Research of personalized knowledge search for food safety system
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摘要: 大数据时代，从海量的数据中发现对用户有用的知识成为研究领域重要的问题.通过集成多个搜索引擎的查询结果，实现食品安全领域中搜索信息的集成和个性化自适应排序.本文设计基于元搜索技术、知识本体和自适应的排序学习技术，实现多个搜索引擎相关查询结果集成，在对用户点击的标注和知识本体的基础上，利用基于监督学习的排序技术，实现对食品安全领域信息的个性化自适应排序.系统实现了集成多个搜索引擎的食品安全相关知识的提取和相关结果的重新排序.本研究不仅实现了多个搜索引擎食品安全信息查询相关的结果集成，而且能够根据用户的偏好实现结果的自适应排序.Abstract: In the era of big data, knowledge discovery from the mass of data is an important research problem, especially for the user's customized knowledge. In this paper, an integrated search system aiming at personalized re-ranking of food safety knowledge system, PROSK for short, is designed and implemented. Firstly, using the existing search engines, the meta-search engine technique is employed for integrating the results of multiple search engines; then according to the results of the users' click through and the ontology of food safety domain, ranking-based learning algorithm is applied to sort search results adaptively according to the preference profiles. The system integrates the agricultural information from multi-engineers and ranks the query results adaptively and intelligently. This study proposes a feasible solution for ranking of information and knowledge of food safety from multi-engineers adaptively.
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Key words:
- food safety search /
- personalized ranking /
- search engine integration /
- domain ontology
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表 1 PROSK查询性能
Tab. 1 Query performance of PROSK
查询输入 在线查询时间/ms 离线计算时间/ms 训练时间 集成及解析建模时间 Q1:安全溯源 9 51 7 506 Q2:智慧农业 5 36 13 457 Q3:农业物联网 8 30 8 867 Q4:农药残留 9 45 8 906 Q5:转基因食品标识 7 38 7 270 
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[1] 中国互联网络发展状况统计报告[R]. 中国互联网络信息中心, 2016. [2] 彭玉容, 杨捧, 高媛.农业搜索引擎的发展现状及关键技术研究[J].安徽农业科学, 2010, 38(20):10971-10973. doi: 10.3969/j.issn.0517-6611.2010.20.181 [3] 王超, 李书琴, 肖红.基于本体的旱区农业垂直搜索引擎研究[J].农机化研究, 2013, 35(8):184-187. http://cdmd.cnki.com.cn/Article/CDMD-10712-1013347479.htm [4] 李雷. 基于Nutch的农业信息搜索引擎实现和优化[D]. 长春: 吉林大学, 2011. http://cdmd.cnki.com.cn/Article/CDMD-10183-1011098986.htm [5] SDD农搜. [EB/OL]. [2016-05-01]. http://www.sdd.net.cn/. [6] 乔波, 聂笑一, 方逵.基于本体的农业信息服务个性化推送研究[J].安徽农业科学, 2013, 41(27):11213-11214. doi: 10.3969/j.issn.0517-6611.2013.27.121 [7] 李宏伟, 林萍, 洪小娟.食品安全网络舆情本体学习研究[J].南京邮电大学学报(社会科学版), 2013, 15(4):72-77. http://www.cnki.com.cn/Article/CJFDTOTAL-LYXB201304014.htm [8] 杜小勇, 李曼, 王珊.本体学习研究综述[J].软件学报, 2006, 17(9):1837-1847. http://www.cnki.com.cn/Article/CJFDTOTAL-FJDN200806021.htm [9] GRUBER T R. A translation approach to portable ontology specifications[J]. Knowledge Acquisition. 1993, 5(2):199-220. doi: 10.1006/knac.1993.1008 [10] 杨月华, 杜军平, 平源.基于本体的智能信息检索系统[J].软件学报, 2015, 26(7):1675-1687. http://www.cnki.com.cn/Article/CJFDTOTAL-RJXB201507010.htm [11] NOY N F. Semantic integration:A survey of ontology-based approaches[J]. ACM Sigmod Record, 2004, 33(4):65-70. doi: 10.1145/1041410 [12] 吴小兰, 汪琪.元搜索引擎研究综述[J].图书情报工作, 2009, 53(9):46-49. http://www.cnki.com.cn/Article/CJFDTOTAL-TSQB200909013.htm [13] 曹林, 韩立新, 吴胜利.元搜索引擎排序技术综述[J].计算机应用研究, 2009, 26(2):411-414. http://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ200902004.htm [14] 阳小华, 刘振宇, 谭敏生, 等.元搜索引擎系统合成算法的约束条件[J].软件学报, 2002, 13(7):1264-1270. http://www.cnki.com.cn/Article/CJFDTOTAL-RJXB200207011.htm [15] 胡宜敏. 农业搜索个性化平台的研究与设计[D]. 合肥: 中国科学技术大学, 2009. http://cdmd.cnki.com.cn/Article/CDMD-10358-2010019607.htm [16] 曾春, 邢春晓, 周立柱.个性化服务技术综述[J].软件学报, 2002, 13(10):1952-1961. http://www.cnki.com.cn/Article/CJFDTOTAL-RJXB200210006.htm [17] 花贵春, 张敏, 邝达, 等.面向排序学习的特征分析的研究[J].计算机工程与应用, 2011, 47(17):122-127. doi: 10.3778/j.issn.1002-8331.2011.17.033 [18] LI H. A short introduction to learning to rank[J]. Ieice Transactions on Information & Systems, 2011, 94(10):1854-1862. https://www.jstage.jst.go.jp/article/transinf/E94.D/10/E94.D_10_1854/_pdf [19] LI P, BURGES C J C, WU Q. McRank:Learning to rank using multiple classification and gradient boosting[C]//International Conference on Neural Information Processing Systems. Curran Associates Inc, 2007:897-904. [20] CAO Z, QIN T, LIU T Y, et al. Learning to rank:From pairwise approach to listwise approach[C]//Proceedings of the 24th International Conference on Machine Learning. ACM, 2007:129-136. [21] BURGES C, SHAKED T, RENSHAW E, et al. Learning to rank using gradient descent[C]//Proceedings of the 22nd International Conference on Machine Learning. ACM, 2005:89-96. [22] FREUND Y, IYER R, SCHAPIRE R E, et al. An efficient boosting algorithm for combining preferences[J]. Journal of Machine Learning Research, 2003, 4:933-969. [23] JOACHIMS T. Optimizing search engines using clickthrough data[C]//Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2002:133-142. [24] HERBRICH R, GRAEPEL T, OBERMAYER K. Large margin rank boundaries for ordinal regression[J]. Advances in Neural Information Processing Systems, 2000, 10(3):115-132. [25] CALVANESE D, DE GIACOMO G, LENZERINI M. A framework for ontology integration[C]//Proceedings of the First International Conference on Semantic Web Working. 2001:303-316. [26] ICTCLAS.[EB/OL].[2017-02-01]. http://ictclas.nlpir.org/. [27] SVMrank.[EB/OL].[2016-09-01]. https://www.cs.cornell.edu/people/tj/svmlight/svmrank.html. -
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