# Quantifying Similarity between Relations with Fact Distribution

## TL;DR

This paper presents a simple neural network-based method to measure relation similarity in knowledge bases, correlating well with human judgments and improving relation detection and classification tasks.

## Contribution

It introduces a sampling-based approach to approximate relation similarity via divergence of conditional distributions, enhancing relation detection and classification.

## Key findings

- Significant correlation with human judgments
- Effective detection of redundant relations
- Improved relation classification accuracy

## Abstract

We introduce a conceptually simple and effective method to quantify the similarity between relations in knowledge bases. Specifically, our approach is based on the divergence between the conditional probability distributions over entity pairs. In this paper, these distributions are parameterized by a very simple neural network. Although computing the exact similarity is in-tractable, we provide a sampling-based method to get a good approximation. We empirically show the outputs of our approach significantly correlate with human judgments. By applying our method to various tasks, we also find that (1) our approach could effectively detect redundant relations extracted by open information extraction (Open IE) models, that (2) even the most competitive models for relational classification still make mistakes among very similar relations, and that (3) our approach could be incorporated into negative sampling and softmax classification to alleviate these mistakes. The source code and experiment details of this paper can be obtained from https://github.com/thunlp/relation-similarity.