【正文】 會 包括爬行機(jī)器人排除協(xié)議文件。 URL 服務(wù)器作為網(wǎng)址鏈接的存儲地。索引器會將數(shù)據(jù)進(jìn)行分類。大多數(shù)的搜索引擎將 8 壓縮所有的數(shù)據(jù)以便最大限度的利用存儲空間。然后關(guān)鍵詞 會進(jìn)入反向索 引當(dāng)中， 這便會指向一個 連接到 一個網(wǎng)站的鏈接 。圖 2 顯示了 ZENITH 模型的整體構(gòu)架。 它可以是開戶銀行名稱、付款方法等。一旦被發(fā)現(xiàn) ,它將在索引中標(biāo)出謂語。 ZENITH擴(kuò)展架構(gòu) Zenith 擴(kuò)展結(jié)構(gòu)中只有很少的幾個組成部分一起工作才可以賦予 NUTCH 語義能力。該算法如圖 3。發(fā)展 Nutch 插件包括擴(kuò)展 Nutch 所提供的 IndexerFilter接口。這些數(shù)據(jù)隨后會被儲存在記憶中以便應(yīng)對以后操作的需要。它還為語義索引提供了一個通過添加規(guī)則來推斷出 11 Ontology 的推理引擎。Ontoloay 的題目范圍越寬越廣，搜索引擎便會獲得越多的語義索引。 G. OntoIndex OntoIndex 是一個在循環(huán)訪問 Ontology 數(shù)據(jù)時為語義索引作參考點(diǎn)的索引文件。 效績評估 H. Methodology 這部分突出了一個檢驗 NUTCH 語義機(jī)制效益的引導(dǎo)測試。 12 圖 4. Test Architecture 測試數(shù)據(jù)的層次顯示如圖 5： 圖 5. Hierarchies of Test Data Ontology 是基于 Methontology（ Fernandez） [25]建立的。然后根據(jù)支持搜索引擎最高的效率來修改 Ontology。在這種情況下，它有兩個等級的子類。然后利用軟件 Prot233。支持活動主要獲取客戶方面的電子商務(wù)的知識以及 Ontology 的基本因素。顯示的結(jié)果是一組通過搜索引擎得到的數(shù)據(jù)索引比較。 表 2. Search Results Comparison . 作為一個模型，用于搜索引擎的 Ontology 不能反映真實(shí)世界的數(shù)據(jù)和命名規(guī)則。 J. Issues 14 有限數(shù)據(jù) 由于資源有限，只有小數(shù)據(jù)量的測試才可以實(shí)現(xiàn)。雖然結(jié)果會顯示出包含關(guān)鍵詞實(shí)體或與關(guān)鍵詞語義相關(guān)的網(wǎng)址 和文件，但是它不會根據(jù)重要性的排序來顯示結(jié)果。英語單詞中大部分都是一些含義取決于上下文的模糊單詞。 L. Word Word 是一種英語詞匯數(shù)據(jù)庫。語義索引器會被修改來利用分布式計算影響 Ontology 的推斷進(jìn)程以及術(shù)語的比較進(jìn)程。最后，以上的分析與討論并不是決定性的。 search engine, semantic, information retrieval, ontology. INTRODUCTION The Web at its infancy was a static page which allows users to open and read the contents of 19 the Web pages. There was only a oneway interaction between the users and the Web. As the technology advances, Webenabled devices were getting cheaper and more ubiquitous. More and more people are able to access the Web and utilize the wealth of information in it. This triggered a paradigm shift in Web usage and the way people interact with the Web. Experts and laymen coined this shifting in Web interaction as Web . A Web site enables users to interact with the Web more interactively with still and moving graphics as well as sound [1]. Users were also able to publish their contents for the consumption of other users. It gave way to the birth of Web technologies such as Friendster [2], Youtube [3], Blogger [4] and Facebook [5]. It was an age of ?content by the users for the users?. Content creation were not limited to just an organization but also to anyone who has access to the Inter. As envisioned by Tim Berners Lee in his book titled Weaving the Web [6], the Web will implement semantic properties in its collection of Web pages which will understand the words and terms human used. The large amount of information on the Web can be retrieved using a search engine. Since Web , many search engines were developed and been mercialized. These search engines such as Google[7], AskJeeves[8], Yahoo![9], and Lycos[10] were among the search engines that were dominating at its time. Search engines help users by indexing all the information on the Web and make it easy and quickly retrievable for the users. Early search engines were not a search engine at all. Instead it was a directory which contained indexed information which were indexed manually by the directory provider. It was Google who among the first that implement automate indexing and crawling mechanism which enables the search engine to automatically crawl Web pages and indexed the retrieved Web pages for users to search [11]. Google uses page rank by keeping track the number of ining links and links linked to other pages. The more links linked to a page the more 20 credible the page is, thus will be ranked higher than the other. All these were being puted using mathematical algorithms by calculating the term frequency and inverted term frequency. Data crawled and collected were stored in an inverted database which enables the search engine to locate which terms were stored in which document or links. There is no doubt that the collection of information on the Web is increasing. As for now, with the current search engine which utilizes on mathematical algorithms will be able to cope. As the collection of the information bees larger, it will dilute the accuracy of conventional search engine making it less accurate and less precise. The dilution of the result accuracy will be further aggravated as the collection of information grows rapidly. This work aims to tackle the problem from a different angle. Instead of trying to preserve the accuracy of search engine by relying on machines speed and processing power to enable the usage of more sophisticated mathematical algorithms, this work will explore semantic utilization in search engine. By implementing semantic mechanism in search engine, it will enable information to be related to each other conceptually. This will give the information indexed with the semantic value which can improve information retrieval. Major contributions of this work are basically the analysis of semantic search engine and the development of semantic search engine prototype which enables user to have more accurate search semantically. Section 2 describes related works done. Section 3 describes the methodology used to analyze and develop a semantic search engine. Section 4 describes the architecture and algorithm used in order to provide semantic mechanism for the search engine. Section 5 concludes the paper and finally section 6 describes future improvements to this work. RELATED WORKS 21 Currently, a general purpose ?semantic? search engine had been developed. The search engine can be accessed at . However, most of the mechanism used in the search engine were patented and focused on mercial use. As quoted by Tim Berners Lee [6], ?I mention patents in passing, but they are a great stumbling block for Web development?. All the technologies used in Hakia[12] were patented and therefore are trade secret. This prevent academic circle to study intricate workings of the search engine for future improvement and other applications. Many search engines have been developed throughout the years. One of the most dominant was Google[7]. Many t