Integrating Heterogeneous Domain Information into Relation Extraction: A Case Study on Drug-Drug Interaction Extraction

Understand

The development of deep neural networks has improved representation learning in various domains, including textual, graph structural, and relational triple representations.

• This development opened the door to new relation extraction beyond the traditional text-oriented relation extraction.
• However, research on the effectiveness of considering multiple heterogeneous domain information simultaneously is still under exploration, and if a model can take an advantage of integrating heterogeneous information, it is expected to exhibit a significant contribution to many problems in the world.
• This thesis works on Drug-Drug Interactions (DDIs) from the literature as a case study and realizes relation extraction utilizing heterogeneous domain information.