ADSPlanetaryNamesPipeline

planetary names pipeline

Short summary

This pipeline processes planetary nomenclature to identify named entities (feature names) for a target and feature type. It can collect data, predict entities, and perform end-to-end processing (collect followed by predict).

Required software

- RabbitMQ and PostgreSQL

Setup (recommended)

$ virtualenv python
$ source python/bin/activate
$ pip install -r requirements.txt
$ pip install -r dev-requirements.txt
$ vim local_config.py # edit, edit
$ ./start-celery.sh

Queues

- task_process_planetary_nomenclature: queues one feature name at a time for processing

Command Line Options

-a, --action            Action to perform (collect, identify, etc.)
-t, --target            Target celestial body (e.g., Moon, Mars)
-f, --feature_type      Feature type (e.g., Crater)
-n, --feature_name      Feature name (e.g., Apollo)
-k, --keyword           Knowledge graph keyword to add/remove/export
-c, --confidence_score  (optional) Filter by minimum confidence score
-d, --days              (optional) Filter by days for retrieve_identified_entities action
-s, --timestamp         (optional) Specify date in YYYY-MM-DD format for identification actions
-u, --usgs_update       CSV file for USGS data update
-o, --output_file       (optional) Specify file name for data export
-l, --label             (optional) Specify label of the knowledge graph keywords to export (e.g, planetary or unknown)

Command Lines

To collect data for craters on Mercury:

python run.py -a collect -t Mercury -f Crater

To identify crater features names on Mercury from ADS records:

python run.py -a identify -t Mercury -f Crater

To perform end-to-end processing (collect and identify):

python run.py -a end_to_end -t Mercury -f Crater

To identify features using a specific timestamp:

python run.py -a identify -t Mars -f Crater -s 2023-01-01

To query database for identified entities from last 30 days with confidence >= 0.8:

python run.py -a retrieve_identified_entities -c 0.8 -d 30

To add "basin" keyword to knowledge graph records:

python run.py -a add_keyword_to_knowledge_graph -k basin -t Moon -f Apollo

To remove the "fig" keyword from knowledge graph records:

python run.py -a remove_keyword_from_knowledge_graph -k fig -t Moon -f Apollo

To remove the most recent knowledge base record:

python run.py -a remove_the_most_recent -t Mars -f Avan

To remove all knowledge base records except the most recent one:

python run.py -a remove_all_but_last -t Mars -f Avan

To export keywords from knowledge graph:

python run.py -a retrieve_knowledge_graph_keywords -t Mars -f Crater -l positive -o keywords_export.csv

To update database with new USGS gazetteer data:

python run.py -a update_database_with_usgs_entities -u updated_usgs_terms.csv

To process specific feature by name instead of type:

python run.py -a collect -t Mars -n Amenthes

To retrieve identified entities for specific feature:

python run.py -a retrieve_identified_entities -t Mars -n Amenthes -c 0.75

To retrieve knowledge graph keywords for specific feature:

python run.py -a retrieve_knowledge_graph_keywords -t Mars -n Amenthes -l planetary## Maintainers

Maintainers

Golnaz