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| Discovering Subject Knowledge in Life and Medical Sciences with Knowledge Graph |
Hu Zhengyin1,2( ),Liu Leilei1,2,Dai Bing1,2,Qin Xiaochu3,4 |
1Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041, China
2Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
3Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou 510700, China
4Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China |
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Abstract [Objective] This paper explores new methods for deep subject knowledge discovery using multi-source heterogeneous data. [Methods] First, we constructed a SPO semantic network of literature to create the core domain knowledge graph. Then, we implemented multi-source heterogeneous data fusion through “entity alignment, concept level fusion and relationship fusion” to obtain the whole domain knowledge graph. Finally, we discovered deep subject knowledge with the help of this knowledge graph. We examined our method with data on Hematopoietic Stem Cell for Cancer Treatment (HSCCT). [Results] This paper proposed a knowledge graph-based framework for subject knowledge discovery (KGSKD), which fuses multi-source heterogeneous data multi-dimensionally and fine-grainedly, enriches semantic relationships among data, and supports knowledge discovery techniques such as knowledge inference, pathfinder, and link prediction natively. [Limitations] KGSKD has some limitations including data supersaturation, poor interpretability of knowledge discovery results and difficulty in communicating with domain experts. [Conclusions] KGSKD has the advantages of “richer data types”, “more comprehensive knowledge linkage”, “more advanced mining methods” and “deeper discovery results”, which effectively supports research and services of deep knowledge discovery in life sciences and medicine.
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Received: 13 July 2020
Published: 04 December 2020
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Corresponding Authors:
Hu Zhengyin
E-mail: huzy@clas.ac.cn
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