# Companion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation Learning

arXiv: https://arxiv.org/abs/2309.03219 · HTML: https://mlsanity.com/paper/2309.03219/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 58 references that are in the map.
- First posted on arXiv 31 Aug 2023.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Simple and Deep Graph Convolutional Networks](https://mlsanity.com/paper/2007.02133/) (2020)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [KGAT: Knowledge Graph Attention Network for Recommendation](https://mlsanity.com/paper/1905.07854/) (2019)
- [Graph-Bert: Only Attention is Needed for Learning Graph Representations](https://mlsanity.com/paper/2001.05140/) (2020)
- [Learning to Diagnose with LSTM Recurrent Neural Networks](https://mlsanity.com/paper/1511.03677/) (2015)
- [A Survey on Knowledge Graph Embeddings with Literals: Which model links better Literal-ly?](https://mlsanity.com/paper/1910.12507/) (2019)
- [Incorporating Literals into Knowledge Graph Embeddings](https://mlsanity.com/paper/1802.00934/) (2018)
- [Multi-task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs](https://mlsanity.com/paper/1708.04828/) (2017)
- [Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity](https://mlsanity.com/paper/2308.09517/) (2023)
- [EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning](https://mlsanity.com/paper/1709.06908/) (2017)
- [Connector 0.5: A unified framework for graph representation learning](https://mlsanity.com/paper/2304.13195/) (2023)

## Built on by (most cited first, 4 of 4)

- [A Survey on Structure-Preserving Graph Transformers](https://mlsanity.com/paper/2401.16176/) (2024)
- [Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures](https://mlsanity.com/paper/2312.16788/) (2023)
- [Halal or Not: Knowledge Graph Completion for Predicting Cultural Appropriateness of Daily Products](https://mlsanity.com/paper/2501.05768/) (2025)
- [Mitigating Degree Bias in Graph Representation Learning with Learnable Structural Augmentation and Structural Self-Attention](https://mlsanity.com/paper/2504.15075/) (2025)

## Abstract (arXiv)

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.