Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous Medical Knowledge and Services. Shi, L., Li, S., Yang, X., Qi, J., Pan, G., & Zhou, B. BioMed Research International, 2017:e2858423, February, 2017. ZSCC: 0000044 Publisher: Hindawi![Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous Medical Knowledge and Services [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex With the explosion of healthcare information, there has been a tremendous amount of heterogeneous textual medical knowledge (TMK), which plays an essential role in healthcare information systems. Existing works for integrating and utilizing the TMK mainly focus on straightforward connections establishment and pay less attention to make computers interpret and retrieve knowledge correctly and quickly. In this paper, we explore a novel model to organize and integrate the TMK into conceptual graphs. We then employ a framework to automatically retrieve knowledge in knowledge graphs with a high precision. In order to perform reasonable inference on knowledge graphs, we propose a contextual inference pruning algorithm to achieve efficient chain inference. Our algorithm achieves a better inference result with precision and recall of 92% and 96%, respectively, which can avoid most of the meaningless inferences. In addition, we implement two prototypes and provide services, and the results show our approach is practical and effective.
@article{shi_semantic_2017,
title = {Semantic {Health} {Knowledge} {Graph}: {Semantic} {Integration} of {Heterogeneous} {Medical} {Knowledge} and {Services}},
volume = {2017},
issn = {2314-6133},
shorttitle = {Semantic {Health} {Knowledge} {Graph}},
url = {https://www.hindawi.com/journals/bmri/2017/2858423/},
abstract = {With the explosion of healthcare information, there has been a tremendous amount of heterogeneous textual medical knowledge (TMK), which plays an essential role in healthcare information systems. Existing works for integrating and utilizing the TMK mainly focus on straightforward connections establishment and pay less attention to make computers interpret and retrieve knowledge correctly and quickly. In this paper, we explore a novel model to organize and integrate the TMK into conceptual graphs. We then employ a framework to automatically retrieve knowledge in knowledge graphs with a high precision. In order to perform reasonable inference on knowledge graphs, we propose a contextual inference pruning algorithm to achieve efficient chain inference. Our algorithm achieves a better inference result with precision and recall of 92\% and 96\%, respectively, which can avoid most of the meaningless inferences. In addition, we implement two prototypes and provide services, and the results show our approach is practical and effective.},
language = {en},
urldate = {2020-08-25},
journal = {BioMed Research International},
author = {Shi, Longxiang and Li, Shijian and Yang, Xiaoran and Qi, Jiaheng and Pan, Gang and Zhou, Binbin},
month = feb,
year = {2017},
doi = {https://doi.org/10.1155/2017/2858423},
doi = {https://doi.org/10.1155/2017/2858423},
note = {ZSCC: 0000044
Publisher: Hindawi},
pages = {e2858423},
}
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