[1]朱 睿,冯锡炜,窦予梓,等.改进贝叶斯的语义推送算法设计[J].计算机技术与发展,2020,30(03):104-110.[doi:10. 3969 / j. issn. 1673-629X. 2020. 03. 020]
　ZHU Rui,FENG Xi-wei,DOU Yu-zi,et al.Design of Semantic Push Algorithm Based on Bayesian[J].Computer Technology and Development,2020,30(03):104-110.[doi:10. 3969 / j. issn. 1673-629X. 2020. 03. 020]
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改进贝叶斯的语义推送算法设计()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年03期
页码:
104-110
栏目:
智能、算法、系统工程
出版日期:
2020-03-10

文章信息/Info

Title:
Design of Semantic Push Algorithm Based on Bayesian
文章编号:
1673-629X(2020)03-0104-07
作者:
朱 睿; 冯锡炜; 窦予梓; 高天铸; 马 蕾; 吴衍兵
辽宁石油化工大学 计算机与通信工程学院,辽宁 抚顺 113001
Author(s):
ZHU Rui; FENG Xi-wei; DOU Yu-zi; GAO Tian-zhu; MA Lei; WU Yan-bing
School of Computer and Communication Engineering,Liaoning Shihua University,Fushun 113001,China
关键词:
语义本体; 信息推送; 词汇频度分析模型; 教育信息化
Keywords:
semantic ontology; information push; lexical frequency analysis model; educational informatization
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 03. 020
摘要:
教育信息语义本体构建是通过语义本体构建方式去设计教育信息本体库。 本体间逻辑关系表示方法,是构建出 有逻辑结构的教育信息集合的过程。 实现教育信息的半结构化数据归类,对不同时间采集的归类数据在规定好的模型中 进行计算—词汇频度分析模型。 词汇频度分析模型运用逆概率的贝叶斯思想,经过对传统贝叶斯算法与语义本体性质相 结合,使MapReduce善于处理半结构化数据;经过对语义本体构建的教育信息数据结合词汇频度分析模型进行计算,获得 教育信息本体的推荐能力值 Ei;通过对不同本体 Ei 值进行排序,获得了推荐信息的顺序;根据推荐权重进行信息的推送 工作,同时根据JS指数,经过比较基于词汇频度分析模型与目录结构推送算法的分析结果得出:词汇频度分析模型优于基 于目录结构推送算法。
Abstract:
The construction of educational information semantic ontology is to design educational information ontology database through semantic ontology construction. The representation method of logical relation between ontologies is the process of constructing the set of educational information with logical structure. The semi-structured data classification of educational information is realized,and the classification data collected at different time? are calculated in a well-defined model—lexical frequency analysis model. The Bayesian idea of inverse probability is introduced in the lexical frequency analysis model. The combining of the traditional Bayesian algorithm with the semantic ontology property makes Map Reducedeal with semi-structured datawell. After calculating the educational information databased on semantic ontology and vocabulary frequency analysis model,? the recommendation ability value (Ei) of educational information ontology is obtained. By sorting different ontology Ei values,the order of recommen-dation information is obtained. The information is pushed by the recommendation weight. According to JS index,by comparing the analysis results based on lexical frequency analysis model and directory structure push algorithm,it is concluded that the lexical frequency analysis model is superior to the push algorithm based on directory structure.
更新日期/Last Update: 2020-03-10