RelNN is a novel first-order deep neural model for relational learning.
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Updated
Nov 15, 2017 - Java
RelNN is a novel first-order deep neural model for relational learning.
Computes contingency tables for relational databases, i.e. counts across tables
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.
Project repository for MA6040: Fuzzy Logic Connectives: Theory and Applications offered in Spring 2019
🐍🚧 Experimental tool for SRL learning in Python. For something more stable, see: https://github.com/srlearn/srlearn
A largely incomplete but hopefully useful list of links to datasets for relational learning and inductive logic programming. No guarantees on availability.
SimplE Embedding for Link Prediction in Knowledge Graphs
Lossless Compression of Structured Convolutional Models via Lifting
A grammar and linter for ILP datasets.
Beyond Graph Neural Networks with Lifted Relational Neural Networks
Machine learning on knowledge graphs for context-aware security monitoring (data and model)
Experimental setup and results for 2021-2022 academic research "Effects of knowledge graph structural properties on their predictive performance".
Readings for "A Unified View of Relational Deep Learning for Drug Pair Scoring." (IJCAI 2022)
Julia package for fetching and using srlearn-compatible relational datasets.
srlearn-compatible relational datasets
Python package for fetching and using srlearn-compatible relational datasets.
☕ A Python library for gradient-boosted statistical relational models / learning probabilistic relational programs.
Code and data to the publication "SpikE: spike-based embeddings for multi-relational graph data".
A code base for Automated Relational Feature Engineering
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