]
        geocoded @ Mendocino National Forest (06, US, FRST), score=20.56 with conf=93
    MENTIONS DISTINCT PLACES == 1
    [Mendocino National Forest]
    MENTIONS COUNTRIES == 0
    []
    MENTIONS COORDINATES == 1
    [39.56N, -123.45W]
    ....
    -=-=-=-=-=-=-==-=-=-=-=-=-=

Java versus Python Libraries

Python and Java functionality overlaps but is still drastically differ. The Core API resembles the Python library somewhat. The primary NLP/geocoding work is done in the Xponents SDK proper, whereas the other modules support that work as utilities and foundational data classes, etc.

Setup

As mentioned above to work with just pattern extraction, the Core API is needed. But to do anything more, like geotagging, the SDK API is needed along with the instance of Solr as prescribed in the ./solr folder. Here are some useful pre-requisites:

Maven

Insert these dependencies into your POM depending on what you need.

  <!-- Xponents Core API -->
  <dependency>
    <groupId>org.opensextant</groupId>
    <artifactId>opensextant-xponents-core</artifactId>
    <version>3.7.0</version>
  </dependency>

  <!-- Xponents SDK API -->
  <dependency>
    <groupId>org.opensextant</groupId>
    <artifactId>opensextant-xponents</artifactId>
    <version>3.7.0</version>
  </dependency>

For reference: OpenSextant Xponents on Maven. For that matter, the only relevant artifacts in our org.opensextant group are:

These libraries have been folded into Xponents Core library for long term Java sustainment; They are rooted under org.opensextant.

Python

Someday we’ll just post this to PyPi.


    pushd ./python
    python3 ./setup.py sdist
    popd

    # Install built lib with dependencies to ./piplib
    pip3 install -U --target ./piplib ./python/dist/opensextant-1.5*.tar.gz 
    pip3 install -U --target ./piplib lxml bs4 arrow requests pycountry
    
    # Note - if working with a distribution release, the built Python 
    # package is in ./python/ (not ./python/dist/)

    # * IMPORTANT *
    export PYTHONPATH=$PWD/piplib
    
    # Adjust the "--target piplib" and the PYTHONPATH according to how 
    # you like it.

Demonstration

See the Examples material that you can use from within the Docker image or from a full checkout/build of the project. Pipeline topics covered there are :

API Documentation and Developer Notes

These extractors are in the org.opensextant.extractors packages, and demonstrated in the Examples sub-project using Groovy. These libraries and builds are located in Maven Central and in Docker Hub. Here is a quick overview of the organization of the project:

A packaged distribution has the API docs at ./lib/opensextant-*-javadoc.jar

Xponents Philosophy: The intent of Xponents is to provide the extraction without too much infrastructure, as you likely already have that. This library tool chest contains the following ideas and capabilities:

Build

See BUILD.md

Release History and Versioning

RELEASES