Self-learning semantic search engine

1. A method for providing a self-learning semantic search engine, comprising:

setting up, by a computer processor, an initial configuration for a semantic network;

building, by the processor, indexes and semantic indexes in a search engine coupled to the semantic network, wherein the indexes indicate terms included in the at least one document and the semantic indexes include at least one concept defining a semantic relation between at least two terms of the at least one document;

receiving, by the processor, a request for business data;

updating, by the processor:

the indexes and

the semantic indexes by adjusting a ranking of an existing term based on context information derived from the request;

retrieving a document from the search engine that matches the request;

receiving markups or bookmarks for the document; and

updating indexes and semantic indexes with the user markups or bookmarks.

2. The method of claim 1 , wherein the initial configuration for the semantic network is received from a user.

3. The method of claim 1 , wherein the initial configuration for the semantic network is extracted from existing business objects.

4. The method of claim 1 , wherein the semantic indexes includes at least one of: user preferences information based on user roles; synonyms for business terms based on user roles; indicators indicating importance of business terms based on user roles.

5. The method of claim 1 , wherein the semantic dispatcher dispatches the request for business data based on user's business roles, semantic terminology and business objects assigned to the semantic terminology.

6. The method of claim 1 , wherein the context information derived from the request includes a relationship to a term derived from the request.

7. The method of claim 1 , wherein the updating of the indexes and semantic indexes of the semantic search engine includes reusing semantic knowledge.

8. The method of claim 1 , wherein the updating of the indexes and semantic indexes of the semantic search engine includes reusing terminology defined in a configuration of the semantic network to influence an importance of business terminology.

9. A non-transitory machine readable storage medium embodied with computer instructions for causing a processor to execute a process for building a self-learning semantic search engine, the process comprising:

setting up an initial configuration for a semantic network;

building indexes and semantic indexes in a search engine coupled to the semantic network, wherein the indexes indicate terms included in the at least one document and the semantic indexes include at least one concept defining a semantic relation between at least two terms of the at least one document;

receiving a request for business data;

updating, by the processor:

the indexes and

the semantic indexes by adjusting a ranking of an existing term based on context information derived from the request;

retrieving a document from the search engine that matches the request;

receiving markups or bookmarks for the document; and

updating indexes and semantic indexes with the user markups or bookmarks.

10. The non-transitory machine readable storage medium of claim 9 , wherein the initial configuration for the semantic network is received from a user.

11. The non-transitory machine readable storage medium of claim 9 , wherein the initial configuration for the semantic network is extracted from existing business objects.

12. The non-transitory machine readable storage medium of claim 9 , wherein the semantic indexes includes at least one of: user preferences information based on user roles; synonyms for business terms based on user roles; indicators indicating importance of business terms based on user roles.

13. The non-transitory machine readable storage medium of claim 9 , wherein the semantic dispatcher dispatches the request for business data based on user's business roles, semantic terminology and business objects assigned to the semantic terminology.

14. The non-transitory machine readable storage medium of claim 9 , wherein the context information derived from the request includes a relationship to a term derived from the request.

15. A system for providing a self-learning semantic search engine, the system comprising:

a database for storing business data related to an enterprise;

a user input-output device coupled to the database;

a processor responsive to inputs from the user input device and coupled to the database, the processor configured to:

set up an initial configuration for a semantic network;

build indexes and semantic indexes in a search engine coupled to the semantic network, wherein the indexes indicate terms included in the at least one document and the semantic indexes include at least one concept defining a semantic relation between at least two terms of the at least one document;

receive a request for business data;

update the indexes and semantic indexes, wherein the semantic indexes are updated by adjusting a ranking of an existing term based on context information derived from the request;

retrieve a document from the search engine that matches the request;

receive markups or bookmarks for the document; and

update indexes and semantic indexes with the markups or bookmarks.

16. The system of claim 15 , wherein the initial configuration for the semantic network is received from a user.

17. The system of claim 15 , wherein the initial configuration for the semantic network is extracted from existing business objects.

18. The system of claim 15 , wherein the semantic indexes includes at least one of: user preferences information based on user roles; synonyms for business terms based on user roles; indicators indicating importance of business terms based on user roles.

19. The system of claim 15 , wherein the semantic dispatcher dispatches the request for business data based on user's business roles, semantic terminology and business objects assigned to the semantic terminology.

20. The system of claim 15 , wherein the context information derived from the request includes a relationship to a term derived from the request.