Generating searchable keywords

• US 20070288451A1
• Filed: 06/07/2006
• Published: 12/13/2007
• Est. Priority Date: 06/07/2006
• Status: Active Grant

• Current Assignee: SAP SE
• Original Assignee: SAP AG (SAP SE)
• Inventors: Meyer, Marek, Hildebrandt, Tomas, Faatz, Andreas
• Granted Patent: US 7,849,078 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06F 16/907 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually

Abstract

Generating searchable keywords includes obtaining sets of parameters that include weights, where the weights correspond to actions for changing first words into predicted keywords, using the sets of parameters to obtain predicted keywords from the first words, determining a prediction value for each set of parameters, where the prediction values are based on an extent to which predicted keywords for a set of parameters match predefined keywords, ranking the sets of parameters based on prediction values, and applying a selected set of parameters to a second word to obtain a new keyword, wherein the selected set of parameters is applied based on rank.

Claims

1. A method comprising:

obtaining sets of parameters comprising weights, the weights corresponding to actions for changing first words into predicted keywords;

using the sets of parameters to obtain predicted keywords from the first words;

determining a prediction value for each set of parameters, the prediction values being based on an extent to which predicted keywords for a set of parameters match predefined keywords;

ranking the sets of parameters based on prediction values; and

applying a selected set of parameters to a second word to obtain a new keyword, wherein the selected set of parameters is applied based on rank.

2. The method of claim 1, wherein the selected set of parameters has a prediction value that indicates a best match between predicted keywords and predefined keywords, and wherein the method further comprises:

receiving feedback indicating whether the new keyword is acceptable; and

if the new keyword is not acceptable, applying another set of parameters to the second word to obtain an additional keyword.

3. The method of claim 2, wherein the other set of parameters has a prediction value that indicates a next best match between predicted keywords and predefined keywords.

4. The method of claim 1, wherein the weights comprise costs assigned to actions required to change the first words into the predicted keywords, the actions comprising character transitions.

5. The method of claim 4, wherein the character transitions comprise one or more of:

deleting an end position character, deleting a middle position character, deleting a beginning position character, editing the end position character, editing a middle position character, editing the beginning position character, inserting the end position character, inserting a middle position character, and inserting the beginning position character; and

wherein the end position comprises a last character position in the first word, the beginning position comprises a first character position in the first word, and a middle position comprises a position in the first word between the end position and the beginning position.

6. The method of claim 1, wherein a weight for a set of parameters is based on whether a first word is part of a title of data or a body of the data.

7. The method of claim 1, wherein the new keyword is part of metadata for a learning object, the learning object being stored in a repository that is part of an electronic learning system.

8. The method of claim 1, wherein the prediction value is determined for plural predicted keywords, the plural predicted keywords corresponding to keywords that are related to the first word hierarchically.

9. The method of claim 1, further comprising:

storing the new keyword as metadata for a data object;

receiving a search query; and

returning information relating to the data object if at least part of the search query matches the new keyword.

10. The method of claim 1, further comprising obtaining the first words by removing stop words from text that includes the first words.

11. The method of claim 2, wherein the feedback is received from a user;

and

wherein the method further comprises;

receiving feedback indicating whether the additional is acceptable;

if the additional keyword is not acceptable, applying another set of parameters to the second word to obtain a next keyword; and

repeating receiving and applying until an acceptable keyword is obtained.

12. One or more machine-readable media for storing instructions that are executable by one or more processing devices to perform the following actions:

obtaining sets of parameters comprising weights, the weights corresponding to actions for changing first words into predicted keywords;

using the sets of parameters to obtain predicted keywords from the first words;

determining a prediction value for each set of parameters, the prediction values being based on an extent to which predicted keywords for a set of parameters match predefined keywords;

ranking the sets of parameters based on prediction values; and

applying a selected set of parameters to a second word to obtain a new keyword, wherein the selected set of parameters is applied based on rank.

13. The one or more machine-readable media of claim 12, wherein the selected set of parameters has a prediction value that indicates a best match between predicted keywords and predefined keywords, and wherein the one or more machine-readable media further comprises instructions to perform the following actions:

receiving feedback indicating whether the new keyword is acceptable; and

if the new keyword is not acceptable, applying another set of parameters to the second word to obtain an additional keyword.

14. The one or more machine-readable media of claim 13, wherein the other set of parameters has a prediction value that indicates a next best match between predicted keywords and predefined keywords.

15. The one or more machine-readable media of claim 12, wherein the weights comprise costs assigned to actions required to change the first words into the predicted keywords, the actions comprising character transitions.

16. The one or more machine-readable media of claim 15, wherein the character transitions comprise one or more of:

deleting an end position character, deleting a middle position character, deleting a beginning position character, editing the end position character, editing a middle position character, editing the beginning position character, inserting the end position character, inserting a middle position character, and inserting the beginning position character; and

wherein the end position comprises a last character position in the first word, the beginning position comprises a first character position in the first word, and a middle position comprises a position in the first word between the end position and the beginning position.

17. The one or more machine-readable media of claim 12, wherein a weight for a set of parameters is based on whether a first word is part of a title of data or a body of the data.

18. The one or more machine-readable media of claim 12, wherein the new keyword is part of metadata for a learning object, the learning object being stored in a repository that is part of an electronic learning system.

19. The one or more machine-readable media of claim 12, wherein the prediction value is determined for plural predicted keywords, the plural predicted keywords corresponding to keywords that are related to the first word hierarchically.

20. The one or more machine-readable media of claim 12, further comprising instructions that are executable by one or more processing devices to perform the following actions:

storing the new keyword as metadata for a data object;

receiving a search query; and

returning information relating to the data object if at least part of the search query matches the new keyword.