Semantic matching system and method

1. A computer-based method for determining similarity between at least two heterogenous unstructured data records and for optimizing processing performance, the method comprising:

generating, by at least one processor that is configured by executing code stored on non-transitory processor readable media, a plurality of occupational data records;

creating, by the at least one processor, for each of the occupational data records, a respective vector in an n-dimensional non-orthogonal unit vector space by calculating dot products between unit vectors corresponding to concepts from an ontology to represent the occupational data record;

slicing, by the at least one processor, each of the vectors into a plurality of chunks;

performing, by the at least one processor, semantic matching for each of the chunks in parallel to compare at least one occupational data record to at least one other occupational data record; and

outputting, by the at least one processor, values representing similarities between at least two of the occupational data records.

2. The method of claim 1 , wherein each of the respective vectors has magnitude and direction.

3. The method of claim 1 , further comprising applying correlation coefficients derived from information provided by an ontology.

4. The method of claim 1 , further comprising weighting vectorially represented concepts.

5. The method of claim 1 , further comprising storing information associated with dot products that are above zero or at least equal to a predefined threshold.

6. The method of claim 1 , wherein the matching step includes performing asymmetric comparisons.

7. The method of claim 6 , wherein the asymmetric comparisons are based on cosine similarity.

8. The method of claim 1 , wherein the output is sorted based on degree of similarity.

9. A computer-based system for determining similarity between at least two heterogenous unstructured data records and for optimizing processing performance, the system comprising:

at least one processor configured to access non-transitory processor readable media, the at least one processor further configured, when executing instructions stored on the non-transitory processor readable media, to:

generate a plurality of occupational data records;

create, for each of the occupational data records, a respective vector in an n-dimensional non-orthogonal unit vector space by calculating dot products between unit vectors corresponding to concepts from an ontology to represent the occupational data record;

slice each of the vectors into a plurality of chunks;

perform semantic matching for each of the chunks in parallel to compare at least one occupational data record to at least one other occupational data record; and

output values representing similarities between at least two of the occupational data records.

10. The system of claim 9 , wherein each of the respective vectors has magnitude and direction.

11. The system of claim 10 , wherein the at least one processor is further configured to:

apply correlation coefficients derived from information provided by an ontology.

12. The system of claim 10 , wherein the at least one processor is further configured to:

weight vectorially represented concepts.

13. The system of claim 9 , wherein the at least one processor is further configured to:

store information associated with dot products that are above zero or at least equal to a predefined threshold.

14. The system of claim 9 , wherein the matching step includes performing asymmetric comparisons.