  The knowledge contained in academic literature is interesting to mine.
Inspired by the idea of molecular markers tracing in the field of biochemistry,
three named entities, namely, methods, datasets and metrics are used as AI
markers for AI literature. These entities can be used to trace the research
process described in the bodies of papers, which opens up new perspectives for
seeking and mining more valuable academic information. Firstly, the entity
extraction model is used in this study to extract AI markers from large-scale
AI literature. Secondly, original papers are traced for AI markers. Statistical
and propagation analysis are performed based on tracing results. Finally, the
co-occurrences of AI markers are used to achieve clustering. The evolution
within method clusters and the influencing relationships amongst different
research scene clusters are explored. The above-mentioned mining based on AI
markers yields many meaningful discoveries. For example, the propagation of
effective methods on the datasets is rapidly increasing with the development of
time; effective methods proposed by China in recent years have increasing
influence on other countries, whilst France is the opposite. Saliency
detection, a classic computer vision research scene, is the least likely to be
affected by other research scenes.
