Knowledge Base Population using Semantic Label Propagation

TL;DR

This paper introduces Semantic Label Propagation, a method that enhances training data quality for relation extractors by combining distant supervision with semantic similarity, significantly improving knowledge base population with minimal manual effort.

Contribution

The paper presents Semantic Label Propagation, a novel technique that extends noisy training sets using semantic similarity, boosting relation extraction performance with minimal manual labeling.

Findings

Semantic Label Propagation improves precision and recall in relation extraction.

The method achieves substantial performance gains over existing approaches.

Manual annotation effort is nearly eliminated with the proposed strategy.

Abstract

A crucial aspect of a knowledge base population system that extracts new facts from text corpora, is the generation of training data for its relation extractors. In this paper, we present a method that maximizes the effectiveness of newly trained relation extractors at a minimal annotation cost. Manual labeling can be significantly reduced by Distant Supervision, which is a method to construct training data automatically by aligning a large text corpus with an existing knowledge base of known facts. For example, all sentences mentioning both 'Barack Obama' and 'US' may serve as positive training instances for the relation born_in(subject,object). However, distant supervision typically results in a highly noisy training set: many training sentences do not really express the intended relation. We propose to combine distant supervision with minimal manual supervision in a technique called feature labeling, to eliminate noise from the large and noisy initial training set, resulting in a significant increase of precision. We further improve on this approach by introducing the Semantic Label Propagation method, which uses the similarity between low-dimensional representations of candidate training instances, to extend the training set in order to increase recall while maintaining high precision. Our proposed strategy for generating training data is studied and evaluated on an established test collection designed for knowledge base population tasks. The experimental results show that the Semantic Label Propagation strategy leads to substantial performance gains when compared to existing approaches, while requiring an almost negligible manual annotation effort.