minimalKB 2.4.0

A RDFlib-backed minimalistic knowledge based for robotic application

minimalKB is a SQLite-backed minimalistic knowledge base, initially designed for robots (in particular human-robot interaction or multi-robot interaction).

It stores triples (like RDF/OWL triples), and provides an API accessible via a simple socket protocol.

pykb provides an idiomatic Python binding, making easy to integrate the knowledge base in your applications.

It has almost no features, except it is fast and simple. Basic RDFS reasoning is provided.

Written in Python. The only required dependency is sqlite3. If rdflib is also available, you can easily import existing ontologies in RDF/OWL/n3/Turtle formats in the knowledge base.

Documentation

You can use minimalkb either as a server, accessible from multiple applications (clients), or in embedded mode (which does not require to start a server process, but is limited to one single component). Note that the embedded mode is only available for Python applciations.

In both case, and if your application is written in Python, it is highly recommended to use pykb to interact the knowledge base.

Server mode

To start the knowledge base as a server, simply type:

$ minimalkb

(run minimalkb --help for available options)

Then:

import kb

with kb.KB() as kb:
    #...

See usage examples on the pykb page, or in the minimalkb unit-tests.

Embedded mode

No need to start minimalkb. Simply use the following code to start using the knowledge base in your code:

import kb

with kb.KB(embedded=True) as kb:
    #...

Interacting with the minimalkb from other languages

• from C++: check liboro

• from any other language: the communication with the server relies on a simply socket-based text protocol. Feel free to get in touch if you need help to add support for your favourite language!

Features

Server-Client or embedded

minimalKB can be run as a stand-alone (socket) server, or directly embedded in Python applications.

Multi-models

minimalKB is intended for dynamic environments, with possibly several contexts/agents requiring separate knowledge models.

New models can be created at any time and each operation (like knowledge addition/retractation/query) can operate on a specific subset of models.

Each models are also independently classified by the reasoner.

Event system

minimalKB provides a mechanism to subscribe to some conditions (like: an instance of a given type is added to the knowledge base, some statement becomes true, etc.) and get notified back.

Reasoning

minimalKB only provides very basic RDFS/OWL reasoning capabilities:

• it honors the transitive closure of the rdfs:subClassOf relation.

• functional predicates (child of owl:functionalProperty) are properly handled when updating the model (ie, if <S P O> is asserted with P a functional predicate, updating the model with <S P O'> will first cause <S P O> to be retracted).

• owl:equivalentClass is properly handled.

The reasoner runs in its own thread, and classify the model at a given rate, by default 5Hz. It is thus needed to wait ~200ms before the results of the classification become visible in the model.

Transient knowledge

minimalKB allows to attach ‘lifespans’ to statements: after a given duration, they are automatically collected.

Ontology walking

minimalKB exposes several methods to explore the different ontological models of the knowledge base. It is compatible with the visualization tool oro-view.