Using Ontology Embeddings With Deep Learning Architectures to Improve Prediction of Ontology Concept

Full Title: Using Ontology Embeddings With Deep Learning Architectures to Improve Prediction of Ontology Concepts From Literature

Abstract:
Natural language processing methods powered by deep learning have been well-studied over the past years for the task of automated ontology-based annotation of scientific literature. Many of these approaches focus solely on learning associations between text and ontology concepts and use that to annotate new text. However, a great deal of information is embedded in the ontology structure and semantics. Here, we present deep learning architectures that learn not only associations between text and ontology concepts but also the structure of the ontology. Our experiments show that creating architectures that are capable of learning the structure of the ontology result in enhanced annotation performance.

Presented by Pratik Devkota on September 1, 2023

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