Knowledge injection of Datalog rules via Neural Network Structuring with KINS
by Matteo Magnini, Giovanni Ciatto, and Andrea Omicini
We propose a novel method to inject symbolic knowledge in form of Datalog formulæ into neural networks (NN), called Knowledge Injection via Network Structuring (KINS). The idea behind our method is to extend NN internal structure with ad-hoc layers built out of the injected symbolic knowledge. KINS does not constrain NN to any specific architecture, neither requires logic formulæ to be ground. Moreover, it is robust w.r.t. both lack of data and imperfect/incomplete knowledge. Experiments are reported, involving multiple datasets and predictor types, to demonstrate how KINS can significantly improve the predictive performance of the neural networks it is applied to.
- DOI: https://doi.org/10.1093/logcom/exad037
- URL: https://academic.oup.com/logcom/article/33/8/1832/7190990
@article{kins-jlc33,
author = {Magnini, Matteo and Ciatto, Giovanni and Omicini, Andrea},
dblp = {journals/logcom/MagniniCO23},
doi = {10.1093/logcom/exad037},
eissn = {1465-363X},
iris = {11585/950567},
issn = {0955-792X},
journal = {Journal of Logic and Computation},
keywords = {neural network, expalinable AI, symbolic knowledge injection, KINS, PSyKI},
month = dec,
number = 8,
numpages = 19,
pages = {1832--1850},
publisher = {Oxford University Press},
scholar = {2353304508513748358},
scopus = {2-s2.0-85179896166},
semanticscholar = {266726315},
title = {Knowledge injection of {D}atalog rules via Neural Network Structuring with {KINS}},
url = {https://academic.oup.com/logcom/article/33/8/1832/7190990},
volume = 33,
wos = {WOS:001003002200001},
year = 2023
}