Graph Neural Networks as the Copula Mundi between Logic and Machine Learning: A Roadmap


pagemagnifierBibTeX_logo.pngmagnifierpage_white_acrobatmagnifier

Andrea Agiollo, Giovanni Ciatto, Andrea Omicini

Roberta Calegari, Giovanni Ciatto, Enrico Denti, Andrea Omicini, Giovanni Sartor (eds.)
“WOA 2021 – 22nd Workshop “From Objects to Agents””, pages 98–115
CEUR Workshop Proceedings (AI*IA Series) 2963
Sun SITE Central Europe, RWTH Aachen University
October 2021

Combining machine learning (ML) and computational logic (CL) is hard, mostly because of the inherently-different ways they use to represent knowledge. In fact, while ML relies on fixed-size numeric representations leveraging on vectors, matrices, or tensors of real numbers, CL relies on logic terms and clauses—which are unlimited in size and structure.
Graph neural networks (GNN) are a novelty in the ML world introduced for dealing with graph-structured data in a sub-symbolic way. In other words, GNN pave the way towards the application of ML to logic clauses and knowledge bases. However, there are several ways to encode logic knowledge into graphs: which is the best one heavily depends on the specific task at hand.
Accordingly, in this paper, we (i) elicit a number of problems from the field of CL that may benefit from many graph-related problems where GNN has been proved effective; (ii) exemplify the application of GNN to logic theories via an end-to-end toy example, to demonstrate the many intricacies hidden behind the technique; (iii) discuss the possible future directions of the application of GNN to CL in general, pointing out opportunities and open issues.

(keywords) Graph Neural Networks, Machine Learning, Embedding, Computational Logic

Talks

Events

  • 22nd Workshop “From Objects to Agents” (WOA 2021) — 01/09/2021–03/09/2021

Publications

Publication

— authors

— editors

— status

published

— sort

paper in proceedings

— publication date

October 2021

— volume

WOA 2021 – 22nd Workshop “From Objects to Agents”

— series

CEUR Workshop Proceedings / AI*IA Series

— volume

2963

— pages

98–115

— article no.

8

— number of pages

18

URLs

original page

identifiers

— DBLP

conf/woa/AgiolloCO21

— IRIS

11585/834362

— Scholar

817372786663443317

— Scopus

2-s2.0-85116916925

— print ISSN

1613-0073

Partita IVA: 01131710376 — Copyright © 2008–2023 APICe@DISI – PRIVACY