A code base for Automated Relational Feature Engineering
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Updated
Apr 18, 2023 - Python
A code base for Automated Relational Feature Engineering
Implementation of a learning and fragment-based rule inference engine -- M. Svatoš, S. Schockaert, J. Davis, and O. Kuželka: STRiKE: Rule-driven relational learning using stratified k-entailment, ECAI'20
Computes contingency tables for relational databases, i.e. counts across tables
A package for generating Relational Features for PDDL Planning
Implementation of the framework in the paper: Waegeman, W., Pahikkala, T., Airola, A., Salakoski, T., Stock, M., & De Baets, B. (2012). A kernel-based framework for learning graded relations from data. IEEE Transactions on Fuzzy Systems, 20(6), 1090-1101.
A grammar and linter for ILP datasets.
Experimental setup and results for 2021-2022 academic research "Effects of knowledge graph structural properties on their predictive performance".
This is the project repo associated with the paper "Disentangling and Integrating Relational and Sensory Information in Transformer Architectures" by Awni Altabaa, John Lafferty
srlearn-compatible relational datasets
Project repository for MA6040: Fuzzy Logic Connectives: Theory and Applications offered in Spring 2019
Lossless Compression of Structured Convolutional Models via Lifting
Julia package for fetching and using srlearn-compatible relational datasets.
Word embeddings for Transfer Learning using Relational Dependency Networks
Distributed Non Negative RESCAL decomposition with estimation of latent features
Learning for planning architecture using both classical and deep learning methods.
🐍🚧 Experimental tool for SRL learning in Python. For something more stable, see: https://github.com/srlearn/srlearn
Python package for fetching and using srlearn-compatible relational datasets.
Beyond Graph Neural Networks with Lifted Relational Neural Networks
[Accepted by TNNLS] Source Code for Relational Redundancy-Free Graph Clustering
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