Abstract
Document similarity had become essential in many applications such as document retrieval, recommendation systems, and plagiarism checker. Many similarity evaluation approaches rely on word-based document representation, because it is very fast. But these approaches are not accurate when documents with different language and vocabulary are used. When graph representation is used for documents, they use some relational knowledge which is not feasible in many applications because of expensive graph operations. In this work a novel approach for document similarity computation which utilizes verbal intent has been developed. This improves the similarity and graph databases were also used for faster performance. The performance of the system is evaluated using various datasets and verbal intent-based approach has registered promising results.
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Priya, V., Umamaheswari, K. (2020). Document Similarity Approach Using Grammatical Linkages with Graph Databases. In: Haldorai, A., Ramu, A., Mohanram, S., Onn, C. (eds) EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-19562-5_13
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DOI: https://doi.org/10.1007/978-3-030-19562-5_13
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