watsongraph

watsongraph is a concept discovery, graphing, and processing library written in Python 3. The library's core facility is the ConceptModel object, a conceptual graph constructed out of the individual concept nodes associated with labels from the IBM Watson wikipedia/en-20120601 Wikipedia-derived conceptual graph. This graph is queried using the Concept Insights API and then reconstructed locally as a networkx-based weighted conceptual graph:

>>> from watsongraph.conceptmodel import ConceptModel
>>> ibm = ConceptModel(['IBM'])
>>> ibm.explode()
>>> ibm.concepts()
['.NET Framework', 'ARM architecture', 'Advanced Micro Devices', ...]
>>> len(ibm.concepts())
37
>>> 'Server (computing)' in ibm.concepts()
True
>>> ibm.augment('Server (computing)')
>>> len(ibm.concepts())
58
>>> ibm.edges()
[(0.89564085, 'IBM', 'Digital Equipment Corporation'),
 (0.8793883, 'Solaris (operating system)', 'Server (computing)'),
 ...
]

The ConceptModel can then be associated with any number of applications. Basic bindings are provided, in particular, for a recommendation service using library-provided Item and User classes:

>>> from watsongraph.user import User
>>> from watsongraph.item import Item
>>> Bob = User(user_id="Bob")
>>> Bob.input_interests(["Data science", "Machine learning", "Big data", "Cognitive science"])
>>> meetup = Item("Meetup", "This is a description of a pretty awesome event...")
>>> relay = Item("Relay", "This is a description of another pretty awesome event...")
>>> Bob.interest_in(meetup)
1.633861635
>>> Bob.interest_in(relay)
1.54593405
# Update the "Bob" model to account for our new information on Bob's preferences.
>>> Bob.express_interest(meetup)

Setup

watsongraph is available on PyPi and can be downloaded locally with pip install watsongraph.

However, in order to use IBM Watson cognitive APIs you must first register an account on IBM Bluemix. If you do not have an account already you may register for a free trial account.

Once you are logged in, enter the catalog, scroll down to the "IBM Watson" section, and click through to create an instance of the Concept Insights service. Go back to the dashboard, click on the newly populated service, and click through to "Service Credentials" on the sidebar to get your service credentials: copy-paste this json output and save it locally as concept_insight_credentials.json. Your credentials should look like this:

{
  "credentials": {
    "url": "https://gateway.watsonplatform.net/concept-insights/api",
    "username": "........-....-....-....-............",
    "password": "............"
  }
}

Account access is provided on a thirty-day free trial basis by default, however there is free monthly allotment (25,000 queries), more than enough for experimental purposes.

Documentation and examples

• "Exploring the IBM Watson Concept Insights service using watsongraph" is a blog post on my personal website which explores the capacities and use cases for the watsongraph library. If you are curious about how it works, the visualizations here are the best place to start!
• The ConceptModel Jupyter notebook provides a detailed walkthrough of basic ConceptModel operations. To learn how to use this library, start here, then move on to the two notebooks below.
• The Advanced Concept Modeling Jupyter notebook provides a detailed walkthrough of advanced ConceptModel features as well as recommendations about how to use them for modeling.
• The Recommendations Modeling Jupyter notebook applies watsongraph to user recommendation modeling.
• The Sphinx documentation is the reference manual for all watsongraph methods.
• For further inspiration you can also try out IBM's own example application (which predates this library).

Contributing

The watsongraph library is currently in its first stable release, so it is still in a fairly early state of development: there are quite a large number of improvements and new features which could potentially be made. At the moment I am waiting for work to finish on the Watson Developer Cloud Python SDK so that I can make a large volume of low-level architectural improvements (and add a few new features) for the next planned stable release, 0.3.0. You can see the milestone composite issues in this repository's issue tracker.

To pull the latest build onto your development machine, clone this repository (git clone https://github.com/ResidentMario/cultural-insight.git) and follow the instructions in setup to populate your access credentials.

To submit a minor fix just submit a pull request. Be sure to explain what problem your change addresses!

If you are interested in contributing new features or major enhancements, we should talk! You can submit an issue or pull request summarizing the work using the "Enhancement" label. You can also filter to enhancements to see what's already on the radar.

I am very receptive to feedback and would defintely like to see this code reviewed by others, you can reach out to me at aleksey@residentmar.io.