ConceptNet 5.5.5 is out, and it's running on conceptnet.io. The version5.5 tag in Git has been updated to point to this version. Here's what's new.

Changelog

Data changes: Build process changes: Library changes:

Understanding our version numbers

Version numbers in modern software are typically described as major.minor.micro. ConceptNet's version numbers would be better described as mega.major.minor. Now that all the version components happen to be 5, I'll explain what they mean to me. The change from 5.5.4 to 5.5.5 is a "minor" change. It involves important fixes to the data, but these fixes don't affect a large number of edges or significantly change the vocabulary. If you are building research on ConceptNet and require stable results, we suggest building a particular version (such as 5.5.4 or 5.5.5) from its Docker container, as a "minor" change could cause inconsistent results. The change from 5.4 to 5.5 was a "major" change. We changed the API format somewhat (hopefully with a smooth transition), we made significant changes to ConceptNet's vocabulary of terms, we added new data sources, and we even changed the domain name where it is hosted. We're working on another "major" update, version 5.6, that incorporates new data sources again, though I believe the changes will not be as sweeping as the 5.5 update. The change from ConceptNet 4 to ConceptNet 5 (six years ago) was a "mega" change, a thorough rethinking and redesign of the project, keeping things that worked and discarding things that didn't, which is not well described by software versions. The appropriate way to represent it in Semantic Versioning would probably be to start a new project with a different name. Don't worry, I have no urge to make a ConceptNet 6 anytime soon. ConceptNet 5 is doing great. The word vectors that ConceptNet uses in its relatedness API (which are also distributed separately as ConceptNet Numberbatch) are recalculated for every version, even minor versions. The results you get from updating to new vectors should get steadily more accurate, unless your results depended on the ability to represent harmful stereotypes. You can't mix old and new vectors, so any machine-learning model needs to be rebuilt to use new vectors. This is why we gave ConceptNet Numberbatch a version numbering scheme that is entirely based on the date (vectors computed in June 2017 are version 17.06).