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

Images of the "future of work" : a discourse analysis of visual data on the internet

This paper presents findings from a critical discourse analysis of visual data gathered in regular, monthly data sampling on Google, DuckDuckGo and Bing on the theme ‘the future of work’ that were published online on Polish and Swedish websites during 2018–2021. Visions about the future in the form of images create an archive of ideas on the potential directions of societal development, where discourse is present both in what is visible, and what is invisible. The study shows predominantly stereotypical framings of work by young office workers. Conclusions are drawn on how the future is visualized contrary to popular claims of job losses that are predicted to strike mainly the younger, middle-class population. In the images collected, humans appear as mainly content in a working life without manual labour, frustration or clutter, but also without leisure, displaying a lack of visions of an older workforce, as well as the possible role of humans as useful and fulfilled without work in the future

Designing Authority Data Properties Based on Microdata Method and Study of Web Search Engines’ Reaction to Them

Objective: The purpose of this research was to study the Search Engine’s responses to authority data properties embedded into schema.org-based metadata on the Microdata syntax. Methods: The experimental method was used in this research. The research population comprised 400 records of authority metadata based on the Microdata method from the digital library of Allameh Tabataba'i University. The examination group consisted of 200 metadata records, 100 records with authority data extensions embedded into schema.org-based metadata in the Microdata syntax and 100 other similar records in the JSON-LD syntax (50 samples of name authority, and 50 other subject authority) And the control group consisted of 200 Records, including 100 Records related to the description of the book in the Microdata syntax and 100 other similar records in the JSON-LD syntax. The records have been published on the independent website at www.Aghadeh.ir and have been introduced to the Google, Bing, Yahoo, and Yandex search engines as designers of the schema.org standard. Then, through searching the search engines, using the data gathering tool, the checklist provided by the researchers, the indexing and retrieval of the metadata records of the control groups and experimental groups were evaluated in the search results of the selected search engines. Results: The results of this study showed that search engines were able to index and retrieve all of the metadata records and values of added extensions associated with authority data. Such a possibility had the same status for the name authority records and the subject authority data. Conclusions: By retrieving each of the variant properties’ values of examination group’s records, in addition to the authorized values of the name and subject terms, a suitable platform for the comprehensiveness of the retrieve process, and the authority control in the Web search tools will be improved

Measuring the relevance of retrieved images in search engines based on Persian language writing styles

The aim of this study was to investigate the image retrieval from selected search engines according to the written and semantic features of Persian language and determine their relevance using recall and precision formulas and to identify the most efficient search engine in retrieving images in Persian and by survey-analytical method. It was done using direct observation technique. After reviewing related researches, search keywords list was formed in the form of a checklist based on the written and semantic features of Persian language. Each of these keywords in the studied search engines, including two general search engines Google and Bing and Duckduckgo semantic search engine, which are among the most used search engines and have also provided the ability to search for images in Persian, search and the number of relevant and unrelated retrieved results were recorded. Then, the recall and precision of search results in each search engine were calculated and the relevance of images based on these features in each of the studied search engines was investigated. A variety of descriptive statistical techniques were applied to analyze the data along with Kolmogorov-Smirnov, Shapiro-Wilk, Kruskal-Wallis and Friedman tests. Findings demonstrated that Google, Bing and Duckduckgo search engines do not pay enough attention to the written and semantic features of Persian language and many of these features are ignored while searching and retrieving images. In the present study, Google search engine had a higher recall and precision than the other two search engines, and despite the claim of semantic search engines to provide better and more relevant information than other search engines, Duckduckgo search engine did not show good performance in retrieving images related to the written and semantic part of Persian language. There is also a significant difference between the recall and the precision of the three studied search engines