# Data Augmentation for Personal Knowledge Base Population

arXiv: https://arxiv.org/abs/2002.10943 · HTML: https://mlsanity.com/paper/2002.10943/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [Document Structure aware Relational Graph Convolutional Networks for Ontology Population](https://mlsanity.com/paper/2104.12950/) (2021)
- [Reimagining GNN Explanations with ideas from Tabular Data](https://mlsanity.com/paper/2106.12665/) (2021)
- [xEM: Explainable Entity Matching in Customer 360](https://mlsanity.com/paper/2212.00342/) (2022)

## Abstract (arXiv)

Cold start knowledge base population (KBP) is the problem of populating a knowledge base from unstructured documents. While artificial neural networks have led to significant improvements in the different tasks that are part of KBP, the overall F1 of the end-to-end system remains quite low. This problem is more acute in personal knowledge bases, which present additional challenges with regard to data protection, fairness and privacy. In this work, we present a system that uses rule based annotators and a graph neural network for missing link prediction, to populate a more complete, fair and diverse knowledge base from the TACRED dataset.