云制造服务中供需智能匹配引擎的研究

发布时间：2018-01-31 20:08

本文关键词： 云制造 Web语义本体智能搜索　出处：《湖北工业大学》2013年硕士论文　论文类型：学位论文

【摘要】：面对海量信息，智能信息检索一直是科研人员的重要课题。但是网络上传统的信息表示方法使信息检索面临各种难以逾越的障碍。因此改进信息检索的重要方法之一就是整理和重新规范Web上的信息。整理大量的HTML页面内容的实质就是如何从HTML页面中提取语义信息，构建能描述这些页面的本体。实现本体的自动或半自动提取，不仅对文本信息可以采用语义Web的方法来加强智能检索，而且还可以对多媒体信息，结合模式识别和对象提取技术，实现基于内容的检索。本文介绍了云制造服务平台搜索引擎技术的国内外研究现状，云制造服务平台的关键技术。对供需智能匹配的关键字搜索算法和语义搜索算法进行了分析和比较。论述了语义本体在供需匹配中的应用以及云制造服务中供需智能匹配引擎的实现。传统的信息检索方法是将用户输入的检索关键字按照字面匹配的方法在云制造服务资源库中检索目标结果，检索系统仅仅将关键词作为符号，无法理解其语义含义。基于领域本体的智能匹配引擎的设计核心是引入领域本体层作为匹配和推理的关键部件，，与传统的检索方法相比，增加了本体检索推理层。本课题研究的供需智能匹配引擎来源于知识、语义的匹配检索方式,这种检索方式主要是利用规范后的检索按领域和标注后的信息源索引库来进行语义的匹配和搜索，再提交给检索系统的一段过程。本体技术被引入云制造服务资源，按不同应用领域对语义进行检索，多义词之间的相互联系和语义关联也能被很好的解决，使信息检索中的词义干扰大大减少，节约了耗时，缩小了检索的范围，能有效解决信息分类错乱等问题，提高了用户的满意度。通过对云制造服务平台智能搜索引擎的设计与开发，可以看出尽管语义Web在元数据描述和本体领域方面的研究已经基本成熟，要充分发挥Web的潜能，完全实现语义Web的构想，还面临许多问题和挑战。
[Abstract]:In the face of massive information. Intelligent information retrieval has always been an important subject for researchers, but traditional information representation methods on the network make information retrieval face various insurmountable obstacles. Therefore, one of the important methods to improve information retrieval is to organize and improve information retrieval. The essence of sorting out a large amount of HTML page content is how to extract semantic information from a HTML page. To construct ontology that can describe these pages and realize automatic or semi-automatic extraction of ontology, not only the semantic Web method can be used to enhance the intelligent retrieval of text information, but also the multimedia information can be obtained. Combined with pattern recognition and object extraction technology to achieve content-based retrieval. This paper introduces the research status of cloud manufacturing service platform search engine technology at home and abroad. The key technologies of cloud manufacturing service platform are analyzed and compared. The keyword search algorithm and semantic search algorithm of intelligent matching between supply and demand are analyzed and compared. The application of semantic ontology in supply and demand matching and the supply and demand of cloud manufacturing service are discussed. The realization of intelligent matching engine. The traditional information retrieval method is to retrieve the target results in the cloud manufacturing service resource database according to the literal matching method of the search keywords entered by the user. The search system can not understand the semantic meaning of keywords only by using keywords as symbols. The design core of intelligent matching engine based on domain ontology is to introduce domain ontology layer as the key component of matching and reasoning. Compared with traditional retrieval methods, ontology retrieval reasoning layer is added. The intelligent matching engine of supply and demand in this subject comes from knowledge and semantic matching retrieval methods. This kind of retrieval method mainly uses the standard retrieval according to the domain and the annotated information source index library to carry on the semantic matching and the search. Ontology technology is introduced into cloud manufacturing service resources to retrieve semantics according to different application fields. The interrelation and semantic association between polysemous words can also be solved very well. It greatly reduces the interference of word meaning in information retrieval, saves time, reduces the scope of retrieval, effectively solves the problem of information classification disorder, and improves the satisfaction of users. Through the design and development of the intelligent search engine for cloud manufacturing service platform, we can see that although the semantic Web in the field of metadata description and ontology research has been basically mature, we should give full play to the potential of Web. There are many problems and challenges in realizing the concept of semantic Web.
【学位授予单位】：湖北工业大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP391.1;TP393.09

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