Nl-project

[...] it, you define words, and use the words to produce sentences and rules, that you add to the knowledge base. Terms extracts consecuences from those sentences and rules, and adds them to the knowledge [...]

[...] I have released a beta version of Terms, with many bugfixes and improved documentation. If you are interested in learning [...]

[...] resides in a RDBMS. I think the systax of Terms is intuitive enough that that I can give some code examples without going into much detail. Almost everything in Terms are words. Words have types, and [...]

[...] the words to produce sentences and rules, that you add to the knowledge base. Terms extracts consecuences from those sentences and rules, and adds them to the knowledge base. Then you [...]

[...] of this. To start with, we define a word she of type thing: Then we define 2 verbs (2 words of type verb), wants and gets: With these words, we can make facts shuch as (want she, what <some word&# [...]

[...] here. This means that we only need first order variables (ranging over words) to cover all Terms constructs with the exception of rules. I will give an example of this. To start with, we define a word [...]

[...] and improved documentation. If you are interested in learning about Terms, you can visit its pypi page. [...]

I have released a beta version of Terms, with many bugfixes and improved documentation. If you are interested in learning about Terms, you can visit i [...]

[...] This post is to announce the release of Terms, a knowledge store written in Python. Its main selling point would be the language it provides to express knowledge, that, in my opinion, is very simple [...]

[...] can use Python in the rules, but I won’t touch on that here. This means that we only need first order variables (ranging over words) to cover all Terms constructs with the exception of rules. I will [...]