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New Concept based Indexing Technique for Search Engine

机译:基于新概念的搜索引擎索引技术

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摘要

Objectives: To find a better indexing method of search engine for better information retrieval. Methods/Statistical Analysis: Indexing technique is very important part of any search engine or information retrieval system. There are many indexing techniques proposed earlier but they are not accurately retrieving information from the database. In this paper we are trying to propose a new indexing technique for maximizing attempt to get proper information according to search query by the information retrieval system. There are many different parts to a search engine index, such as design factors and data structures. When a search engine index is being built, there are also many different types of data structures to choose from. Some well known data structures are suffix tree, tree, inverted index, Citation index, Ngram index, Term document matrix, which are all used for different type of index designing for search engine. In this paper combination of different existing indexing methods has been used to form a new indexing technique. Findings: The experimental results described in the paper show that the accuracy of search results using S-N Indexing methods is 5% better than existing search engine indexing techniques. Application/Improvements: In this paper we are trying to design an indexing technique which is combination of Ngram index and suffix tree index for better information retrieval process by search engines. So, proposed method of indexing technique can improve searching results with more accurate and more related way.
机译:目的:寻找一种更好的搜索引擎索引方法,以更好地检索信息。方法/统计分析:索引技术是任何搜索引擎或信息检索系统的重要组成部分。先前提出了许多索引技术,但是它们无法从数据库中准确检索信息。在本文中,我们试图提出一种新的索引技术,以最大程度地尝试根据信息检索系统的搜索查询来获取适当的信息。搜索引擎索引有很多不同的部分,例如设计因素和数据结构。建立搜索引擎索引时,也有许多不同类型的数据结构可供选择。一些众所周知的数据结构是后缀树,树,倒排索引,引文索引,Ngram索引,术语文档矩阵,它们都用于搜索引擎的不同类型的索引设计。在本文中,已将现有不同索引方法的组合用于形成一种新的索引技术。结果:本文描述的实验结果表明,使用S-N索引方法的搜索结果的准确性比现有的搜索引擎索引技术高5%。应用/改进:本文试图设计一种将Ngram索引和后缀树索引相结合的索引技术,以更好地利用搜索引擎检索信息。因此,提出的索引技术方法可以更准确,更相关的方式改善搜索结果。

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