An Intelligent Semantic-Aware Academic Library System Using Natural Language Processing and Knowledge Graphs

Traditional library systems designed for academic research suffer from poor integration when employing AI-based techniques for enhancement purposes. These limitations are mainly related to ignoring semantic relationships during search, overlooking hidden relationships among words, and lacking the generalization capability required to enhance researchers’ experience. To address these issues, the Intelligent Library System (IntLS) is proposed and further enhanced through effective knowledge graph modeling to enable more accurate retrieval of results. In addition, the basic NLP processing steps are optimized to capture hidden relationships during tokenization, stop-word removal, and stemming stages. The architecture of the proposed system consists of two main components: a semantic component, responsible for generating semantic representations of documents, and an analytics component, responsible for analyzing historical searches to predict future user needs and support effective resource management. The proposed system is compared with two well-known systems using similarity-based accuracy and a set of AI-based evaluation metrics. The enhanced system, Enh-IntLS, demonstrates superior performance, achieving a 1.9% improvement in similarity-based accuracy and a 1.7% improvement in AI-based accuracy.