# Differentiable Representations For Multihop Inference Rules

## TL;DR

This paper introduces differentiable methods for second-order multi-hop reasoning over large symbolic knowledge bases, enabling scalable, efficient neural models that perform well on complex reasoning tasks.

## Contribution

It presents a new operation for compositional multi-hop reasoning and evaluates various implementations for scalability and performance.

## Key findings

- Techniques scale to KBs with millions of entities and triples
- Models achieve competitive performance on multi-hop reasoning tasks
- New operation improves compositional reasoning capabilities

## Abstract

We present efficient differentiable implementations of second-order multi-hop reasoning using a large symbolic knowledge base (KB). We introduce a new operation which can be used to compositionally construct second-order multi-hop templates in a neural model, and evaluate a number of alternative implementations, with different time and memory trade offs. These techniques scale to KBs with millions of entities and tens of millions of triples, and lead to simple models with competitive performance on several learning tasks requiring multi-hop reasoning.