KnowledgeCore
KnowledgeCore is a RDFlib-backed minimalistic knowledge base, initially designed for robots (in particular human-robot interaction or multi-robot interaction). It features full ROS support.
It stores triples (like RDF/OWL triples), and provides an API accessible via a simple socket protocol or a ROS wrapper.
pykb provides an idiomatic Python binding over the socket interface, making easy to integrate the knowledge base in your application. A similar API wrapper exists for ROS as well (see example below).
It integrates with the reasonable OWL2 RL reasoner to provide OWL2 semantics and fast knowledge materialisation.
Example
This example uses the ROS API (see below), with some Pythonic syntatic sugar:
from knowledge_core.api import KB
rospy.init_node("test_knowledge_base")
kb = KB()
def on_robot_entering_antonio_property(evt):
print("A robot entered Antonio's %s: %s" (evt[0]["place"], evt[0]["robot"]))
kb += "ari rdf:type Robot"
kb += ["antonio looksAt ari", "ari isIn kitchen"]
kb.subscribe(["?robot isIn ?place", "?place belongsTo antonio", "?robot rdf:type Robot"], onRobotEnteringAntonioProperty)
kb += "kitchen belongsTo antonio"
# try as well:
# kb -= "antonio looksAt ari" to remove facts
# kb["* rdf:type Robot"] to query the knowledge base
rospy.spin()
will print:
A robot entered Antonio's kitchen: ari
Installation
KnowledgeCore only supports Python 3
Prerequisite
rdflib >= 6.0.0
:
$ pip install rdflib
For reasoning (optional):
$ pip install reasonable
Installation
From pypi
:
$ pip install knowledge_core
From source:
$ git clone https://github.com/severin-lemaignan/knowledge_core.git
$ cd knowledge_core
$ python setup.py install
$ knowledge_core
If using ROS, you can also use your regular catkin workflow.
Documentation
General usage
If you are a roboticist, jump to ROS usage
You can use KnowledgeCore
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 client). Note that the embedded
mode is only available for Python applications.
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:
$ knowledge_core
(run knowledge_core --help
for available options)
Then:
import kb
with kb.KB() as kb:
#...
See usage examples on the pykb page, or in the KnowledgeCore
unit-tests.
Embedded mode
No need to start KnowledgeCore
. 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 KnowledgeCore 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!
How do I get that fancy image on top of the README?
Check oro-view ;-)
ROS usage
Please first read the general API introduction, as this applies to the ROS interface as well.
To start the ROS node:
rosrun knowledge_core knowledge_core
Note that, in general, you want to use the 'Pythonic' wrapper built on top of the low-level ROS topics/services API. See example above. This Pythonic interface follows the pykb
API (except in a few corner case that are not supported by the ROS interface).
knowledge_core
exposes two topics, /kb/add_facts
and
/kb/remove_facts
, to add/remove triples to the knowledge base. Both topics
expect a simple string with 3 tokens separated by spaces (if the object is a
literal string, use double quotes to escape it).
It also exposes the following services:
-
/kb/revise
to add/remove facts using a synchronous interface -
/kb/query
to perform simple queries -
/kb/sparql
to perform complex queries (full SPARQL end-point) -
/kb/events
to subscribe to 'events' by providing a (set of) partially-bound triples. Calling the service returns an event id. Subscribe then to/kb/events/<id>
to be notified everytime a new instance/class match the provided pattern -
/kb/manage
to manage the knowledge base (including eg clearing all the facts)
Features
Server-Client or embedded
KnowledgeCore
can be run as a stand-alone (socket) server, or directly embedded
in Python applications.
Multi-models
KnowledgeCore
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
KnowledgeCore
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
KnowledgeCore
provides RDFS/OWL reasoning capabilities via the
reasonable reasoner.
See reasonable README for the exact level of support of the different OWL2 RL rules.
Transient knowledge
KnowledgeCore
allows to attach 'lifespans' to statements: after a given duration,
they are automatically collected.
[this functionality is currently disabled. Please open an issue of you need it urgently]
Ontology walking
KnowledgeCore
exposes several methods to explore the different ontological models
of the knowledge base. It is compatible with the visualization tool
oro-view.