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Biomedical event causal relation extraction based on a knowledge-guided hierarchical graph network

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Abstract

Biomedical Event Causal Relation Extraction (BECRE) is a challenging task in biological information extraction and plays a crucial role to serve for knowledge base and knowledge graph construction. Here a biomedical cause-effect relation is defined as an association between two events and requires that the cause-event must occur before the effect-event. Some current advances tend to apply deep learning for the BECRE task and have achieved comparable performances. However, because most of event causal relations are implicitly stated, the performances of these works based on contextual semantics and syntactics might be limited. This fact suggests that it is necessary to introduce external cues to improve the performance of the implicit BECRE especially in the low source scenario. To improve the potential of the designed model, an intuitive idea is to introduce hierarchical knowledge from biological knowledge bases to supplement domain cues for the contexts. Nevertheless, it is difficult to learn the hierarchy and cause-effect direction of knowledge in the model and also few works focus on this issue. Thus, to better fuse knowledge, we propose a Graph Edge-Cluster Attention Network (GECANet) for the BECRE task. Specifically, we introduce external knowledge and build hierarchical knowledge graphs for the contexts. Also, the proposed GECANet effectively aggregates the context and hierarchical knowledge semantics under the guidance of cause-effect direction. The experimental results confirm that fusing external knowledge can effectively guide the model to identify event causal relations and facilitate our approach to achieve state-of-the-art performances respectively on the Hpowell and BioCause datasets.

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Data Availability

The datasets generated during and/or analysed during the current study are available in the https://github.com/Zhangbeibei1991/KG-BCRE/tree/master/datasets repository and the program code has been also made available at: https://github.com/Zhangbeibei1991/KG-BCRE.

Notes

  1. Rel\( _\mathrm{A \rightarrow B} \) denotes the relation from nodes A to node B.

  2. Before the start of experiments, we conducted the operation of the knowledge discovery in advance for all texts in the evaluated datasets and construct a large number of triplets to pre-train knowledge representations.

  3. The reason using GRU in Eq. (5) is that GRU has a relatively simple structure compared with other variants of RNN.

  4. https://huggingface.co/dmis-lab/biobert-base-cased-v1.1

  5. https://huggingface.co/dmis-lab/biobert-v1.1.

  6. https://huggingface.co/monologg/biobert_v1.0_pubmed_pmc.

  7. https://huggingface.co/monologg/biobert_v1.1_pubmed.

  8. The bold in tables indicate the best scores of each column.

  9. The Bold and Italic text span and Bold span in sentence respectively denote E1 and E2 events.

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Funding

This work is supported by grant from the National Natural Science Foundation of China (No. 62076048), the Science and Technology Innovation Foundation of Dalian (2020JJ26GX035).

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Authors and Affiliations

  1. School of Computer Science and Technology, Dalian University of Technology, No.2 Linggong Road, Dalian City, 116024, Liaoning Province, China

    Beibei Zhang, Lishuang Li & Yang Zhao

  2. Finance and Economics College, Hebei Normal University of Science and Technology, No.360 West Section of Hebei Street, Qinhuangdao City, 066004, Hebei Province, China

    Dingxin Song

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  1. Beibei Zhang
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  2. Lishuang Li
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  3. Dingxin Song
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  4. Yang Zhao
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BZ and LL conceived the experiment(s), BZ and YZ conducted the experiment(s), YZ and DS analysed the results. BZ and LL wrote and reviewed the manuscript.

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Correspondence to Lishuang Li.

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Zhang, B., Li, L., Song, D. et al. Biomedical event causal relation extraction based on a knowledge-guided hierarchical graph network. Soft Comput 27, 17369–17386 (2023). https://doi.org/10.1007/s00500-023-08882-7

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