This review presents various perspectives on converting user keywords into a formal query. Without understanding the dataset’s underlying structure, how can a user input a text-based query and then convert this text into semantic protocol and resource description framework query language (SPARQL) that deals with the resource description framework (RDF) knowledge base? The user may not know the structure and syntax of SPARQL, a formal query language and a sophisticated tool for the semantic web (SEW) and its vast and growing collection of interconnected open data repositories. As a result, this study examines various strategies for turning natural language into formal queries, their workings, and their results. In an Internet search engine from a single query, such as on Google, numerous matching documents are returned, with several related to the inquiry while others are not. Since a considerable percentage of the information retrieved is likely unrelated, sophisticated information retrieval systems based on SEW technologies, such as RDF and web ontology language (OWL), can help end users organize vast amounts of data to address this issue. This study reviews this research field and discusses two different approaches to show how users with no knowledge of the syntax of semantic web technologies deal with queries.