# A Neural Architecture for Person Ontology population

arXiv: https://arxiv.org/abs/2001.08013 · HTML: https://mlsanity.com/paper/2001.08013/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2020.

## Built on by (most cited first, 2 of 2)

- [An Ecosystem for Personal Knowledge Graphs: A Survey and Research Roadmap](https://mlsanity.com/paper/2304.09572/) (2023)
- [A Deep Learning Approach for Ontology Enrichment from Unstructured Text](https://mlsanity.com/paper/2112.08554/) (2021)

## Abstract (arXiv)

A person ontology comprising concepts, attributes and relationships of people has a number of applications in data protection, didentification, population of knowledge graphs for business intelligence and fraud prevention. While artificial neural networks have led to improvements in Entity Recognition, Entity Classification, and Relation Extraction, creating an ontology largely remains a manual process, because it requires a fixed set of semantic relations between concepts. In this work, we present a system for automatically populating a person ontology graph from unstructured data using neural models for Entity Classification and Relation Extraction. We introduce a new dataset for these tasks and discuss our results.