Abstract
Document ontology matching is one of the fastest-growing fields in the world of Semantic Web Technologies. Clustering documents based on their contents is a difficult task due to the under-optimized algorithms. In the proposed method, TF-IDF is used to select the features in order to analyse the documents and the word occurrences in the document. Multiple variations of TF-IDF are used as feature selection methods to analyse the best method clubbed with other measuring techniques and optimization algorithms to devise a high precision and accurate algorithm for Discovering Patterns in the documents. The Dataset used in the proposed method is CHIC heritage dataset in the English language which contains over 1000 documents. WebPMI and KL divergence is used as a measuring technique to measure semantic similarity between two queries. At last Whale Optimization Algorithm (WOA) is used to optimize the whole framework and produce high accuracy results.
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Sawarn, S., Deepak, G. (2021). An Approach for Document Clustering Using Semantic Similarity and Whale Optimization. In: Musleh Al-Sartawi, A.M., Razzaque, A., Kamal, M.M. (eds) Artificial Intelligence Systems and the Internet of Things in the Digital Era. EAMMIS 2021. Lecture Notes in Networks and Systems, vol 239. Springer, Cham. https://doi.org/10.1007/978-3-030-77246-8_31
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DOI: https://doi.org/10.1007/978-3-030-77246-8_31
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