Abstract
The Internet has a vast collection of information from different domains, including scientific knowledge in books, journals, and conference proceedings. When a user creates the query, these systems typically retrieve documents based on keywords that are irrelevant to the user in most cases. So, there is a need to retrieve the scientific knowledge more based on knowledge. This paper proposes a knowledge-centric scientific document recommendation framework for the recommendation of scientific documents. The recommendation is user query-centric and uses Lin Similarity for term enrichment. The preprocessing is done by Tokenization, Lemmatization, Stop Word Removal, and Named Entity Recognition (NER). Normalized Google Distance and Normalized Pointwise Mutual Information methods are used to compute semantic similarities to achieve ontology alignment. The final solution set is achieved using Flying Fox Algorithm, and the HybRDFSciRec achieves the best-in-class accuracy and high percentage of precision for a wide range of recommendations over the other baseline models, making it an efficient system for recommending scientific documents.
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Singh, D., Deepak, G. (2023). HybRDFSciRec: Hybridized Scientific Document Recommendation Framework. In: Abraham, A., Bajaj, A., Gandhi, N., Madureira, A.M., Kahraman, C. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2022. Lecture Notes in Networks and Systems, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-031-27499-2_41
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