Comparing the Performance of Information Retrieval of Semantic and Keyword Search Engines Based on Phrase Search

Purpose: The aim of the research is to compare the performance of Information Retrieval of semantic and keyword search engines based on phrase search (simple & complex). Methodology: The present applied and semi-experimental research community includes all active search engines on the web. Research samples were selected based on stratified random sampling and purposive sampling. The data collection tool of two researcher-made checklists includes ten simple and complex phrase queries. Findings: Bing and Cluuz (with similar precision of 53%), DuckDuckGo, and Yahoo were the most accurate in searching for simple phrases, respectively. Bing, DuckDuckGo, Yahoo, and Cluuz were the most accurate in their search for complex terms, respectively. In general, Bing, DuckDuckGo, Cluuz and Yahoo have the highest precision, respectively. Also, the average total precision of keyword search engines is more heightened than semantic search engines. Conclusion: The Bing keyword search engine performs better than the other three semantic search engines and other keywords. Semantic search engines claim to have more capabilities in retrieving relevant information than keyword search engines. But in this study, it was found that Cluuz and DuckDuckGo do not excel in search terms over keyword search engines. These tools did not perform as well as semantic web search engines, and it seems that they have a long way to go to become real semantic search engines. And to achieve this, it is necessary to use the facilities, tools, modules, and emerging technologies of the new age, such as machine learning, deep learning, combining these modules with pervasive techniques, data mining, etc. Value: So far, not been compared the phrase search performance in the sample semantic and keyword search engines. And in this regard, the researcher has tried to achieve an actual result with an exact Survey