Conclusion
In this work, we perform a joint learning model to construct a fine-grained biomedical entity and relation linking. We adopt a probabilistic a probabilistic logic network to align the entities in biomedical text to the concepts in the knowledge base. And we utilize the dispersion degree of standard deviation to evaluate feature distribution and select those tuples with distinguishing features for interaction relationship classifying. Then we rank the candidate entity-relation triples to obtain the linking result.
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Acknowledgements
This research was supported by the National Natural Science Foundation of China (62072084, 62072086, 62172082), the National Defense Basic Scientific Research Program of China (JCKY2018205C012), and the Fundamental Research Funds for the Central Universities (N2116008).
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Hu, Y., Shen, D., Nie, T. et al. Biomedical entity linking based on less labeled data. Front. Comput. Sci. 16, 163343 (2022). https://doi.org/10.1007/s11704-022-1192-8
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DOI: https://doi.org/10.1007/s11704-022-1192-8