TypeDB-ML is the Machine Learning integrations library for TypeDB
Documentation: https://github.com/vaticle/typedb-ml
PyPI package: https://pypi.org/project/typedb-ml Available through https://pypi.org
pip install -r requirements.txt
pip install typedb-client==0.3.0
This release adds new features to the KGCN project.
The new features included in this release are:
This release marks the second major iteration of KGCNs. This new KGCN framework is designed to provide a versatile means of performing learning tasks over a Grakn knowledge graph, including:
At present, Relation prediction is well-implemented. The other functionalities mentioned here will be tackled in future releases.
A KGCN is now a learned message-passing graph algorithm. Neural network components are learned, and are used to transform signals that are passed around the graph. This approach is convolutional due to the fact that the same transformation is applied to all edges and another is applied to all nodes. It may help your understanding to analogise this to convolution over images, where the same transformation is applied over all pixel neighbourhoods.
This approach leverages DeepMind's Graph Nets framework, detailed in their paper. This work is a generalisation of graph learning approaches, which offers plenty of ways to structure learning tailored to various knowledge graph problems.
This release includes a methodology for automatically generating small Grakn Knowledge Graphs based on a Probability Mass Function. This is used for the KGCN example, and allows us to be certain that the graphs we learn on contain sufficient information for good predictions to be made by the learner.
The KGCN project comes with a full example that acts as a template for users to create a KGCN for their own domain.
Presently KGCNs can ingest Entities, Relations and Attributes. However, attribute values can only be ingested if they are categorical in nature, since only categorical embedding components are included. Support for embedding continuous valued attributes will be added in the next release.
The first release of kglib introduces Knowledge Graph Convolutional Networks
This release improves upon the prereleases as follows:
Supports supervised multi-class classification for both single-label and multi-label cases
Works stably with Grakn Core 1.5.3
Includes a simplified input data pipeline (which creates the arrays fed to the TensorFlow model)
Fixes grakn-client
dependency issue, now depending upon a client release rather than a test server
Now in CI only end-to-end tests use Grakn 1.5.3 with pre-loaded data, integration and unit tests use the plain Grakn 1.5.3 release
grakn-kglib
from the PyPi test serverAddresses #35 #36 #37 #31 #30
grakn-kglib
installed from PyPianimaltrade
example requirements reference the general KGCN requirementsanimaltrade
example in the KGCN READMEAddresses #37 #38
The first release of Grakn kglib!
The first included project is Knowledge Graph Convolutional Networks, with example usage demonstrated on CITES animal trade data.