MoCS is a system to visualize topics from the DBLP database of research papers. A live version of this code is available, including a description of the algorithms implemented and demonstrations of the maps rendered by the system, and a research paper describing how the system works.

Allows creation of basemaps through lib/cli_interface.py.

1. Install the python dependencies listed in requirements.txt. Using pip:

    pip install -r requirements.txt
   

2. Install graphviz

3. Download NLTK corpora by running

    python -m nltk.downloader brown langid
   

4. Rename lib/mocs_configTEMPLATE.py to lib/mocs_config.py Edit this file if your graphviz binaries or nltk data are in a non-standard location, or if you want the document database to be stored in a non-SQLite database. If this file is not edited, the DBLP data will be stored in a SQLite database called mocs.db in the project root.

5. Run ./build_dblp.sh to create the database and load it with data downloaded from DBLP.

Example Usage:

    python lib/cli_interface.py perlis.pdf --author "%Perlis" --ending_year 1990

For a full list of command options:

    python lib/cli_interface.py -h

Allows creation of basemaps and heatmaps.

1. Install rabbitmq.

2. Set up Django settings (optional). Edit DATABASES, SECRET_KEY, and ADMINS in settings.py, or create a new file local_settings.py containing these parameters. If DATABASES is not edited, the Django tables will be stored in a SQLite database called mocs.db in the project root (which is by default the same database the DBLP data is stored in).

3. Create Django databases:

    ./manage.py syncdb
   

4. Run the server:

    ./manage.py celeryd
    ./manage.py runserver
   

5. Access the map interface at http://localhost:8000/maps/query/

Code is released under the MIT License.