System, tools and methods to facilitate identification and organization of new information based on context of user's existing information

• US 20030220895A1
• Filed: 05/22/2002
• Published: 11/27/2003
• Est. Priority Date: 05/22/2002
• Status: Active Grant

• Current Assignee: Univa Corporation (Altair Engineering Incorporated)
• Original Assignee: Agilent Technologies, Inc.
• Inventors: Vailaya, Aditya
• Granted Patent: US 7,058,643 B2
• Time in Patent Office: Days
• US Class Current: 707/1
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: Y10S 707/99934 Query formulation, input preparation, or translation
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access
• CPC Class Codes: Y10S 707/99942 Manipulating data structure, e.g. compression, compaction, compilation

Abstract

A user'"'"'s context can be captured in terms of experimental results and other locally collected data. The use of extracted links between imported scientific text, biological models or experimental data, and proprietary or public data to provide relevance ranking, filtering, and categorization of imported scientific text or biological models, based upon their linking with the local data is described.

Claims

1. A method of facilitating identification and organization of new information that is relevant to the context of a user'"'"'s locally stored information, the method comprising the steps of:

extracting relevant data from a first data set;

converting the relevant data from the first data set to a local format;

linking the relevant data in the local format with locally formatted data from a second, locally stored data set which matches the relevant data; and

comparing data from the first data set that is relevant to the locally stored data set, based on links set up by the linking of data in the local format.

2. The method of claim 1, wherein the locally stored data set is experimental data.

3. The method of claim 1, further comprising the step of ranking the relevant data based on the number of links established between each instance of locally formatted relevant data and the locally stored, locally formatted data, relative to the number of links established between other instances of locally formatted relevant data and the locally stored, locally formatted data.

4. The method of claim 3, wherein the first data set comprises textual documents, each instance is contained within a textual document, and the ranking is performed on an entire document basis.

5. The method of claim 4, further comprising the step of further ranking the relevant data within each document.

6. The method of claim 3, wherein the first data set comprises textual documents, each instance is contained within a textual document, and the ranking is performed on a basis of relevant paragraphs or passages of text.

7. The method of claim 3, wherein the first data set comprises biological models, and the ranking is performed on a basis of entire models.

8. The method of claim 3, wherein the first data set comprises biological models, and the ranking is performed on a basis of relevant portions of the biological models.

9. The method of claim 3, wherein the first data set comprises experimental data, and the ranking is performed on a basis of portions of the experimental data.

10. The method of claim 1, wherein the step of comparing includes overlaying data from the locally stored data set on the first data set, or vice versa.

11. The method of claim 1, further comprising the steps of:

identifying a subset of the locally formatted data from the second, locally stored data set;

linking the relevant data in the local format with subset of the locally formatted data from the second, locally stored data set which matches the relevant data;

and filtering the first data set, based on the links formed by linking the relevant data in the local format with the subset of the locally formatted data from the second, locally stored data set, thereby identifying a subset of the first data set.

12. The method of claim 11, wherein the first data set comprises textual documents, and the subset is defined on an entire document basis.

13. The method of claim 11, wherein the first data set comprises textual documents, and the subset is defined by selecting one or more paragraphs or portions of at least one of the textual documents.

14. The method of claim 11, wherein the first data set comprises biological models, and the subset is defined on a basis of entire models.

15. The method of claim 11, wherein the first data set comprises biological models, and the subset is defined on a basis of relevant portions of the biological models.

16. The method of claim 11, wherein the first data set comprises experimental data, and the subset is defined on a basis of portions of the experimental data.

17. The method of claim 11, further comprising the step of comparing data from the subset of the first data set that is relevant to the locally stored data set, based on links set up by the linking of data in the local format.

18. The method of claim 17, wherein the step of comparing data from the subset of the first data set that is relevant to the locally stored data set, includes overlaying data from the locally stored data set on the subset of the first data set, or vice versa.

19. The method of claim 1, further comprising the steps of:

defining multiple subsets of the locally formatted data from the second, locally stored data set which correspond to buckets; and

linking the relevant data in the local format with each of the subsets of the locally formatted data from the second, locally stored data set which matches the relevant data; and

categorizing the first data set, based on the links formed by linking the relevant data in the local format with the subsets of the locally formatted data from the second, locally stored data set, wherein portions of the first data set having links to a particular subset of the locally formatted, locally stored data set are categorized in the bucket which corresponds to that subset, respectively.

20. The method of claim 19, wherein the first data set comprises textual documents, and the categorizing is performed on an entire document basis, such that the portions of data allocated to the buckets are in the form of entire documents.

21. The method of claim 19, wherein the first data set comprises textual documents, and the categorizing is performed on a basis of relevant paragraphs or passages of text from the textual documents, such that the portions of data allocated to the buckets are paragraphs or passages of text.

22. The method of claim 19, wherein the first data set comprises biological models, and the categorizing is performed on a basis of entire models, such that the portions of data allocated to the buckets are in the form of entire models.

23. The method of claim 19, wherein the first data set comprises biological models, and the categorizing is performed on a basis of relevant portions of entire models, such that the portions of data allocated to the buckets are in the form of portions of the models.

24. The method of claim 19, wherein the first data set comprises experimental data, and the categorizing is performed on a basis of portions of the experimental data, such that the portions of data allocated to the buckets are in the form of portions of the experimental data.

25. The method of claim 19, further comprising the step of selectively browsing at least one of the buckets.

26. The method of claim 1, wherein the step of comparing comprises overlaying at least one item of data from the locally stored data set on the first data set, or vice versa, which supports the data on which it is overlayed.

27. The method of claim 1, wherein the step of comparing comprises overlaying at least one item of data from the locally stored data set on the first data set, or vice versa, which opposes the data on which it is overlayed.

28. The method of claim 1, wherein the step of comparing comprises differentially overlaying at least one item of data from the locally stored data set on the first data set, or vice versa, which supports the data on which it is overlayed, in addition to overlaying at least one item which opposes the data on which it is overlayed.

29. A method of identification and organizing new information that is relevant to the context of a user'"'"'s locally stored information, the method comprising the steps of:

retrieving data resultant from a query of one or more databases;

mining the data to extract relevant data;

converting the relevant data from the first data set to a local format;

linking the relevant data in the local format with locally formatted data from a local data set which matches the locally formatted relevant data; and

ranking the data based on the number of links established between the locally formatted relevant data and the locally formatted local data.

30. The method of claim 29, further comprising the steps of:

identifying a subset of the locally formatted local data;

linking the relevant data in the local format with the subset of the locally formatted local data; and

filtering the data, based on the links formed by linking the relevant data in the local format with the subset of the locally formatted local data.

31. The method of claim 29, further comprising the steps of:

defining multiple subsets of the locally formatted local data;

assigning a bucket to each subset, respectively;

linking the relevant data in the local format with each of the subsets of the locally formatted local data which matches the relevant data;

and categorizing the data, based on the links formed by linking the relevant data in the local format with the subsets of the locally formatted local data, wherein portions of the data having links to a particular subset of the locally formatted, local data are categorized in the corresponding bucket, respectively.

32. The method of claim 31, further comprising the step of selectively browsing at least one of the buckets.

33. A method of identifying data relevant to the context of a user'"'"'s local data, the method comprising:

retrieving data resultant from a query of at least one scientific text database or biological model database;

mining the data to extract relevant data;

converting the relevant data from the first data set to a local format;

linking the relevant data in the local format with locally formatted data from a local data set which matches the locally formatted relevant data; and

organizing the relevancy of the data based on the number of links established between the locally formatted relevant data and the locally formatted local data.

34. The method of claim 33, wherein the step of organizing includes ranking the data.

35. The method of claim 33, wherein the step of organizing includes filtering the data.

36. The method of claim 33, wherein the step of organizing includes categorizing the data.

37. A system for identifying data relevant to the context of a user'"'"'s local data, the method comprising:

means for retrieving data resultant from a query of at least one scientific text database or biological model database;

means for mining the data to extract relevant data;

means for converting the relevant data from the first data set to a local format;

means for linking the relevant data in the local format with locally formatted data from a local data set which matches the locally formatted relevant data; and

means for organizing the relevancy of the data based on the number of links established between the locally formatted relevant data and the locally formatted local data.

38. The system of claim 37, wherein said means for organizing includes means for ranking the data.

39. The system of claim 37, wherein said means for organizing includes means for filtering the data.

40. The system of claim 37, wherein said means for organizing includes means for categorizing the data.

41. The system of claim 37, further including means for selectively browsing the organized data.

42. An application for ranking data retrieved from one or more scientific text databases or biological model databases, said application comprising:

means for converting relevant data extracted from the data to a local format;

means for linking the locally formatted relevant data with locally formatted local data of a user of the application; and

means for ranking the relevancy of the data based on the number of links established between the locally formatted relevant data and the locally formatted local data.

43. An application for filtering data retrieved from one or more scientific text databases or biological model databases, said application comprising:

means for converting relevant data extracted from the data to a local format;

means for linking the locally formatted relevant data with a subset of locally formatted local data of a user of the application; and

means for filtering the data based on the number of links established between the locally formatted relevant data and the subset of the locally formatted local data.

44. An application for categorizing data retrieved from one or more scientific text databases or biological model databases, said application comprising:

means for converting relevant data extracted from the data to a local format;

means for linking the locally formatted relevant data with subsets of locally formatted local data of a user of the application; and

means for categorizing the data based on the number of links established between the locally formatted relevant data and each of the subsets of the locally formatted local data.

45. A computer readable medium carrying one or more sequences of instructions from a user of a computer system for identifying data relevant to the context of a user'"'"'s local data, wherein the execution of the one or more sequences of instructions by one or more processors cause the one or more processors to perform the steps of:

converting relevant data extracted from the data to a local format;

linking the locally formatted relevant data with locally formatted local data of the user which matches the locally formatted relevant data; and

organizing the relevancy of the data based on the number of links established between the locally formatted relevant data and the locally formatted local data.

46. The computer readable medium of claim 45, wherein the step of organizing includes ranking the data.

47. The computer readable medium of claim 45, wherein the step of organizing includes filtering the data.

48. The computer readable medium of claim 45, wherein the step of organizing includes categorizing the data.

49. The computer readable medium of claim 45, wherein the following further step is performed:

selectively browsing the organized data.

50. The computer readable medium of claim 45, wherein the following further step is performed:

mining the data to extract the relevant data.

51. The computer readable medium of claim 50, wherein the following further step is performed:

retrieving the data as a result of a query of at least one scientific text database or biological model database.

52. The computer readable medium of claim 45, wherein the following further step is performed:

overlaying at least a portion of the locally stored data on at least a portion of the data, or vice versa.