Abstract
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.
Highlights
The proliferation of the Internet and social media have added new challenges to the traditional search engines that must be addressed
The proposed implementation runs on five personal computers which one master PC used to split tasks between slaves, and others are slaves used to perform different offline processes and PCs characteristics are Intel (R) Core (TM) i7-4702MQ CPU @ 2.20 GHz processor
The implemented eight different test cases will be conducted as follows: 1) Test Case I: In this test case, the system is triggered by a simple unclear single word as query
Summary
The proliferation of the Internet and social media have added new challenges to the traditional search engines that must be addressed. The retrieved results may be affected by the ranking process [2] based on their relevance and semantic relation to the subject of the search [3]–[5]. There are several significant challenges in this field that can be summarized as follows [6]–[8]: i) Most current search engines rely on indexing and retrieving different pages on keywords only that are often small, unclear, and does not reflect the meaning of the topic. El-Gayar et al.: Enhanced Search Engine Using Proposed Framework and Ranking Algorithm Based on Semantic Relations ii) A novel of preprocessing algorithm is developed to extract useful keywords from crawled pages. The remaining of this article is divided as follows: Section II reviews previous work of traditional, semantic information retrieval schemes and focused on ranking methods.
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