ConceptNet aims to give computers access to common-sense knowledge, the kind of information that ordinary people know but usually leave unstated.

ConceptNet is a semantic network that represents things that computers should know about the world, especially for the purpose of understanding text written by people. Its "concepts" are represented using words and phrases of many different natural language -- unlike similar projects, it's not limited to a single language such as English. It expresses over 13 million links between these concepts, and makes the whole data set available under a Creative Commons license.

Much of the current development of ConceptNet involves using it as an input for machine learning about the semantics of text. Its multilingual representation makes it particularly expressive, because the semantic overlaps and differences between languages are a useful signal that a learning system can learn from.

ConceptNet grew out of Open Mind Common Sense, an early project for crowd-sourced knowledge, and expanded to cover many different languages through a collaboration with groups around the world. ConceptNet is cited in many research papers, and its public API gets over 50,000 hits per day.

This Python package contains a toolset for building the ConceptNet 5 knowledge graph, possibly with your own custom data, and it serves the HTML interface and JSON Web API for it.

You don't need this package to simply access ConceptNet 5; see http://conceptnet.io for more information and a browsable Web interface with an API.

Further documentation is available on the ConceptNet wiki.

Licensing and attribution appear in LICENSE.txt and DATA-CREDITS.md.

If you're interested in using ConceptNet, please join the conceptnet-users Google group, for questions and occasional announcements: http://groups.google.com/group/conceptnet-users?hl=en

For real-time discussion, ConceptNet also has a chat channel on Gitter: https://gitter.im/commonsense/conceptnet5

To be able to run all steps of the ConceptNet build process, you'll need a Unix command line (Ubuntu 16.04 works great), Python 3.5 or later, 30 GB of RAM, and some other dependencies. See the build process on our wiki for instructions.

You may not need to build ConceptNet yourself! Try the Web API first.

Run pytest to test the ConceptNet libraries and a small version of the build process.

Run pytest --quick to re-run the tests more quickly, with the assumption that the small test database has already been built.

Run pytest --fulldb to run additional tests on the fully built ConceptNet database.