SMILE Knowledge Source: ParseQuery
- GraphDB can be installed for your distribution.
- Make sure it's running port
7200
, e.g. http://localhost:7200. - Make sure you have GraphDB running on http://localhost:7200.
- For testing, make sure the username and password are set to
admin
- Create a new test repository. Go to http://localhost:7200
- Create new repository:
- Name the repository (Repository ID) as
smile
- Set context index to True *(checked).
- Set query timeout to 45 second.
- Set the
Throw exception on query timeout
checkmark to True (checked) - Click on create repository.
- Name the repository (Repository ID) as
- Make sure the repository rin in "Running" state.
- Create new repository:
- See instruction below for troubleshooting
A few notes on configurting SMILE to connect to the database.
- The main SMILE Flask application can be configured in the config/local_config.yml file.
- Knowledge source that are running in a Docker instance must use the "Docker" version of the config file: config/local_config_test.yml.
Install swi-prolog for your environment.
cd src/smile_ks_parse_query/libs/corenlp/
chmod +x nlp_server.sh
curl https://downloads.cs.stanford.edu/nlp/software/stanford-corenlp-4.3.1.zip > stanford-corenlp-4.3.1.zip
open stanford-corenlp-4.3.1.zip
curl https://downloads.cs.stanford.edu/nlp/software/stanford-parser-4.2.0.zip > stanford-parser-4.2.0.zip
open stanford-parser-4.2.0.zip
cd ../../../..
cd src
conda env create -f PyParseQuery.yml
./scripts/setup_folders.sh
You will need two terminals.
Term 1, run:
cd src/smile_ks_parse_query/libs/
./corenlp/nlp_server.sh
This will start a NLP server at http://localhost:9000. It will run in the background.
Term 2: Run ParseQuery example
conda activate PyParseQuery
cd src
python -m smile_ks_parse_query.main
conda activate PyParseQuery
cd src
python -m smile_ks_parse_query.listener