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MHRE: Multivariate link prediction method for medical hyper-relational facts

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Abstract

As hyper-relational facts continue to proliferate within knowledge graphs, link prediction on binary relations has become inadequate, while link prediction on hyper-relations has emerged as a research hotspot. Existing methods typically employ n-ary tuples, primary triple with auxiliary descriptions, or hypergraphs to represent hyper-relational facts and conduct link prediction. However, medical hyper-relational facts are more intricate and frequently lack multiple components, which presents challenges for current methods in conveying their structure, semantics, and predicting multiple missing elements simultaneously. To address these issues, in this paper, we introduce MHRE, the pioneering link prediction method specifically designed for medical hyper-relational facts. Initially, we represent medical hyper-relational facts as a heterogeneous multi-relational directed graph with hyper-relations at its core to depict both its structure and implicit semantics. Next, we develop a role-aware graph attention mechanism network to acquire distributed vector representations of entities and relations within the graph. Importantly, it fine-tunes the semantic weights of different components within hyper-relational facts by incorporating neighboring nodes and role information through learning. Lastly, we devise a prediction module based on self-attention mechanisms, enabling the simultaneous prediction of multiple missing elements within a medical hyper-relational fact. We conduct experiments using publicly available datasets, such as JF17K, WikiPeople, and their adapted versions, alongside a proprietary medical dataset. We compare MHRE with state-of-the-art baselines and further conduct ablation studies and parameter analysis. The experimental results confirm the efficacy and superiority of MHRE. In a range of benchmark tests involving hyper-relational facts, MHRE consistently outperforms current state-of-the-art methods.

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Data Availability

The JF17K and WikiPeople datasets are publicly available at https://github.com/PaddlePaddle/Research/tree/master/KG/ACL2021_GRAN. The MedCKG dataset is not currently available as a private dataset.

Notes

  1. https://www.w3.org

  2. https://schema.org/

  3. https://github.com/lijp12/SIR/

  4. https://github.com/gsp2014/NaLP

  5. https://github.com/PaddlePaddle/Research/tree/master/KG/ACL2021_GRAN

  6. https://github.com/liuyuaa/GETD

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Acknowledgements

We would like to thank the anonymous reviewers for their insightful suggestions. Our work is supported by the National Key Research and Development Program of China (No.2020AAA0109400).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning, China

    Weiguang Wang, Xuanyi Zhang & Xia Zhang

  2. Neusoft Research of Intelligent Healthcare Technology, Co. Ltd, Shenyang, 110167, Liaoning, China

    Weiguang Wang, Wei Cai & Xia Zhang

  3. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China

    Juan Zhang

  4. School of Computer Science, Peking University, Beijing, 100871, China

    Haiyan Zhao

  5. Key Laboratory of High Confidence Software Technologies (PKU), Ministry of Education, Beijing, 100871, China

    Haiyan Zhao

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  1. Weiguang Wang
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Contributions

Author 1 (First Author) designed the study, performed measurements, designed the analysis, and wrote the manuscript. Author 2 designed the medical schema and extracted the data. Author 3 designed the analysis and the medical schema. Author 4 designed the analysis and the medical schema. Author 5 designed the study and the analysis. Author 6 designed the study and the analysis. All authors contributed to the article and approved the submitted version.

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Correspondence to Xia Zhang.

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Appendices

Appendix A: Hyperparameter settings

We tuned our model on the validation set using the hyperparameters reported in Table 7. Bold values are fixed across all the datasets. The characters in brackets are shorthand for the hyperparameters.

In addition, on each dataset, we further tuned the hyperparameters. As shown in Table 8.

Table 8 Optimal configuration of the MHRE on each dataset
Full size table

Appendix B: Infrastructure and runtime

We train our MHRE model on one Tesla V100 32G GPU using the hyperparameters selected in Tables 7 and 8. On JF17K, our model takes about 6 hours to train and evaluate. On WikiPeople, it takes about 1.5 days. On \(\text {WikiPeople}^-\), it takes about 22 hours. For JF17K-3, JF17K-4, WikiPeople-3, and WikiPeople-4, the runtime is around 1.5 hours. On JF17K-4+ and WikiPeople-4+, it takes roughly 3 hours. In contrast, running the MHRE model on MedCKG requires approximately 2 days. Because of the effect of parameters and graph convolution, MHRE is weaker than previous methods like GRAN and HINGE in terms of time efficiency, but similar to STARE.

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Wang, W., Zhang, X., Zhang, J. et al. MHRE: Multivariate link prediction method for medical hyper-relational facts. Appl Intell 54, 1311–1334 (2024). https://doi.org/10.1007/s10489-023-05248-2

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