Deep Model Framework for Ontology-Based Document Clustering

Searching of relevant documents from the web has become more challenging due to the rapid growth in information. Although there is enormous amount of information available online, most of the documents are uncategorized. It is a time-consuming task for the users to browse through a large number of documents and search for information about the specific topics. The automatic clustering from these documents could be important and has great potential to improve the efficiency of information seeking behaviors. To address this issue, the authors propose a deep ontology-based approach to document clustering. The obtained results are encouraging and in implementation annotation rules are used. The work compared the information extraction capabilities of annotated framework of using ontology and without using ontology. The increase in F-measure is achieved when ontology as the distance measure. The improvement of 11% is achieved by ontology in comparison with keyword search.