Log In Sign Up

Knowledge Base Population using Semantic Label Propagation

by   Lucas Sterckx, et al.

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.


page 1

page 2

page 3

page 4


Interactive Knowledge Base Population

Most work on building knowledge bases has focused on collecting entities...

False Positive and Cross-relation Signals in Distant Supervision Data

Distant supervision (DS) is a well-established method for relation extra...

CANDiS: Coupled & Attention-Driven Neural Distant Supervision

Distant Supervision for Relation Extraction uses heuristically aligned t...

Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents

Distant supervision based methods for entity and relation extraction hav...

Dual Supervision Framework for Relation Extraction with Distant Supervision and Human Annotation

Relation extraction (RE) has been extensively studied due to its importa...

Distant IE by Bootstrapping Using Lists and Document Structure

Distant labeling for information extraction (IE) suffers from noisy trai...

Does Recommend-Revise Produce Reliable Annotations? An Analysis on Missing Instances in DocRED

DocRED is a widely used dataset for document-level relation extraction. ...