From the website:
ConceptNet is a semantic network containing lots of things computers should know about the world, especially when understanding text written by people.
It is built from nodes representing concepts, in the form of words or short phrases of natural language, and labeled relationships between them. These are the kinds of things computers need to know to search for information better, answer questions, and understand people’s goals. If you wanted to build your own Watson, this should be a good place to start!
ConceptNet contains everyday basic knowledge:
ConceptNet 5 is a graph
To be precise, it’s a hypergraph, meaning it has edges about edges. Each statement in ConceptNet has justfications pointing to it, explaining where it comes from and how reliable the information seems to be.
Previous versions of ConceptNet has been distributed as idiosyncratic database structures plus some software to interact with them. ConceptNet 5 is not a piece of software or a database; it is a graph. It’s a set of nodes and edges, which we can represent in multiple formats including JSON. You probably know better than we do what software you want to use to interact with it!
(That said, you can have our idiosyncratic Solr index if you want, but that’s not ConceptNet, it’s just a system for quickly looking things up in ConceptNet.)
Some other interesting properties:
- The ConceptNet graph is ID-less. Every node and assertion contains all the information necessary to identify it and no more in its URI, and does not rely on arbitrarily-assigned IDs. The advantage of this is that if multiple branches of ConceptNet are developed in multiple places, we can later merge them simply by taking the union of the nodes and edges. (And we hope for this to happen!)
- ConceptNet supports linked data: you can download a list of links to the greater Semantic Web, via DBPedia and via RDF/OWL WordNet. For example, our concept cat is linked to the DBPedia node at http://dbpedia.org/resource/Cat.
In addition to being a data source, interesting notion of “ID-less” nodes and edges.
Information on the software setup, Solr and Python to deliver ConceptNet5 as a hypergraph is also available.
I first saw this in Max De Marzi’s Knowledge Bases in Neo4j. You will find that Max’s approach involves dumbing down the hypergraph.