Knowledge Graphs for Intelligent Decision-Making in Library Networks

Abstract

With everything moving towards a data world, libraries should not just sit back and be left as repositories of bits. They must evolve and be complex networks. The topic of the study is around using Knowledge Graphs (KGs) as an infrastructure for library network technology to support smart decisions within a library. Knowledge Graphs provide a semantic framework of the relationships between catalogs, digital collections, logs of clients' actions, research outputs, and institutional information stores with heterogeneous data. Thus, giving us a richer understanding of context and automated reasoning. We will demonstrate how KGs can be used to illuminate resource allocation, develop collection strategies, examine client trends, and provide other personalized services for decision-making. In the ultimate phase of the KGs applications, machine learning (ML) with knowledge graphs (KGs) will be considered to improve the predictive ability of the system, thus refocusing strategies towards a more proactive than reactive. Case studies concentrate on academic libraries of higher education and show how KG-based systems may be combined to facilitate discovery, enhance interoperability and boost operational efficiency. We highlight the issues of poorer data quality, ontology matching, and privacy concerning knowledge graphs and offer scalable governance frameworks to address these challenges. Knowledge graphs can be used in library networks to create intelligent and adaptive learning infrastructures, as well as optimization and foresight systems. These systems enhance resources for knowledge dissemination and engage the community in this digital era.