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 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 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 for Apollo/Moon where the keyword exists in the excerpt but not in the keywords:

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

To remove the "fig" keyword from knowledge graph records for Apollo/Moon where it exists in the keywords field:

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

To remove the most recent knowledge base record for the given feature name (Avan) and target (Mars):

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

To remove all knowledge base records except the most recent one for the given feature name and target:

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

Maintainers

Golnaz