# Fact Discovery from Knowledge Base via Facet Decomposition

## TL;DR

This paper introduces a new task called fact discovery from knowledge bases, focusing on discovering facts related to a known head entity, and proposes a facet decomposition framework with auto-encoder and feedback components, showing promising results.

## Contribution

It presents a novel fact discovery task and a facet decomposition framework with auto-encoder and feedback mechanisms, addressing limitations of existing KB completion methods.

## Key findings

- Framework achieves promising results on benchmark datasets.
- Effective in discovering different kinds of facts.
- Extensive analysis demonstrates framework's versatility.

## Abstract

During the past few decades, knowledge bases (KBs) have experienced rapid growth. Nevertheless, most KBs still suffer from serious incompletion. Researchers proposed many tasks such as knowledge base completion and relation prediction to help build the representation of KBs. However, there are some issues unsettled towards enriching the KBs. Knowledge base completion and relation prediction assume that we know two elements of the fact triples and we are going to predict the missing one. This assumption is too restricted in practice and prevents it from discovering new facts directly. To address this issue, we propose a new task, namely, fact discovery from knowledge base. This task only requires that we know the head entity and the goal is to discover facts associated with the head entity. To tackle this new problem, we propose a novel framework that decomposes the discovery problem into several facet discovery components. We also propose a novel auto-encoder based facet component to estimate some facets of the fact. Besides, we propose a feedback learning component to share the information between each facet. We evaluate our framework using a benchmark dataset and the experimental results show that our framework achieves promising results. We also conduct extensive analysis of our framework in discovering different kinds of facts. The source code of this paper can be obtained from https://github.com/thunlp/FFD.