Document Structure aware Relational Graph Convolutional Networks for Ontology Population

• URL: http://arxiv.org/abs/2104.12950v1
• Date: Tue, 27 Apr 2021 02:50:39 GMT
• Title: Document Structure aware Relational Graph Convolutional Networks for Ontology Population
• Authors: Abhay M Shalghar, Ayush Kumar, Balaji Ganesan, Aswin Kannan, Shobha G
• Abstract summary: We look at 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 form of RCN model.
• Score: 1.076210145983805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ontologies comprising of concepts, their attributes, and relationships, form the quintessential backbone of many knowledge based AI systems. These systems manifest in the form of question-answering or dialogue in number of business analytics and master data management applications. 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.

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