SYSTEMS AND METHODS FOR SEMANTIC DATA INTEGRATION

• US 20130198117A1
• Filed: 06/22/2012
• Published: 08/01/2013
• Est. Priority Date: 01/31/2012
• Status: Active Grant

• Current Assignee: Infosys Limited
• Original Assignee: Infosys Limited
• Inventors: Upadhyaya, Sujatha Raviprasad, Pisipati, Radha Krishna
• Granted Patent: US 9,406,018 B2
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/025 Extracting rules from data
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

Embodiments of the present invention relate to a system for data integration and information retrieval by bringing semantically related data together for a given context. As described, the integration of data may include the building of an ontology, the mapping of one or more processes, semantic maps and concept dictionaries in the ontology to one or more data sources, tagging the data sources in accordance with the ontology, providing a query interface for accepting an input query from a user, the mapping of the input query to one or more concepts in the ontology, and deriving one or more subqueries thereby, and the querying of data sources in accordance with the composed one or more subqueries, wherein the data sources queried are tagged with one or more concepts from the ontology. Additionally, the tracking of data across data sources in accordance with a defined data value chain is disclosed.

Claims

1. A system for semantic integration of data, the system comprising:

a processor in operable communication with a processor readable storage medium, the processor readable storage medium containing one or more programming instructions whereby the processor is configured to implement;

a semantic data integrator, wherein the semantic data integrator comprises;

an ontology in an ontology repository, wherein the ontology comprises at least one ontology level and at least one concept in an ontology level;

a query interface module in communication with the ontology, wherein the query interface module is configured to receive a input command;

a semantic data integrator module in communication with the ontology and the query interface module, wherein the semantic data integrator module is configured to;

receive one or more input commands from the query interface;

map the input command to one or more concepts in the ontology and compose one or more subcommands from the input command thereby; and

query one or more data sources in accordance with the composed one or more data queries, wherein the one or more data sources queried are tagged with one or more concepts from the ontology; and

one or more persistent data sources stored on at least one database, wherein the data sources are communicably connected to the data integration layer, and tagged with at least one concept from the ontology.

2. The system as claimed in claim 1, wherein the ontology comprises a concept dictionary.

3. The system as claimed in claim 2, wherein the concept dictionary is built through a combination of best practices, expert knowledge, and the application of a machine learning technique on the one or more data sources.

4. The system as claimed in claim 2, wherein the ontology comprises at least one semantic map.

5. The system as claimed in claim 4, wherein the at least one semantic map is built through a combination of best practices, expert knowledge, and the application of a machine learning technique on the one or more data sources.

6. The system as claimed in claim 4, wherein the ontology comprises one or more process ontologies.

7. The system as claimed in claim 6, wherein the one or more process ontologies are built through a combination of best practices, expert knowledge, and the application of a machine learning technique on the one or more data sources.

8. The system as claimed in claim 1, further comprising identifying an information good in response to a query, wherein an information good is a semantically related set of data.

9. The system as claimed in claim 1, wherein the ontology comprises a multi-level index.

10. The system as claimed in claim 1, wherein relationships between entities at different levels in the ontology are defined by a user.

11. The system as claimed in claim 1, wherein new standard ontologies may be incorporated into the repository to describe data in a data source.

12. The system as claimed in claim 1, wherein the semantic data integrator is further configured to consolidate data received from one or more data sources in response to the query passed to each of the data sources.

13. The system as claimed in claim 12, wherein the consolidated data is organized in accordance with one or more concepts relevant to the input query received, and presented thereby as unified, semantically integrated data through an output interface, wherein the output interface includes a display device.

14. The system as claimed in claim 13, wherein the concept tagged to the at least one data source by the tagging process is selected from the ontology.

15. The system as claimed in claim 1, wherein a data source is marked as a persistent data source, and terminating thereby the operation of one or more tagging processes on the data source.

16. The system as claimed in claim 1, wherein the ontology is continually revised.

17. The system as claimed in claim 1, further comprising one or more processes associated with a data value chain.

18. The system as claimed in claim 17, wherein the data value chain comprises a data tracking process executed by at least one processor communicably connected to at least one data source, wherein the tracking process tracks the origin and movement of data in the data source, and wherein the movement of data is a measure of a quantity of tags with which the data source is associated.

19. The system as claimed in claim 18, wherein a tag comprises metadata relevant to the data encapsulated in the at least one data source.

20. The system as claimed in claim 19, wherein the data value chain comprises a data cleanup process.

21. A computer aided method for the integration of semantically related data, the method comprising:

building an ontology, the ontology comprising one or more concept dictionaries and semantic maps and inserting the ontology into an ontology repository stored on a processor readable storage medium;

specifying one or more process ontologies, and inserting the one or more process ontologies so specified into the ontology;

a computer implemented act of mapping the one or more processes, semantic maps and concept dictionaries to one or more data sources, wherein the one or more data sources are stored in at least one processor readable storage medium and comprise at least one data source, the mapping comprising;

tagging the at least one data source in accordance with the one or more process ontologies, concept dictionaries, or semantic maps specified;

a computer implemented act of providing a query interface for accepting an input query from a user, wherein the query interface is communicably coupled to the ontology repository;

a computer implemented act of mapping the input query to one or more concepts in the ontology, and deriving one or more subqueries thereby;

a computer implemented act of querying the one or more data sources in accordance with the composed one or more subqueries, wherein the one or more data sources queried are tagged with one or more concepts from the ontology.

22. The method as claimed in claim 21, wherein the ontology comprises a concept dictionary.

23. The method as claimed in claim 22, wherein the concept dictionary is built through a combination of best practices, expert knowledge, and the application of a machine learning technique on the one or more data sources.

24. The method as claimed in claim 22, wherein the ontology comprises at least one semantic map.

25. The method as claimed in claim 24, wherein the at least one semantic map is built through a combination of best practices, expert knowledge, and the application of a machine learning technique on the one or more data sources.

26. The method as claimed in claim 24, wherein the ontology comprises one or more process ontologies.

27. The method as claimed in claim 26, wherein the one or more process ontologies are built through a combination of best practices, expert knowledge, and the application of a machine learning technique on the one or more data sources.

28. The method as claimed in claim 21, further comprising identifying information goods in response to a query, wherein an information good is a semantically related set of data.

29. The method as claimed in claim 21, wherein the ontology comprises a multi-level index.

30. The method as claimed in claim 21, further comprising defining, by a user, relationships between entities at different levels in the ontology.

31. The method as claimed in claim 21, further comprising incorporating new standard ontologies into the ontology to describe data.

32. The method as claimed in claim 21, further comprising consolidating data received from one or more data sources in response to the query passed to each of the data sources.

33. The method as claimed in claim 32, wherein the consolidated data is organized in accordance with one or more concepts relevant to the input query received, and presented as unified, semantically integrated data through an output interface, wherein the output interface includes a display device.

34. The method as claimed in claim 33, wherein the concept tagged to the at least one data source by the tagging process is selected from the ontology.

35. The method as claimed in claim 21, further comprising marking a data source as a persistent data source, and terminating thereby the operation of one or more tagging processes on the data source.

36. The method as claimed in claim 21, wherein the ontology is continually revised.

37. The method as claimed in claim 21, further comprising subjecting the one or more data sources to one or more processes associated with a data value chain.

38. The method as claimed in claim 37, wherein the data value chain comprises a data tracking process executed by at least one processor communicably connected to at least one data source, wherein the tracking process tracks the origin and movement of data in the data source, and wherein the movement of data is a measure of a quantity of tags with which the data source is associated.

39. The method as claimed in claim 38, wherein a tag comprises metadata relevant to the data encapsulated in the at least one data source.

40. The method as claimed in claim 39, wherein the data value chain comprises a data cleanup process.

41. A non-transitory computer readable storage medium having stored thereon computer executable instructions for performing a method of optimizing the performance of one or more software components at runtime, the method comprising:

building an ontology comprising concept dictionaries and semantic maps and inserting the ontology into an ontology repository, wherein the ontology repository is stored on a processor readable storage medium;

inserting one or more process ontologies into the ontology;

mapping the one or more process ontologies, semantic maps and concept dictionaries to one or more data sources, wherein the one or more data sources are stored in at least one processor readable storage medium, the mapping comprising;

tagging the at least one data source in accordance with the one or more process ontologies, concept dictionaries, or semantic maps specified;

providing a command interface for accepting an input query or an input command from a user, wherein the command interface is communicably coupled to the ontology;

mapping the input query to one or more concepts in the ontology, and deriving one or more data commands thereby;

querying the one or more data sources in accordance with the composed one or more data queries, wherein the one or more data sources queried are tagged with one or more concepts from the ontology;

consolidating data received from one or more data sources in response to the query passed to each of the data sources;

organizing the consolidated data in accordance with one or more concepts relevant to the input query received; and

presenting the unified, semantically integrated data through an output interface.