本文關(guān)鍵詞： 云制造 Web語義 本體 智能搜索　出處：《湖北工業(yè)大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：面對海量信息，智能信息檢索一直是科研人員的重要課題。但是網(wǎng)絡(luò)上傳統(tǒng)的信息表示方法使信息檢索面臨各種難以逾越的障礙。因此改進(jìn)信息檢索的重要方法之一就是整理和重新規(guī)范Web上的信息。整理大量的HTML頁面內(nèi)容的實質(zhì)就是如何從HTML頁面中提取語義信息，構(gòu)建能描述這些頁面的本體。實現(xiàn)本體的自動或半自動提取，不僅對文本信息可以采用語義Web的方法來加強(qiáng)智能檢索，而且還可以對多媒體信息，結(jié)合模式識別和對象提取技術(shù)，實現(xiàn)基于內(nèi)容的檢索。本文介紹了云制造服務(wù)平臺搜索引擎技術(shù)的國內(nèi)外研究現(xiàn)狀，云制造服務(wù)平臺的關(guān)鍵技術(shù)。對供需智能匹配的關(guān)鍵字搜索算法和語義搜索算法進(jìn)行了分析和比較。論述了語義本體在供需匹配中的應(yīng)用以及云制造服務(wù)中供需智能匹配引擎的實現(xiàn)。傳統(tǒng)的信息檢索方法是將用戶輸入的檢索關(guān)鍵字按照字面匹配的方法在云制造服務(wù)資源庫中檢索目標(biāo)結(jié)果，檢索系統(tǒng)僅僅將關(guān)鍵詞作為符號，無法理解其語義含義�；陬I(lǐng)域本體的智能匹配引擎的設(shè)計核心是引入領(lǐng)域本體層作為匹配和推理的關(guān)鍵部件，，與傳統(tǒng)的檢索方法相比，增加了本體檢索推理層。 本課題研究的供需智能匹配引擎來源于知識、語義的匹配檢索方式,這種檢索方式主要是利用規(guī)范后的檢索按領(lǐng)域和標(biāo)注后的信息源索引庫來進(jìn)行語義的匹配和搜索，再提交給檢索系統(tǒng)的一段過程。本體技術(shù)被引入云制造服務(wù)資源，按不同應(yīng)用領(lǐng)域?qū)φZ義進(jìn)行檢索，多義詞之間的相互聯(lián)系和語義關(guān)聯(lián)也能被很好的解決，使信息檢索中的詞義干擾大大減少，節(jié)約了耗時，縮小了檢索的范圍，能有效解決信息分類錯亂等問題，提高了用戶的滿意度。 通過對云制造服務(wù)平臺智能搜索引擎的設(shè)計與開發(fā)，可以看出盡管語義Web在元數(shù)據(jù)描述和本體領(lǐng)域方面的研究已經(jīng)基本成熟，要充分發(fā)揮Web的潛能，完全實現(xiàn)語義Web的構(gòu)想，還面臨許多問題和挑戰(zhàn)。
[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.
【學(xué)位授予單位】：湖北工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP391.1;TP393.09

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