Integrating prior field knowledge as key documents with main path analysis utilizing key-node path search

The integration of prior field knowledge in analytical or modeling processes is generally considered favorable across various disciplines, yet its utilization in Main Path Analysis (MPA) has been limited to gathering documents or validating the obtained main paths (MPs). This study envisions that prior knowledge about a field can be embodied in certain key documents that are considered seminal or crucial to the field's development. A so-called key-node path search is then employed to produce MPs that capture a distinct knowledge flow centering around these key documents. This study further proposes a unified approach that automatically and simultaneously produces the key-document MPs alongside the traditional MPs. Through this unified approach, the focused knowledge flow through the key documents and the field's overall knowledge flow, as revealed by the traditional MPs, can be concurrently observed to see how they interact, thereby providing additional insights into the field's development. Not only may the key-document MPs capture a meaningful development trajectory, but their complement to the traditional MPs can also hint at their respective representativeness. To establish this unified approach, this study formally demonstrates how the traditional MPs can be produced with key-node path searches, enabling their simultaneous creation alongside the key-document MPs. A case study is conducted based on patents in the field of Evolutionary Computation from an official artificial intelligence patent dataset to demonstrate the application of this unified approach.