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| 引用本文： | Ф?,?????,?1?.???????μ?????????????????????[J].图书情报工作,1957,64(6):100-107. |
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| 作者姓名： | Ф? ????? ?¹? |
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| 作者单位： | 1. ??????????????? ??? 210023;
2. ???????????ù????? ??? 210094 |
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| 摘 要： |
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| 关 键 词： | ????????? ?????? ???????? ???????? |
Holistic Perspective Multi-knowledge Relations Mining in Network Community |
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| Xiao Lu,Zhao Zhihui,Chen Guo.Holistic Perspective Multi-knowledge Relations Mining in Network Community[J].Library and Information Service,1957,64(6):100-107. |
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| Authors: | Xiao Lu Zhao Zhihui Chen Guo |
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| Institution: | 1. School of Journalism, Nanjing University of Finance & Economics, Nanjing 210023;
2. School of Economics & Management, Nanjing University of Science and Technology, Nanjing 210094 |
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| Abstract: | ??Purpose/significance] There are many knowledge units in the network community, among which there are intricate relationships. It is necessary to carry out multiple knowledge relations mining uniformly and succinctly on the premise of retaining all the relations of knowledge units.Method/process] This paper puts forward the solution of multi-knowledge relations mining in network community. Firstly, 3 typical knowledge units (users, texts and words) in the network community and their multiple relations in the knowledge communication were extracted into a supernetwork. Secondly, the network representation learning algorithm was used to uniformly represent the nodes in the supernetwork as low-dimensional dense vectors. Finally, multiple knowledge relations calculation was carried out based on nodal vector.Result/conclusion] The effectiveness of the scheme was verified by taking cardiovascular BBS in dingxiang garden as an example. This scheme not only retains all the information of the knowledge unit, but also carries out the mining of the knowledge relation under the unified low-dimensional characteristics, and finally the knowledge relation meets the requirements of the diversity of the knowledge organization scene in the network community. |
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| Keywords: | knowledge relation mining super network network representation learning network community |
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