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Frontier knowledge discovery and visualization in cancer field based on KOS and LDA

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Abstract

Scientific research journals have achieved the latest development in scientific research in various fields. However, the interpretation and use of biomedical information is still a very complicated issue. How to use practical methods to interpret biomedical literature into structured data and analyze it into what we can understand has become a major issue. In this paper, a frontier knowledge discovery model based on KOS and LDA is proposed and applied in detecting burst topic and its sematic information relationship in cancer field. Experiments showed that the model plays an important role in topic recognition, evolution recognition and visualization. Furthermore, the application of KOS combined with LDA can effectively remove noisy concept in sematic layer and show a good effect.

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Acknowledgements

The project is supported by the National Natural Science Foundation of China (Grant No. 61502402), the Fundamental Research Funds for the Central Universities (Grant No. 20720180073), the state key laboratory of virtual reality technology and systems of China (Grant No. BUAA-VR-15 KF-09) and the Xiamen University (Grant No. 20720150081).

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  1. Software School, Xiamen University, Xiamen, 361005, China

    Qingqiang Wu, Yichen Kuang, Qingqi Hong & Yingying She

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  1. Qingqiang Wu
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  2. Yichen Kuang
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Correspondence to Yingying She.

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Wu, Q., Kuang, Y., Hong, Q. et al. Frontier knowledge discovery and visualization in cancer field based on KOS and LDA. Scientometrics 118, 979–1010 (2019). https://doi.org/10.1007/s11192-018-2989-y

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