# Document Structure aware Relational Graph Convolutional Networks for Ontology Population

arXiv: https://arxiv.org/abs/2104.12950 · HTML: https://mlsanity.com/paper/2104.12950/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 27 Apr 2021.

## Builds on (most cited first, 10 of 10)

- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Graph Convolution over Pruned Dependency Trees Improves Relation Extraction](https://mlsanity.com/paper/1809.10185/) (2018)
- [Representation Learning for Attributed Multiplex Heterogeneous Network](https://mlsanity.com/paper/1905.01669/) (2019)
- [TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network](https://mlsanity.com/paper/2001.09522/) (2020)
- [Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection](https://mlsanity.com/paper/1612.04460/) (2016)
- [Relation-aware Graph Attention Model With Adaptive Self-adversarial Training](https://mlsanity.com/paper/2102.07186/) (2021)
- [Link Prediction using Graph Neural Networks for Master Data Management](https://mlsanity.com/paper/2003.04732/) (2020)
- [On2Vec: Embedding-based Relation Prediction for Ontology Population](https://mlsanity.com/paper/1809.02382/) (2018)
- [Data Augmentation for Personal Knowledge Base Population](https://mlsanity.com/paper/2002.10943/) (2020)
- [Fine Grained Classification of Personal Data Entities](https://mlsanity.com/paper/1811.09368/) (2018)

## Built on by (most cited first, 1 of 1)

- [Similar Cases Recommendation using Legal Knowledge Graphs](https://mlsanity.com/paper/2107.04771/) (2021)

## Abstract (arXiv)

Ontologies comprising of concepts, their attributes, and relationships are used in many knowledge based AI systems. While there have been efforts towards populating domain specific ontologies, we examine the role of document structure in learning ontological relationships between concepts in any document corpus. Inspired by ideas from hypernym discovery and explainability, our method performs about 15 points more accurate than a stand-alone R-GCN model for this task.