Knowledge push based on knowledge usage scenario

Abstract

Abstract: To solve the problem of knowledge pushing, the models of knowledge pushing based on knowledge usage scenario and knowledge base drove by usage scenario are proposed. Semantic similarity is used to calculate the similarity between knowledge usage scenario and knowledge index. According to the relationship between knowledge and knowledge usage scenario, knowledge is recognized and pushed to the task. A case study of process knowledge push in a valve production enterprise is provided to demonstrate the feasibility of this method.

Key words: knowledge usage scenario, knowledge pushing, semantic similarity

摘要： 针对知识推送问题，建立了基于场景的知识推送模型、场景驱动的知识库模型。采用语义相似度的方法计算知识使用场景的相似度，通过知识使用场景与知识的映射，实现与任务场景最匹配知识的推送。以某气门生产企业的工艺知识推送为例验证了该知识推送方法的可行性。

关键词: 知识使用场境, 知识推送, 语义相似度

ZHAO Xiaoling, GUO Gang, DONG Yuanfa. Knowledge push based on knowledge usage scenario[J]. Computer Engineering and Applications, 2013, 49(22): 132-135.

赵晓玲，郭钢，董元发. 基于知识使用场景的知识推送方法研究[J]. 计算机工程与应用, 2013, 49(22): 132-135.

[1]	SHI Chen, ZHANG Yu, HU Bo. Model for Near-Synonym/Synonym Phrase Finding Based on Common Surrounding Context [J]. Computer Engineering and Applications, 2021, 57(14): 142-147.
[2]	QIAO Weitao, HUANG Haiyan, WANG Shan. Semantic Similarity Calculation Based on Transformer Encoder [J]. Computer Engineering and Applications, 2021, 57(14): 158-163.
[3]	YUAN Zhongchen, MA Zongmin. Ensemble Classification for UML Class Diagram Based on Semantics [J]. Computer Engineering and Applications, 2021, 57(12): 257-262.
[4]	XU Ge, YANG Xiaoyan, WANG Tao. Survey on Semantic Similarity Calculation of Words [J]. Computer Engineering and Applications, 2020, 56(4): 9-15.
[5]	YAO Jiaqi, XU Zhengguo, YAN Jikun, XIONG Gang, LI Zhixiang. Dynamic Multi-label Text Classification Algorithm Based on Label Semantic Similarity [J]. Computer Engineering and Applications, 2020, 56(19): 94-98.
[6]	YANG Quan, SUN Yuquan. Research on Semantic Similarity Calculation Based on Depth of CiLin [J]. Computer Engineering and Applications, 2020, 56(17): 48-54.
[7]	HAN Xueren1, WANG Qingshan1, GUO Yong1, CUI Xingya2. Geographic ontology concept semantic similarity measure model based on BP neural network optimized by PSO [J]. Computer Engineering and Applications, 2017, 53(8): 32-37.
[8]	CHEN Hongyang, WANG Linlin, LU Jiangkun, TANG Zhi, WANG Feixue. Research on method of topic tracking for micro-blog texts based on double topic model [J]. Computer Engineering and Applications, 2017, 53(16): 144-148.
[9]	QIU Yunfei, ZHAO Bin, LIN Mingming, WANG Wei. Improved K-means clustering algorithm combined semantic similarity of short text [J]. Computer Engineering and Applications, 2016, 52(19): 78-83.
[10]	OUYANG Liubo, TAN Ruizhe. Query expansion method based on ontology and user log [J]. Computer Engineering and Applications, 2015, 51(1): 151-155.
[11]	DONG Lili1, LI Huan1, ZHANG Xiang1, LIU Yanfeng2. Method for automatic extraction of Chinese domain concepts [J]. Computer Engineering and Applications, 2014, 50(6): 127-131.
[12]	ZHAN Zhijian, YANG Xiaoping. Text similarity calculation based on language network and semantic information [J]. Computer Engineering and Applications, 2014, 50(5): 33-38.
[13]	YU Hongzhi, XIA Jianhua, WAN Fucheng, CHEN Xinyi. Automatic scoring algorithm for Tibetan subjective questions based on multi-features combination [J]. Computer Engineering and Applications, 2014, 50(5): 216-220.
[14]	WANG Xuyang, WAN Li. Research on semantic similarity in information retrieval [J]. Computer Engineering and Applications, 2014, 50(10): 124-127.
[15]	ZHANG Yufang, ZHANG Hongbo, XIONG Zhongyang. Semantic similarity calculation in application of semantic annotation [J]. Computer Engineering and Applications, 2013, 49(4): 153-156.

Knowledge push based on knowledge usage scenario

基于知识使用场景的知识推送方法研究

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics