Abstract
Web contains a vast amount of data, which are accumulated, studied, and utilized by a huge number of users on a daily basis. A substantial amount of data on the Web is available in an unstructured format, such as Web pages, books, journals, and files. Acquiring appropriate information from such humongous data has become quite challenging and a time-consuming task. Trivial keyword-based information retrieval systems highly depend on the statistics of data, thus facing word mismatch problem due to inevitable semantic and context variations of a certain word. Therefore, this marks the desperate need to organize such massive data into a structured format so that information can be easily processed in a large context by taking data semantics into account. Ontologies are not only being extensively employed in the semantic Web to store unstructured information in an organized and structured way but it has also raised the performance of diverse information retrieval approaches to a great extent. Ontological information retrieval systems retrieve data based on the similarity of semantics between the user query and the indexed data. This paper reviews modern ontology-based information retrieval methods for textual, multimedia, and cross-lingual data types. Furthermore, we compare and categorize the most recent approaches used in the above-mentioned information retrieval methods along with their major drawbacks and advantages.