Comprehension Normalization Method for Language
When researchers are studying literature, very often they are limited to understanding it only from their own point of reference. Siloed knowledge within their limited domain, inhibits the catalyzing wealth of knowledge discoverable through cross-disciplinary research.
Big Data Implementation
Just as people have unique languages, or unique relationships to words, data sets are now large enough to support their own unique languages. If two languages describe common realities but describe them differently, then the differences and similarities in the languages can act like metaphors extending knowledge about one to the other. CNM uses the unique language of a second data set to rephrase the content of the first data set, creating clusters of association in the starting language data set which have been uniquely brought together by the rephrasing language. For example with CNM, an ophthalmologist can read ophthalmology journals, but read them, or topics in them, grouped according to oncology journals. With CNM the researcher can study the same pieces of literature from the perspective of many different fields.
Big data support
- Interdisciplinary
- Easy
- Fast
Big data managed
- One
- Two
Large Data Sets Can Support Unique Languages
Languages can be created out of any semantic or otherwise complex data set, based on the connections which are sometimes similar and sometimes different between data sets in different fields.