Retrieval system enhanced by fine‐grained knowledge entities.

Along with the massive increase of academic full texts, users now have a growing need for fine‐grained academic literature retrieval. However, few studies have attempted to build domain knowledge retrieval system for academic literature in information science. Compared with other disciplines, information science pays more attention to the domain knowledge entities sequence including data resources, models and software. To fill the gap, this paper use academic full texts from JASIST. Using SVM, Text‐CNN, Bi‐LSTM and BERT to extract the knowledge entities sequence including data resource, model, software. The highest Precision, Recall and F1 are 92.45%, 93.86% and 93.14%, respectively. And the knowledge entity sequence indexing system is built based on the extracted knowledge entities sequences. It was found that the MRR score of the knowledge entities sequence retrieval system was higher by 13% as compared with the full text retrieval system. [ABSTRACT FROM AUTHOR]