Skip to main content

A SQLite-backed minimalistic knowledge based for robotic application. Mostly KB-API conformant.

Project description

![Screenshot of a minimalKB knowledge model viewed with oro-view](doc/oroview.jpg)

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 a mostly conformant [KB-API]( 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 (cf below for details).

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.


` $ git clone $ cd minimalkb $ python install $ minimalkb `

Run minimalkb –help for available options.


### 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 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](

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for minimalKB, version 0.9
Filename, size File type Python version Upload date Hashes
Filename, size minimalKB-0.9.tar.gz (36.0 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page