Pidgin English for Topic Maps Knowledge Engineering

This paper introduces a new generation of the AsTMa= notation which has evolved from earlier versions based on usage patterns from novices and casual users alike. These experiences have shaped a low-barrier language which is optimized for human-centric encoding of semantically rich Topic Maps content.