ShiftsSigns_LP

For many SemWeb people, big data does not mark an end of theory, but instead demands that we build better infrastructure for extracting meaning from data. Jim Hendler, director of the Institute for Data Exploration and Applications at RPI, suggests that data is moving from ‘big’ to ‘broad’ – that data today can be characterized, not only by its size, but also by its //variety//. To make broad data both meaningful and sharable, we thus need to innovate technologies that, through “semantics,” give it structure.
 * From big data to broad data**

The SemWeb aims to make it possible for machines to interpret meaning from data. Notably, this is done by “structuring” data – in the classic linguistic sense, giving data more syntax (not necessarily more semantics). Notable to this sign system is that meaning itself has become about logic (description logics, and first order logic) – partly because logical meaning is seen as more “objective” and partly because it is seen as more “pragmatic,” ...which leads me to my next point.
 * Meaning from syntax**

There is an increased emphasis on “pragmatism” in AI aspects of Semantic Web research. Specifically, AI researchers working on the SemWeb today look back on attempts to define the whole world (like Cyc) as impractical and not scalable. They thus have shifted to focus on how to implement just a “little semantics” or to establish “middle ontologies” that will be useful for most web developers.
 * “A little semantics goes a long way.”**