智慧企业中的智慧搜索

doi:10.11959/j.issn.1000-436x.2015317

摘要/Abstract

摘要：

现代企业除了面临复杂的生产环境和网络环境外，还需积极应对和处理随之产生的海量数据。这些数据服务于企业发展是智慧企业的目的之一。基于企业各个环节可能产生的各种数据类型和对应的搜索技术，智慧搜索旨在为智慧企业的实现与发展增加一种新的智慧服务。通过实例来阐述智慧搜索的内涵和外延，以及智慧搜索能为企业带来的不同级别的服务和相应的挑战。

关键词: 智慧企业, 智慧搜索, 大数据, 文本搜索

Abstract:

Modern enterprises are facing not only complex production and networked environments,but also massive amount of data generated from the various processes.One of the goals of smart enterprise (SE) is to make full use of the big data for further development of enterprises.By examining the types and applicable search techniques of the data which may come from different process of the enterprise.The notion of smart search was introduced,as a new mechanism to facilitate the implementation and development of SE.Smart search is elaborated through real-life examples,and discuss how smart search can bring different levels of services as well as the challenging issues to be tackled.

Key words: smart enterprise, smart search, big data, text retrieval

陈扬斌,李青,庄越挺. 智慧企业中的智慧搜索[J]. 通信学报, 2015, 36(12): 89-96.

Yang-bin CHEN,Qing LI,Yue-ting ZHUANG. Smart search in smart enterprise[J]. Journal on Communications, 2015, 36(12): 89-96.

图/表 4

表1

表2

3种平滑方法的数学表示及其参数"

平滑方法	p_s (q_i\|d)	α_d	参数
线性插值平滑	(1?λ)p_ml (w\|d)+λp(w\|C)	λ	λ
狄利克雷平滑	$\frac{c (w; d) + μ p (w \| C)}{Σ_{w} c (w; d) + μ}$	$\frac{μ}{\| d \| + μ}$	μ
绝对折扣平滑	$\frac{\max (c (w; d) + δ, 0)}{Σ_{w} c (w; d) + μ} + \frac{δ {\| d \|}_{μ}}{\| d \|} p (w \| C)$	$\frac{δ {\| d \|}_{μ}}{\| d \|}$	δ

表2

图1

图2

参考文献 24

[1]	MANNING C D , RAGHAVAN P , SCHüTZE H . Introduction to Information Retrieval[M]. Cambridge: Cambridge University PressPress, 2008.
[2]	GUO L , SHAO F , BOTEV C ,et al. XRANK:ranked keyword search over XML documents[A]. ACM SIGMOD’03[C]. San Diego,CA, 2003. 9-12.
[3]	XU Y , PAPAKONSTANTINOU Y . Efficient keyword search for smallest LCAs in XML databases[A]. Proceedings of ACM SIGMOD’05[C]. Baltimore,Maryland,USA, 2005. 14-16.
[4]	LI G , FENG J , WANG J ,et al. Effective keyword search for valuable lcas over xml documents[A]. Proceedings of ACM CIKM’07[C]. Lisboa,Portugal, 2007. 6-8.
[5]	SALTON G , WONG A , YANG C S . A vector space model for automatic indexing[J]. Communications of the ACM, 1975,18(11): 613-620.
[6]	TURNEY P D , PANTEL P . From frequency to meaning:vector space models of semantics[J]. Journal of Artificial Intelligence Research, 2010,37(1): 141-188.
[7]	FUHR N . Probabilistic models in information retrieval[J]. The Computer Journal, 1992,35(3): 243-255.
[8]	SONTAG D , COLLINS-THOMPSON K , BENNETT P N ，et al. Probabilistic models for personalizing web search[A]. Proceedings of the fifth ACM WSDM conference[C]. Seattle,Washington, 2012. 8-12.
[9]	PONTE J M , CROFT W B . A language modeling approach to information retrieval[A]. Proceedings of ACM SIGIR’98[C]. Melbourne,Australia, 1998. 275-281.
[10]	COLLOBERT R , WESTON J , BOTTOU L ,et al. Natural language processing (almost) from scratch[J]. The Journal of Machine Learning Research, 2011,12(1): 2493-2537.
[11]	WU H C , LUK R W P , WONG K F ,et al. Interpreting TF-IDF term weights as making relevance decisions[J]. ACM Transactions on Information Systems (TOIS), 2008,26(3): 55-59.
[12]	SINGHAL A , BUCKLEY C , MITRA M . Pivoted document length normalization[A]. Proceedings of ACM SIGIR’96[C]. Zurich,Switzerland, 1996. 21-29.
[13]	ROBERTSON S , ZARAGOZA H . The probabilistic relevance framework:BM25 and beyond[J]. Foundations and Trends in Information Retrieval, 2009,3(4): 333-389.
[14]	DING G , BAI S , WANG B . A survey of statistical language modeling for text retrieval[J]. Journal of Computer Research and Development, 2015,43(5): 769-776.
[15]	ZHAI C , LAFFERTY J . A study of smoothing methods for language models applied to information retrieval[J]. ACM Transactions on Information Systems (TOIS), 2004,22(2): 179-214.
[16]	FANG H , TAO T , ZHAI C . Diagnostic evaluation of information retrieval models[J]. ACM Transactions on Information Systems (TOIS), 2011,29(2): 217-230.
[17]	BLEI D M . Probabilistic topic models[J]. Communications of the ACM, 2012,55(4): 77-84.
[18]	MIKOLOV T , CHEN K , CORRADO G ,et al. Efficient estimation of word representations in vector space[J]. rXiv Preprint arXiv:1301.3781, 2013.
[19]	YIH W , CHANG M W , HE X ,et al. Semantic parsing via staged query graph generation:question answering with knowledge base[A]. Proceedings of 53rd Annual Meeting of ACL and the 7th IJCNLP[C]. Beijing,China, 2015.
[20]	YANG Z , NYBERG E . Leveraging procedural knowledge for task-oriented search[A]. Proceedings of ACM SIGIR’15[C]. Santiago,Chile, 2015.
[21]	LIU B . Sentiment analysis and subjectivity[J]. Handbook of Natural Language Processing, 2010,30(36): 152-153.
[22]	ANDREJ K , LI F . Deep visual-semantic alignments for generating image descriptions[J]. arXiv Preprint arXiv:1412.2306, 2014.
[23]	YANG Z , LI Q , LU Z ,et al. Semi-supervised multimodal fusion model for social event detection on Web image collections[J]. International Journal of Multimedia Data Engineering and Management (IJMDEM), 2015,6(4): 1-22.
[24]	ACKOFF R L . From data to wisdom[J]. Journal of applied systems analysis, 2010,16: 3-9.

数据类型	企业管理	生产过程	公司运营	技术研发
结构化数据	财务报表	原料使用记录，监测数据记录	产品销售记录，用户信息记录	数据库文件
半结构化数据	人事档案	生产日志	网站数据（XML,HTML）	需求分析文档，软件设计报告
非结构化数据	文本及多媒体文件	质检报告，产品说明书	用户评论，社交媒体，网络媒体	用户使用指南，专利说明书