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Enhanced Search Engine Using Proposed Framework and Ranking Algorithm Based on Semantic Relations
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DOI：10.1109/ACCESS.2019.2941937.png)
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摘要
En 中文
Today, most users need search engines to facilitate search and information retrieval processes. Unfortunately, traditional search engines have a significant challenge that they should retrieve high-precision results for a specific unclear query at a minimum response time. Also, a traditional search engine cannot expand a small, ambiguous query based on the meaning of each keyword and their semantic relationship. Therefore, this paper proposes a comprehensive search engine framework that combines the benefits of both a keyword-based and a semantic ontology-based search engine. The main contributions of this work are developing an algorithm for ranking results based on fuzzy membership value and a mathematical model of exploring a semantic relationship between different keywords. In the conducting experiments, eight different test cases were implemented to evaluate the proposed system. Executed test cases have achieved a precision rate of 97% with appropriate response time compared to the relevant systems.
Keyword:
Information retrieval
semantic search
semantic ranker
search engine
resource description framework
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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