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 eld. 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 eld lends a new perspective for experimentation. From ‘term gravitation’ over time, one can compute its generating potential whose uctuations manifest modications
['Sándor Darányi', 'Peter Wittek', 'Konstantinos Konstantinidis', 'Symeon Papadopoulos', 'Efstratios Kontopoulos']
A Physical Metaphor to Study Semantic Drift