  In accessibility tests for digital preservation, over time we experience
drifts of localized and labelled content in statistical models of evolving
semantics represented as a vector field. This articulates the need to detect,
measure, interpret and model outcomes of knowledge dynamics. To this end we
employ a high-performance machine learning algorithm for the training of
extremely large emergent self-organizing maps for exploratory data analysis.
The working hypothesis we present here is that the dynamics of semantic drifts
can be modeled on a relaxed version of Newtonian mechanics called social
mechanics. By using term distances as a measure of semantic relatedness vs.
their PageRank values indicating social importance and applied as variable
`term mass', gravitation as a metaphor to express changes in the semantic
content of a vector field lends a new perspective for experimentation. From
`term gravitation' over time, one can compute its generating potential whose
fluctuations manifest modifications in pairwise term similarity vs. social
importance, thereby updating Osgood's semantic differential. The dataset
examined is the public catalog metadata of Tate Galleries, London.
