Exploring the influence of semantic data on the ranking mechanisms of search engines

The web has been evolving towards its Semantic future and, in turn, search engines (SEs) have been evolving to incorporate these changes. Current work probes on search engine ranking factors in a Web 2.0 and Web 3.0 context. To this end, we have created a headless crawler, LSHrank, which employs known search engine APIs, evaluates the results and explores the factors that affect the search engine ranking schemas. Specifically, this work focuses on the influence of the webpage and semantic characteristics on search engine results, as well as the identification of significant differences in the tactics of the search engines. We validate our hypothesis experimentally, that SEs are not only influenced by Semantic characteristics but based on their inherent mechanism, they value the various characteristics in a different context. LSHrank’s ultimate objective is the creation of a future Search Engine Optimization (SEO) framework that will enrich webpages with appropriate content, thus leading to their optimal ranking in search engine result pages (SERPs).