Companion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation Learning
This work addresses a domain-specific problem for veterinary diagnostics by enhancing knowledge graph representations with literal data, though it is incremental as it builds on existing embedding methods.
The paper tackles the challenge of incorporating diverse literal information into knowledge graph embeddings for companion animal disease diagnostics, proposing LiteralKG which fuses graph structure and literals via gate networks and self-supervised learning, achieving improved performance in link prediction tasks over state-of-the-art baselines.
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.