Represent Knowledge Graphs as tensors to perform logic calculus and regression.
This module can be used for different tasks, as demonstrated in the examples folder.
Given a set of logical formulas a Markov Logic Network can be created using the Basis Calculus. One can then sample from the model to generate random data, in this case interpreted as a Random Knowledge Graph.
An example can be found in examples/generation/generate_accounting_kg.py.
Given a Knowledge Graph and positive and negative examples (each a pair of individuals), one can learn a logical formula true on the positive and false on the negative examples. To this end optimization via Alternating Least Squares has been implemented.
Examples can be found in examples/learning/.
Coordinate Calculus: CoordinateCalculus main class for coordinate-based calculus of logical formulas.
Basis Calculus: BasisCalculus main class for basis-vector-based calculus of logical formulas.
Expression Calculus: Evaluation of expressions given dictionaries of CoordinateCalculus/BasisCalculus objects.
generalized_als.py Performs the Alternating Least Squares to solve tensor regression problems.
expression_learning.py Optimizes formulas using Coordinate Calculus and the Alternating Least Squares.
mln_learning.py Learns Markov Logic Networks based on data.
markov_logic_network.py Creates a Markov Logic Network using Basis Calculus based on pgmpy.models.MarkovNetwork.
On KG represented in turtle files:
ttl_to_csv.py Transform turtle file into a DataFrame containing facts.
factdf_to_cores.py Transforms the fact DataFrame into CoordinateCalculus Cores in the variable-based representation.
pairdf_to_cores.py Uses the pair DataFrame to initialize the targetCore.
sampledf_to_cores.py Transform sample DataFrame into CoordinateCalculus Cores in the atom-based representation.