In this review article, we link metrics to mechanisms, by demonstrating how emerging metrics not only offer complementaries to the existing metrics, but also shed light on the underlying mechanisms related to ten key quantities of interest in the Science of Science, including discovery significance, finding replicability, knowledge cumulativeness, and beyond. What is scientific knowledge, and how is it created, accumulated, transformed, and used? If we want to know the answers to these questions, we need to be able to uncover the structures and mechanisms of science, in addition to the metrics that are easily collectable and quantifiable. According to the indirect keyword relationship paired with the same author trace, creators and followers of knowledge evolution are different. During evolution, knowledge pairs stimulate each other’s growth, and some knowledge pairs transfer to others, demonstrating a small step toward knowledge change. The results indicate that knowledge evolution is not a continuous trend but alternating growth and obsolescence. Therefore, we present an empirical study of the informetrics field with five evolution stages: knowledge generation, growth, obsolescence, transfer, and intergrowth. Thus, knowledge evolution could be mined quantitatively from a different perspective. Additionally, the same author trace represents an indirect relationship that a keyword pair provided by the same author in a different paper. The indirect relationship is constructed by a keyword pair-based citation relationship, meaning the citation relationship between keyword co-occurrence pairs, acting as the sequential structure of knowledge pairs. The direct co-occurrence relationship is constructed by keyword co-occurrence pair and acts as the temporal structure of knowledge pairs.
Three relationships were applied: a direct co-occurrence relationship, indirect relationship by keyword pair citation, and same author trace, providing temporal and sequential knowledge evolution. However, multiple relationships between keywords provided by papers are rarely used to explore knowledge evolution. Further, paper keywords are considered efficient knowledge components to depict the knowledge structure of a research field by examining relationships between keywords. Understanding the knowledge evolution of a research field is crucial for researchers, policymakers, and stakeholders. Knowledge evolution offers a road map for understanding knowledge creation, knowledge transfer, and performance in everyday work.