F-GSC, A Flexible Graph-based Subject Classifier for Scientific Articles using Topic Hierarchy
Keywords:
Scholarly search engine, Knowledge graph, Topic hierarchy, Flexible classifier, Subject classification, Sequence graphAbstract
Subject classification of articles using a topic hierarchy or classifier systems enables scholarly search engines to retrieve relevant articles in a fast and efficient manner. This work proposes a flexible knowledge graph-based topic hierarchy that is so flexible as to add new keywords to it. This is an extension of our previous work, which uses a knowledge graph-based topic hierarchy for the subject classification of scholarly articles based on a sequence-based full-text indexing approach into six levels, including the relevant subjects, categories, areas, disciplines, fields, and keywords. The proposed work is tested for efficiency and reliability using the arXiv dataset.
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