Companion Animal Disease Diagnostics based on Literal-aware Medical
  Knowledge Graph Representation Learning
Van Thuy Hoang Sang Thanh Nguyen Sangmyeong Lee Jooho Lee Luong Vuong Nguyen O-Joun Lee
Knowledge graph (KG) embedding has been used to benefit the diagnosis of
animal diseases by analyzing electronic medical records (EMRs), such as notes
and veterinary records. However, learning representations to capture entities
and relations with literal information in KGs is challenging as the KGs show
heterogeneous properties and various types of literal information. Meanwhile,
the existing methods mostly aim to preserve graph structures surrounding target
nodes without considering different types of literals, which could also carry
significant information. In this paper, we propose a knowledge graph embedding
model for the efficient diagnosis of animal diseases, which could learn various
types of literal information and graph structure and fuse them into unified
representations, namely LiteralKG. Specifically, we construct a knowledge graph
that is built from EMRs along with literal information collected from various
animal hospitals. We then fuse different types of entities and node feature
information into unified vector representations through gate networks. Finally,
we propose a self-supervised learning task to learn graph structure in pretext
tasks and then towards various downstream tasks. Experimental results on link
prediction tasks demonstrate that our model outperforms the baselines that
consist of state-of-the-art models. The source code is available at
https://github.com/NSLab-CUK/LiteralKG.