Claims:We claim:
1. A method of generating an ontology based on a plurality of tickets in an enterprise system 100, the method comprising:
processing, by the enterprise system 100, input data associated with the plurality of tickets to obtain a structured data 228 associated with each of the plurality of tickets;
performing multi-level clustering, by the enterprise system 100, on the structured data 228 to obtain a plurality of clusters and corresponding error indicators, based on one or more parameters associated with the plurality of tickets;
mapping, by the enterprise system 100, each cluster of the plurality of clusters with each of the error indicators, to obtain a mapped data at each cluster of the plurality of clusters; and
generating, by the enterprise system 100, the ontology using the mapped data at each cluster of the plurality of clusters, corresponding to the plurality of tickets.

2. The method as claimed in claim 1, wherein the input data comprises at least one of ticket dump, one or more data sources of the enterprise system 100 and user input data.

3. The method as claimed in claim 2, wherein the ticket dump comprises at least one of a ticket identification (ID) 212, a ticket number 214, a ticket title 216, a problem description 220, a resolution category and a resolution description 222 associated with each ticket.

4. The method as claimed in claim 2, wherein each of the one or more data sources comprises a ticket description 218, a resolution date 224 and a resolution time 226.

5. The method as claimed in claim 1, wherein processing the input data to obtain the structured data 228 comprises:
filtering ticket description 218 of the input data using at least one parameter associated with a ticket of the plurality of tickets to obtain a first filtered data 230;
filtering resolution description 222 of the input data using the at least one parameter associated with the ticket to obtain a second filtered data 232; and
obtaining the structured data 228 associated with each of the plurality of tickets using the first filtered data 230 and second filtered data 232.

6. The method as claimed in claim 5, wherein performing the multi-level clustering on the structured data 228 comprises:
mapping data associated with the first filtered data 230 and the second filtered data 232 based on a plurality of first error indicators 234, to obtain a first cluster group 236;
mapping data associated with the first filtered data 230 and the second filtered data 232 based on a plurality of second error indicators 238 encountered by a user, to obtain a second cluster group 240; and
mapping each of the error indicators associated with the first cluster group 236 and the second cluster group 240 based on a resolution obtained to generate a third cluster group 242.

7. The method as claimed in claim 1, wherein the mapping each cluster of the plurality of clusters with each of the error indicators comprises:
obtaining relationship for each of the plurality of clusters with the error indicators using modified natural language processing (NLP);
identifying a root node using the obtained relationship for each of the plurality of clusters with the error indicators;
generating a relationship from a higher level of the cluster to next level of the cluster using the root node; and
aligning the relationships for each of the plurality of clusters from a higher level to a lower level.

8. The method as claimed in claim 1, wherein the mapped data at each of the plurality of clusters comprises a root error indicator 244 corresponding to each of the plurality of clusters.

9. The method as claimed in claim 8, wherein generating the ontology using the mapped data at each cluster of the plurality of clusters comprises grouping each of the plurality of clusters based on the root error indicator 244.

10. An enterprise system 100 for generating an ontology based on plurality of tickets, the enterprise system 100 comprising:
a processor 204; and
a memory 206, communicatively coupled to the processor 204, wherein the memory 206 stores processor-executable instructions, which, on execution, causes the processor 204 to:
process input data associated with the plurality of tickets to obtain a structured data 228 associated with each of the plurality of tickets;
perform multi-level clustering on the structured data 228 to obtain a plurality of clusters and corresponding error indicators, based on one or more parameters associated with the plurality of tickets;
map each cluster of the plurality of clusters with each of the error indicators, to obtain a mapped data at each cluster of the plurality of clusters; and
generate the ontology using the mapped data at each cluster of the plurality of clusters, corresponding to the plurality of tickets.

11. The enterprise system as claimed in claim 10, wherein the input data comprises at least one of ticket dump, one or more data sources of the enterprise system 100 and user input data.

12. The enterprise system as claimed in claim 11, wherein the ticket dump comprises at least one of a ticket identification (ID) 212, a ticket number 214, a ticket title 216, a problem description 220, a resolution category and a resolution description 222 associated with each ticket.

13. The enterprise system as claimed in claim 11, wherein each of the one or more data sources comprises a ticket description 218, a resolution date 224 and a resolution time 226.

14. The enterprise system as claimed in claim 10, wherein to process the input data to obtain the structured data 228, the instructions cause the processor 204 to:
filter ticket description 218 of the input data using at least one parameter associated with a ticket of the plurality of tickets to obtain a first filtered data 230;
filter resolution description 222 of the input data using the at least one parameter associated with the ticket to obtain a second filtered data 232; and
obtain the structured data 228 associated with each of the plurality of tickets using the first filtered data 230 and second filtered data 232.

15. The enterprise system as claimed in claim 14, wherein to perform multi-level clustering on the structured data 228, the instructions cause the processor 204 to:
map data associated with the first filtered data 230 and the second filtered data 232 based on a plurality of first error indicators 234, to obtain a first cluster group 236;
map data associated with the first filtered data 230 and the second filtered data 232 based on a plurality of second error indicators 238 encountered by a user, to obtain a second cluster group 240; and
map each of the error indicators associated with the first cluster group 236 and the second cluster group 240 based on a resolution obtained to generate a third cluster group 242.

16. The enterprise system as claimed in claim 10, wherein to map each cluster of the plurality of clusters with each of the error indicators, the instructions causes the processor 204 to:
obtain a relationship for each of the plurality of clusters with the error indicators using modified natural language processing (NLP);
identify a root node using the obtained relationship for each of the plurality of clusters with the error indicators;
generate a relationship from a higher level of the cluster to next level of the cluster using the root node; and
align the relationships for each of the plurality of clusters from a higher level to a lower level.

17. The enterprise system as claimed in claim 10, wherein the mapped data at each of the plurality of clusters comprises a root error indicator 244 corresponding to each of the plurality of clusters.

18. The enterprise system as claimed in claim 17, wherein to generate the ontology using the mapped data at each cluster of the plurality of clusters, the instructions causes the processor 204 to group each of the plurality of clusters based on the root error indicator 244.

Dated this 17th day of February, 2017

SRAVAN KUMAR GAMPA
OF K & S PARTNERS
AGENT FOR THE APPLICANT
, Description:
TECHNICAL FIELD
The present subject matter is related, in general to an enterprise system, and more particularly, but not exclusively to a method and a system for generating an ontology based on a plurality of tickets.