10. The computer program product of claim 8 , further comprising program code that is readable and executable by the processor to: determine a likelihood that a particular synthetic context-based object is pulled into an appropriate context-based data gravity well according to a Bayesian probability formula of: P ⁡ ( A | B ) = P ⁡ ( B | A ) ⁢ P ⁡ ( A ) P ⁡ ( B ) where: P(A|B) is the probability that a synthetic context object will be an appropriate populator of a particular context-based data gravity well (A) given that (|) a predefined amount of confirmed context objects are applied to the non-contextual data object in a synthetic context-based object (B); P(B|A) is the probability that the predefined amount of confirmed context-based objects are applied to the non-contextual data object in the synthetic context-based object (B) given that (|) the synthetic context-based object is assigned to the particular context-based data gravity well (A); P(A) is the probability that the particular synthetic context-based object will be the appropriate populator of the particular context-based data gravity well regardless of any other information; and P(B) is the probability that the particular synthetic context-based object will have the predefined amount of confirmed context objects regardless of any other information.
8. A computer program product for defining multiple context-based data gravity wells on a context-based data gravity wells membrane, wherein the context-based data gravity wells membrane is a mathematical framework for a data structure, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code readable and executable by a processor to perform a method comprising: receiving a data stream of non-contextual data objects, wherein each of the non-contextual data objects ambiguously relates to multiple subject-matters; associating one of the non-contextual data objects with a context object to define a synthetic context-based object, wherein the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of said one of the non-contextual data objects; parsing the synthetic context-based object into an n-tuple, wherein the n-tuple comprises a pointer to said one of the non-contextual data objects, a probability that a non-contextual data object has been associated with a correct context object, and a weighting factor of importance of the synthetic context-based object; calculating a virtual mass of a parsed synthetic context-based object, wherein the virtual mass of the parsed synthetic context-based object is derived from a formula of: 
 P ( C )× Wt ( S ), where P(C) is the probability that the non-contextual data object has been associated with the correct context object, and where Wt(S) is the weighting factor of importance of the synthetic context-based object; creating multiple context-based data gravity well frameworks on a context-based data gravity wells membrane, wherein each of the multiple context-based data gravity well frameworks comprises at least one non-contextual data object and at least one context object, and wherein the context-based data gravity wells membrane is a virtual mathematical membrane that is capable of supporting multiple context-based data gravity wells; transmitting multiple parsed synthetic context-based objects to the context-based data gravity wells membrane; and defining multiple context-based data gravity wells according to the virtual mass of multiple parsed synthetic context-based objects that are pulled into each of the context-based data gravity well frameworks, wherein each of the multiple parsed synthetic context-based objects is pulled into a particular context-based data gravity well in response to values from its n-tuple matching said at least one non-contextual data object and said at least one context object in said particular context-based data gravity well.