Document Structure aware Relational Graph Convolutional Networks for Ontology Population
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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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 25 references that are in the map. First posted on arXiv 27 Apr 2021.
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10 of 25 references.
References · 10
- 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
- 2018Graph Convolution over Pruned Dependency Trees Improves Relation Extractioncited by 146
- 2019Representation Learning for Attributed Multiplex Heterogeneous Networkcited by 91
- 2020TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Networkcited by 37
- 2016Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detectioncited by 13
- 2021Relation-aware Graph Attention Model With Adaptive Self-adversarial Trainingcited by 12
- 2020Link Prediction using Graph Neural Networks for Master Data Managementcited by 8
- 2018On2Vec: Embedding-based Relation Prediction for Ontology Populationcited by 6
- 2020Data Augmentation for Personal Knowledge Base Populationcited by 3
- 2018Fine Grained Classification of Personal Data Entitiescited by 2
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Citing papers per year: 2021: 1.
1 paper.
- 2021Similar Cases Recommendation using Legal Knowledge Graphs10 Jul 2021