Hyperplane optimization in high dimensional ontology

1. A computer-implemented method for generating a description of a target skill set using domain specific language, comprising, on a processor:

ingesting a data set related to the target skill from a data store;

semantically analyzing the data set to generate a skill ontology, wherein the skill ontology includes relationships between the target skill and a plurality of related skills;

generating a hyperplane to separate one or more priority skills from among the plurality of related skills;

generating a description for the target skill from the one or more priority skills; and

presenting the generated description to a user.

2. The method of claim 1 , wherein the generated description comprises a job posting.

3. The method of claim 2 , wherein the job posting comprises specific technical language for a non-linear job role.

4. The method of claim 1 , wherein the generated description comprises a learning plan for the target skill.

5. The method of claim 1 , wherein semantically analyzing the data set to generate the skill ontology comprises defining a Jaccard similarity coefficient between each of the plurality of related skills.

6. The method of claim 5 , further comprising combining at least two of the plurality of related skills into skill groups.

7. The method of claim 1 , wherein semantically analyzing the data set to generate the skill ontology comprises encoding the plurality of related skills using one-hot encoding (OHE).

8. The method of claim 7 , wherein semantically analyzing the data set to generate the skill ontology further comprises mapping the OHE encoded data to a high-dimensional feature space.

9. The method of claim 8 , wherein semantically analyzing the data set to generate the skill ontology further comprises calculating a separator within the high-dimensional feature space.

10. The method of claim 9 , wherein semantically analyzing the data set to generate the skill ontology further comprises applying a support vector machine (SVM) to the separator.

11. The method of claim 1 , wherein semantically analyzing the data set comprises generating an ontology using graph-based relationships in the data sets.

12. A method of defining a learning plan to a skill set, comprising, using a processor:

ingesting a data set related to a target skill;

semantically analyzing the data set to generate a skill ontology, wherein the skill ontology includes relationships between the target skill and a plurality of related skills;

generating a hyperplane optimization across the skill ontology to separate one or more priority skills from among the plurality of related skills; and

generating a learning plan for the target skill from the one or more priority skills.

13. The method of claim 12 , wherein semantically analyzing the data set to generate the skill ontology comprises defining a Jaccard similarity coefficient between each of the plurality of related skills to combine at least two of the plurality of related skills into skill groups.

14. The method of claim 12 , wherein semantically analyzing the data set to generate the skill ontology comprises:

encoding the plurality of related skills using one-hot encoding (OHE);

mapping the OHE encoded data to a high-dimensional feature space;

calculating a separator within the high-dimensional feature space; and

applying a support vector machine (SVM) to the separator.

15. A method for automatically generating an open seat description, comprising:

ingesting a data set related to a target skill;

semantically analyzing the data set to generate a skill ontology, wherein the skill ontology includes relationships between the target skill and a plurality of related skills;

generating a hyperplane to separate one or more priority skills from among the plurality of related skills; and

generating an open seat description for the target skill from the one or more priority skills.

16. The method of claim 15 , wherein semantically analyzing the data set to generate the skill ontology comprises defining a Jaccard similarity coefficient between each of the plurality of related skills to combine at least two of the plurality of related skills into skill groups.

17. The method of claim 16 , wherein semantically analyzing the data set to generate the skill ontology comprises:

encoding the plurality of related skills using one-hot encoding (OHE);

mapping the OHE encoded data to a high-dimensional feature space;

calculating a separator within the high-dimensional feature space; and

applying a support vector machine (SVM) to the separator.

18. A computer program product for generating a description of a target skill set using domain specific language, the computer program product comprising:

one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising:

program instructions to ingest a data set related to the target skill;

program instructions to semantically analyze the data set to generate a skill ontology, wherein the skill ontology includes relationships between the target skill and a plurality of related skills; and

program instructions to generate a hyperplane to separate one or more priority skills from among the plurality of related skills.

19. The computer program product of claim 18 , further comprising program instructions to define a Jaccard similarity coefficient between each of the plurality of related skills to combine at least two of the plurality of related skills into skill groups.

20. The computer program product of claim 19 , further comprising program instructions to:

encode the plurality of related skills using one-hot encoding (OHE);

map the OHE encoded data to a high-dimensional feature space;

calculate a separator within the high-dimensional feature space; and

apply a support vector machine (SVM) to the separator.

21. A system for generating a description of a target skill set using domain specific language, comprising a processor operably connected to a memory, the memory containing program instructions to, when executed on the processor:

ingest a data set related to the target skill;

semantically analyze the data set to generate a skill ontology, wherein the skill ontology includes relationships between the target skill and a plurality of related skills; and

generate a hyperplane to separate one or more priority skills from among the plurality of related skills.

22. The system of claim 21 , further comprising program instructions to:

generate a description for the target skill from the one or more priority skills, wherein the generated description comprises a job posting; and

presenting the job posting to a user.

23. The system of claim 21 , wherein semantically analyzing the data set to generate the skill ontology comprises defining a Jaccard similarity coefficient between each of the plurality of related skills to combine at least two of the plurality of related skills into skill groups.

24. The system of claim 23 , wherein semantically analyzing the data set to generate the skill ontology comprises:

encoding the plurality of related skills using one-hot encoding (OHE);

mapping the OHE encoded data to a high-dimensional feature space;

calculating a separator within the high-dimensional feature space; and

applying a support vector machine (SVM) to the separator.

25. The system of claim 21 , further comprising program instructions to:

generate a description for the target skill from the one or more priority skills, wherein the generated description comprises a learning plan for the target skill; and

presenting the learning plan to a user.