A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning

TL;DR

This paper compares symbolic and distributional methods for relational learning on knowledge graphs, analyzing their strengths, weaknesses, and rule complexities to guide approach selection.

Contribution

It provides a comparative analysis of symbolic and distributional paradigms for relational learning, highlighting their respective advantages and challenges.

Findings

Preliminary results suggest indicators for choosing between approaches.

Analysis of rule complexity differences.

Insights into strengths and weaknesses of each paradigm.

Abstract

Many real-world domains can be expressed as graphs and, more generally, as multi-relational knowledge graphs. Though reasoning and learning with knowledge graphs has traditionally been addressed by symbolic approaches, recent methods in (deep) representation learning has shown promising results for specialized tasks such as knowledge base completion. These approaches abandon the traditional symbolic paradigm by replacing symbols with vectors in Euclidean space. With few exceptions, symbolic and distributional approaches are explored in different communities and little is known about their respective strengths and weaknesses. In this work, we compare representation learning and relational learning on various relational classification and clustering tasks and analyse the complexity of the rules used implicitly by these approaches. Preliminary results reveal possible indicators that could help in choosing one approach over the other for particular knowledge graphs.