Knowledge graph-based intelligent data management and information innovation service model for university library systems
Fig 2
System operation example: Illustration of personalized recommendation service scenarios.
The graph shows personalized recommendation processes designed for a graduate student (top path) and an undergraduate (bottom path) having varying levels of expertise because of differences in their academic backgrounds. The books (dark green), journals (blue), and attributes (orange ovals) are interlinked through using semantic links (edge labels). The recommended items have confidence scores (88%−96%) calculated through using paths in the knowledge graph. The recommendations vary depending upon historical usage, level of expertise, as well as research interest of the user.