Systems and methods for semantic data integration

• US 9,406,018 B2
• Filed: 06/22/2012
• Issued: 08/02/2016
• Est. Priority Date: 01/31/2012
• Status: Active Grant

• Current Assignee: Infosys Limited
• Original Assignee: Infosys Limited
• Inventors: Upadhyaya, Sujatha Raviprasad, Pisipati, Radha Krishna
• Primary Examiner(s): Chaki, Kakali
• Assistant Examiner(s): Seck, Ababacar
• Application Number:
  US13/531,117
• Publication Number:
  US 20130198117A1
• Time in Patent Office: 1,502 Days
• Field of Search: None
• US Class Current: 1/1
• 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 semantic integration computing device, the device comprising:

at least one processor;

a memory coupled to the processor and configured to be capable of executing programmed instructions, which comprise the programmed instructions stored in the memory to;

receive one or more input commands;

create an ontology by mapping the one or more input commands to one or more concepts, wherein the created ontology comprises one or more ontology levels, the one or more ontology levels comprising the one or more concepts;

compose one or more data queries based on the one or more input commands;

query one or more data sources based on the composed one or more data queries, the queried one or more data sources comprising one or more persistent data sources and one or more dynamic data sources comprising one or more data elements;

tag the one or more data elements in the queried one or more data sources with the one or more concepts, wherein the one or more data elements archived for a duration exceeding a predetermined time period are tagged as persistent data and the one or more data elements archived for a duration not exceeding the predetermined time period are tagged as dynamic data; and

consolidate the tagged one or more data elements in accordance with the one or more concepts, wherein the one or more data elements tagged as persistent data are associated with the one or more persistent data sources and the one or more data elements tagged as dynamic data are associated with the one or more dynamic data sources.

2. The device as claimed in claim 1, wherein the ontology comprises at least one semantic map, a concept dictionary, or one or more process ontologies, wherein the at least one semantic map, the concept dictionary, or the one or more process ontologies comprise best practices, expert knowledge, or an application of machine learning technique on the one or more data sources.

3. The device as claimed in claim 2, wherein the one or more data elements comprises metadata describing the relationships between the one or more data elements.

4. The device as claimed in claim 2, wherein the processor is further configured to be capable of executing programmed instructions, which comprise the programmed instructions stored in the memory to:

resolve one or more conflicts between the one or more data elements by mapping the one or more data elements to the one or more process ontologies.

5. The device as claimed in claim 1, wherein the processor is further configured to be capable of executing programmed instructions, which comprise the programmed instructions stored in the memory to:

identify an information good in response to the one or more data queries, wherein an information good comprises a semantically related set of data elements.

6. The device as claimed in claim 1, wherein the created ontology comprises an index.

7. The device as claimed in claim 1, wherein the processor is further configured to be capable of executing programmed instructions, which comprise the programmed instructions stored in the memory to:

define, relationships between entities at different levels in the one or more ontology levels.

8. The device as claimed in claim 1, wherein the processor is further configured to be capable of executing programmed instructions, which comprise the programmed instructions stored in the memory to:

incorporate new standard ontologies into the created ontology to describe one or more data elements in the one or more data sources.

9. The device as claimed in claim 1, wherein the processor is further configured to be capable of executing programmed instructions, which comprise the programmed instructions stored in the memory to:

compose one or more sub-queries based on the one or more data queries.

10. The device as claimed in claim 1, wherein the processor is further configured to be capable of executing programmed instructions, which comprise the programmed instructions stored in the memory to:

organize the consolidated one or more data elements based on the one or more input commands.

11. The device as claimed in claim 1, wherein the one or more data sources comprises one or more dynamic data sources comprising one or more dynamic data elements generated after a pre-determined time period.

12. The device as claimed in claim 11, wherein the processor is further configured to be capable of executing programmed instructions, which comprise the programmed instructions stored in the memory to:

order the created ontology hierarchically, wherein the higher ontology levels comprise the one or more dynamic data elements.

13. The device as claimed in claim 1, wherein the processor is further configured to be capable of executing programmed instructions, which comprise the programmed instructions stored in the memory to:

track the origin and movement of the one or more data elements in the one or more data sources, wherein the tracked movement of the one or more data elements is a measure of a quantity of tags with which the one or more data sources is associated.

14. The device as claimed in claim 1, wherein the processor is further configured to be capable of executing programmed instructions, which comprise the programmed instructions stored in the memory to:

identify the one or more data sources that are semantically relevant to the one or more data queries; and

retrieve the one or more data elements from the identified one or more data sources.

15. A method for integration of semantically related data, the method comprising:

receiving, by the semantic integration computing device, one or more input commands;

creating, by the semantic integration computing device, an ontology by mapping the one or more input commands to one or more concepts, wherein the created ontology comprises one or more ontology levels, the one or more ontology levels comprising the one or more concepts;

composing, by the semantic integration computing device, one or more data queries based on the one or more input commands;

querying, by the semantic integration computing device, one or more data sources based on the composed one or more data queries, the queried one or more data sources comprising one or more persistent data sources and one or more dynamic data sources comprising one or more data elements;

tagging, by the semantic integration computing device, the one or more data elements in the queried one or more data sources with the one or more concepts, wherein the one or more data elements archived for a duration exceeding a predetermined time period are tagged as persistent data and the one or more data elements archived for a duration not exceeding the predetermined time period are tagged as dynamic data; and

consolidating, by the semantic integration computing device, the tagged one or more data elements in accordance with the one or more concepts, wherein the one or more data elements tagged as persistent data are associated with the one or more persistent data sources and the one or more data elements tagged as dynamic data are associated with the one or more dynamic data sources.

16. The method as claimed in claim 15, wherein the ontology comprises at least one semantic map, a concept dictionary, or one or more process ontologies, wherein the at least one semantic map, the concept dictionary, or the one or more process ontologies comprise best practices, expert knowledge, or an application of machine learning technique on the one or more data sources.

17. The method as claimed in claim 16, further comprising:

resolving, by the semantic integration computing device, one or more conflicts between the one or more data elements by mapping the one or more data elements to the one or more process ontologies.

18. The method as claimed in claim 15, further comprising:

identifying, by the semantic integration computing device, information goods in response to the one or more data queries, wherein an information good is comprises semantically related set of data elements.

19. The method as claimed in claim 15, wherein the created ontology comprises an index.

20. The method as claimed in claim 15, further comprising:

defining, by the semantic integration computing device, relationships between entities at different levels in the one or more ontology levels.

21. The method as claimed in claim 15, further comprising:

incorporating, by the semantic integration computing device, new standard ontologies into the created ontology to describe the one or more data elements in the one or more data sources.

22. The method as claimed in claim 15, further comprising:

composing, by the semantic integration computing device, one or more sub-queries based on the one or more data queries.

23. The method as claimed in claim 15, further comprising:

organizing, by the semantic integration computing device, the consolidated one or more data elements based on the one or more input commands.

24. The method as claimed in claim 15, wherein the one or more data sources comprises one or more dynamic data sources comprising one or more dynamic data elements generated after a pre-determined time period.

25. The method as claimed in claim 24, further comprising:

ordering, by the semantic integration computing device, the created ontology hierarchically, wherein the higher ontology levels comprise the one or more dynamic data elements.

26. The method as claimed in claim 15, further comprising:

tracking, by the semantic integration computing device, the origin and movement of the one or more data elements in the one or more data sources, wherein the tracked movement of the one or more data elements is a measure of a quantity of tags with which the one or more data sources is associated.

27. The method as claimed in claim 15, wherein the one or more data elements comprises metadata describing the relationships between the tagged one or more data elements.

28. The method as claimed in claim 15, further comprising:

identifying, by the semantic integration computing device, the one or more data sources that are semantically relevant to the one or more data queries; and

retrieving, by the semantic integration computing device, the one or more data elements from the identified one or more data sources.

29. A non-transitory computer readable medium having stored thereon instructions for optimizing the performance of one or more software components at runtime comprising machine executable code which when executed by at least one processor, causes the processor to perform steps comprising:

receiving one or more input commands;

creating an ontology by mapping the one or more input commands to one or more concepts, wherein the created ontology comprises one or more ontology levels, the one or more ontology levels comprising the one or more concepts;

composing one or more data queries based on the one or more input commands;

querying one or more data sources based on the composed one or more data queries, the queried one or more data sources comprising one or more data elements, the queried one or more data sources comprising one or more persistent data sources and one or more dynamic data sources comprising one or more data elements;

tagging the one or more data elements in the queried one or more data sources with the one or more concepts, wherein the one or more data elements archived for a duration exceeding a predetermined time period are tagged as persistent data and the one or more data elements archived for a duration not exceeding the predetermined time period are tagged as dynamic data; and

consolidating the tagged one or more data elements in accordance with the one or more concepts, wherein the one or more data elements tagged as persistent data are associated with the one or more persistent data sources and the one or more data elements tagged as dynamic data are associated with the one or more dynamic data sources.

30. The medium as claimed in claim 29, wherein the ontology comprises at least one semantic map, a concept dictionary, or one or more process ontologies, wherein the at least one semantic map, the concept dictionary, or the one or more process ontologies comprise best practices, expert knowledge, or an application of machine learning technique on the one or more data sources.

31. The medium as claimed in claim 30, further having stored thereon instructions that when executed by the processor cause the processor to perform steps further comprising:

resolving one or more conflicts between the one or more data elements by mapping the one or more data elements to the one or more process ontologies.

32. The medium as claimed in claim 29, further having stored thereon instructions that when executed by the processor cause the processor to perform steps further comprising:

identifying information goods in response to the one or more data queries, wherein an information good is comprises semantically related set of data elements.

33. The medium as claimed in claim 29, wherein the created ontology comprises an index.

34. The medium as claimed in claim 29, further having stored thereon instructions that when executed by the processor cause the processor to perform steps further comprising:

defining relationships between entities at different levels in the one or more ontology levels.

35. The medium as claimed in claim 29, further having stored thereon instructions that when executed by the processor cause the processor to perform steps further comprising:

incorporating new standard ontologies into the created ontology to describe the one or more data elements in the one or more data sources.

36. The medium as claimed in claim 29, further having stored thereon instructions that when executed by the processor cause the processor to perform steps further comprising:

composing one or more sub-queries based on the one or more data queries.

37. The medium as claimed in claim 29, further having stored thereon instructions that when executed by the processor cause the processor to perform steps further comprising:

organizing the consolidated one or more data elements based on the one or more input commands.

38. The medium as claimed in claim 29, wherein the one or more data sources comprises one or more dynamic data sources comprising one or more dynamic data elements generated after a pre-determined time period.

39. The medium as claimed in claim 38, further having stored thereon instructions that when executed by the processor cause the processor to perform steps further comprising:

ordering the created ontology hierarchically, wherein the higher ontology levels comprise the one or more dynamic data elements.

40. The medium as claimed in claim 29, further having stored thereon instructions that when executed by the processor cause the processor to perform steps further comprising:

tracking the origin and movement of the one or more data elements in the one or more data sources, wherein the tracked movement of the one or more data elements is a measure of a quantity of tags with which the one or more data sources is associated.

41. The medium as claimed in claim 29, wherein the one or more data elements comprises metadata describing the relationships between the one or more data elements.

42. The medium as claimed in claim 29, further having stored thereon instructions that when executed by the processor cause the processor to perform steps further comprising:

identifying the one or more data sources that are semantically relevant to the one or more data queries; and

retrieving the one or more data elements from the identified one or more data sources.