Reason-able Embeddings: Learning Concept Embeddings with a Transferable Neural Reasoner
Max Adamski, Jędrzej Potoniec
[Paper]
[BibTex]
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Reason-able concept embeddings are learned in a data-driven way, by simply asking queries about logical consequences for a given knowledge base.
Our method uses a logical consequence (entailment) classifier based on a recursive neural network, so after learning embeddings, one can both construct embeddings of arbitrarily complex concepts, and use the resulting classifier to perform fast approximate reasoning.
A significant part of our reasoner (the reasoner head) is transferable across knowledge bases in the ALC description logic, including real-world knowledge bases.
Hopefully, reason-able concept embeddings and neural reasoners will allow for greater use of symbolic knowledge in machine learning architectures.
Notable files in this repository are described below. In parentheses, we indicate if a figure, table, or experiment was based on a given file.
reasonable-embeddings/
├─ factplusplus/ # Git submodule <https://bitbucket.org/dtsarkov/factplusplus/>
├─ scripts/
│ └─ build # Builds FaCT++ and our Cython extensions
├─ src/
│ ├─ simplefact/
│ │ ├─ factpp.pyx # Python bindings to the FaCT++ reasoner
│ │ ├─ owlfun.pyx # OWL functional style syntax parser
│ │ └─ syntax.py # Constants used to construct concept expressions
│ ├─ exp1.ipynb # Training the relaxed reasoner on the synthetic data set (Fig. 4, Table 4)
│ ├─ exp2.ipynb # Training the restricted reasoner on the synthetic data set (Fig. 3, Table 2, 3)
│ ├─ exp3.ipynb # Training the restricted reasoner on the pizza taxonomy
│ ├─ exp4.ipynb # Learning and visualizing embeddings of arbitrary concepts in the pizza ontology (Fig. 5, Table 6)
│ ├─ exp5.ipynb # Evaluating reasoner head transfer from sythetic data to real-world ontologies (Table 7, 10)
│ ├─ extra.ipynb # Finding axioms that are hard for the reasoner (Table 9)
│ ├─ tests.ipynb # Results of statistical tests (Table 5, 8)
│ ├─ reasoner.py # Implementation of the relaxed reasoner and the training procedure
│ └─ generate.py # Axiom generator, synthetic data set generator, and data set loading and saving procedures
└─ local/out/
├─ dataset/
│ ├─ sub-100.json # The synthetic data set used (Experiment 1, 2)
│ └─ *.ofn # Pizza ontology and ontologies from the CQ2SPARQLOWL data set,
│ # converted to OWL functional style syntax (Experiment 3, 4)
└─ exp/
├─ 20220715T194328/ # Artifacts of exp1.ipynb
├─ 20220715T194304/ # Artifacts of exp2.ipynb
├─ 20220719T213232/ # Artifacts of exp5.ipynb
└─ table3-k...e../ # Artifacts of exp2.ipynb, with a set embedding size,
# and number of neurons in the reasoner head
You will need to install Python 3.9, Jupyter and all required dependencies. The easiest way to do this is with Conda. If you don't have conda, install a distribution (for example miniconda). Note that the code was only tested on Linux.
If you want to use conda, then create and enter a new environment with the following commands. If not, skip to the next step.
conda create --name reasoner python=3.9 jupyter
conda activate reasoner
Install the requirements.
pip install -r requirements.txt
With the requirements in place, run the build script for the FaCT++ reasoner and our Python bindings. Of course, you will need to clone the factplusplus submodule repository into the factplusplus/ directory, if you haven't done so already (only required if you cloned our repository from Github and didn't use the --recurse-submodules flag).
chmod +x scripts/build
scripts/build
Lastly, start Jupyter.
jupyter notebook
To re-run an experiment open a Jupyter notebook in a web browser and click Restart & Run All. Results of experiments are shown in notebooks. In addition to that, some experiments save results (including model weights) on disk in a directory based on the time stamp (for example, local/out/exp/20220715T194304 stores the result of exp2.ipynb).
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