A knowledge representation service (aka Leolani's Brain). This service expects structures data and outputs an RDF graph.
This package contains the necessary functionality for creating an RDF episodic knowledge graph. It features:
- Representation of experiences and their context
- Storing of learned facts, their mentions/references, and the perspectives expressed
- Querying of the graph as a way of accessing memories created during previous experiences
- Typing of incoming information through querying of external knowledge sources like DBpedia and Wikidata
- Location reasoning for guessing where a new experience is taking place, based on previous locations
- Thoughts and drives that arise from new information added to the graph. For example
- conflicts resulting from learned facts,
- curiosity based on knowledge gaps,
- generalization via shared characteristics across people or objects
- Trust calculation of agents as sources of information. The trust value is based on:
- the number of interaction with this agent,
- the number of new content the agent has provided,
- the number of conflicting information it has provided
This repository uses Python >= 3.7
Be sure to run in a virtual python environment (e.g. conda, venv, mkvirtualenv, etc.)
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In the root directory of this repo run
pip install -e . python -c "import nltk; nltk.download('wordnet')"
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Additionally, you need to install GraphDB Free with a repository named
sandbox. You will need to launch this before running the package.
For using this repository as a package for different project and on a different virtual environment, you may
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install a published version from PyPI:
pip install cltl.brain
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or, for the latest snapshot, run:
pip install git+git://github.com/leolani/cltl-knowledgerepresentation.git@main
Then you can import it in a python script as:
import cltl.brain
You can also modify the logger level as such:
import logging
from cltl.brain import logger as brain_logger
brain_logger.setLevel(logging.ERROR)Please take a look at the example scripts provided to get an idea on how to run and use this package. Each example has a comment at the top of the script describing the behaviour of the script.
For these example scripts, you need
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A repository on GraphDB Free called
sandbox. To run any example script you first have to launch GraphDB, and then you can run the example script. -
To change your current directory to
./examples/ -
Run some examples (e.g.
python carl.py)
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License.
See LICENSE for more information.