Creation Of A Category Tree With Respect To The Contents Of A Data Stock

• US 20110113043A1
• Filed: 11/08/2010
• Published: 05/12/2011
• Est. Priority Date: 05/08/2008
• Status: Active Grant

• Current Assignee: Knecon Iqser Holding GmbH
• Original Assignee: IQser IP AG
• Inventors: Wurzer, Joerg, Magnus, Christian
• Granted Patent: US 8,745,069 B2
• Time in Patent Office: Days
• US Class Current: 707/750
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: G06F 16/374 Thesaurus

Abstract

Methods for the automatic creation of a category tree with respect to the contents of a data stock, wherein a taxonomy of the data stock will be created on the base of co-occurrences. Another object of the present invention is furthermore a data processing system comprising data which represent information in at least one data stock which is accessible via at least one data source, which is designed and/or adapted to at least partially carry out a method according to the invention. Another object of the present invention is furthermore a data processing device for the electronic processing of data, comprising a control and/or computer unit, an input unit and an output unit, which is designed and/or adapted to at least partially carry out a method according to the invention, preferably using at least a part of a data processing system according to the invention.

Claims

1. A method for the automatic creation of a category tree with respect to the contents of a data stock comprising information objects, wherein the information objects of the data stock are indexed in an index, characterized by the following process steps:

1. Filtering out stop words for each information object in the index by means of a list;

2. Creating a list of words in which the stop words which have been filtered out are not contained;

3. Calculating a significance value for each word in the list of words;

4. Sorting the list of words according to their significance values;

5. Reducing the sorted list of words to a maximum number;

6. Storing the reduced list of words in a table;

7. Detecting co-occurrences in the stored list of words;

8. Storing the co-occurrences in a table in a database;

9. Retrieving words which have the highest significance value but no co-occurrences;

10. Selecting a first level of the category tree from the retrieved words;

11. Retrieving words for each selected word of the first level by means of the co-occurrence table, which words are in co-occurrence with the respectively selected word of the first level;

12. Creating a list of words with the retrieved words;

13. Retrieving the frequency of each word on the list of words;

14. Sorting the list of words according to frequency;

15. Reducing the sorted list of words to a predetermined maximum number, wherein the words which comprise a frequency above average remain on the list of words;

16. Selecting another level of the category tree on the base of the determined words;

17. Iteratively repeating of the process steps 11 through 16 for at least one other level of the category tree, wherein in process step 11 during the retrieve of words by means of the co-occurrence table, for each selected word of the first and at least one other level, the words will be retrieved which are in co-occurrence with the respectively selected word of the first and at least one other level, until the quantity of retrieved/selected words is equal to zero.

2. The method according to claim 1, wherein in process step 3 during the calculation of a significance value for each word in the list of words, the significance value will be calculated as the quotient of the word frequency within the information object and the word frequency within the entire index.

3. The method according to claim 1, characterized in that the maximum number in process step 5 is limited to up to 50.

4. The method according to claim 1, wherein in process step 6 during the storing of the reduced list of words in a table, words in the table will be assigned to the significance value and in case that the significance value is higher than the significance value of an existing/assigned word, the higher significance value will be used.

5. The method according to claim 1, wherein in process step 8 during the persisting/storing of the co-occurrences in a database, the database will contain a table of co-occurrences (word 1 and word 2) having a frequency value in a table line, and wherein the frequency value will be increased by the factor 1, if a co-occurrence (word 1 and word 2) is already existing in the table.

6. The method according to claim 1, characterized in that the predetermined maximum number in process step 15 is limited to up to 20.

7. The method according to claim 1, characterized in that the created category tree will be at least partially displayed by means of a display device of a computer, preferably in graphic form.

24. A data processing system comprising data which represent information in at least one data stock which is accessible via at least one data source, characterized in that this system is adapted to carry out a method according to claim 1.

25. The data processing system according to claim 24, characterized by a graphic user interface for the input and/or reproduction of lists of words, links and/or at least one level of at least one category tree.

26. The data processing system according to claim 25, characterized in that the graphic user interface is adapted for the input, change and/or reproduction of data which represent information in at least one data stock.

27. The data processing system according to claim 24, characterized in that the reproduction will be at least partially realized in a selectable form.

28. The data processing system according to claim 24, characterized in that this one is a component of a database application or can be at least used together with a database application.

8. A method for the automatic creation/calculation of a category tree with respect to the contents of all texts of a data stock, characterized by the following process steps:

1. Creating sets of words having a pre-determinable number of significant words for each text of the data stock;

2. Storing the respective set of words in a relational database in form of a list of words, wherein the words are respectively assigned to an identifier of the respective set of words;

3. Creating a list of words from the base stored sets of words;

4. Selecting a first level of the category tree on the base of the words of the created list of words;

5. Retrieving co-occurrences for each word in the list of words within the sets of words stored in the database;

6. Storing the co-occurrences in a database in form of a list of words;

7. Retrieving another level of the category tree on the base of the words of the created list of words;

8. Retrieving co-occurrences for each word combination of the first and the at least one other level of the category tree with other words of the created list of words within the sets of words stored in the database;

9. Storing the co-occurrences in a database in form of a list of words;

10. Iteratively repeating the process steps 7 through 9 for at least one other level of the category tree until the number of the words retrieved in process step 8 for each combination of words of the first and the at least one other level of the category tree with other words of the list of words within the sets of words stored in the database is equal to zero.

9. The method according to claim 8, characterized in that the list of words created in process step 3 will be at least partially displayed by means of a display device of a computer, preferably in graphic form.

10. The method according to claim 8, characterized in that the list of words created in process step 3 will be sorted in a descending manner according to the frequency of the respective words.

11. The method according to claim 8, wherein in process step 5 during the retrieval of co-occurrences in the stored list of words, each word on the list of words will be compared one after the other to the words of each set of words.

12. The method according to claim 8, characterized in that the list of words stored in process step 6 will be at least partially displayed by a display device of a computer, preferably in graphic form.

13. The method according to claim 8, characterized in that the category tree will be consolidated for displaying by means of a display device, wherein preferably a similarity check will be realized.

14. The method according to claim 13, characterized in that within the scope of the similarity check, words having different word endings but the same word stem will be summarized in the shortest possible variant.

15. The method according to claim 13, characterized in that within the scope of the similarity check, two words having different lengths will be respectively compared to each other, in that the longer word will be shortened by two letters, the shorter word will then be brought to the length of the other word and both words will then be checked on a concordance.

16. The method according to claim 8, characterized in that during the retrieve of co-occurrences in process step 5 and/or process step 8 a similarity check will be realized, wherein words having different word endings but the same word stem will be summarized in the shortest possible variant.

17. The method according to claim 16, characterized in that within the scope of the similarity check two words having different lengths will be respectively compared to each other, in that the longer word will be shortened by two letters, the shorter word will then be brought to the length of the other word and both words will then be checked on a concordance.

18. The method according to claim 8, characterized in that the pre-determinable number in process step 1 is limited to up to 32.

29. A data processing device for the electronic processing of data, comprising a control and/or computer unit, an input unit and an output unit, characterized in that the device is adapted to carry out a method according to claim 8.

31. The data processing device according to claim 29, characterized in that it is a mobile terminal, preferably a mobile terminal which can be used or operated in mobile networks, most preferably a mobile phone.

19. A method for the automatic creation of a category tree with respect to the contents of all texts of a data stock, characterized by the following process steps:

1. Creating sets of words having a pre-determinable number of significant words for each text of the data stock;

2. Storing the respective set of words in a relational database in form of a list of words, wherein the words are respectively assigned to an identifier of the respective set of words;

3. Creating a list of words on the base of the sets of words;

4. Selecting a first level of the category tree on the base of the words of the created list of words;

5. Comparing each word on the list of words to each word within the sets of words stored in the database, wherein it is checked whether two words match and/or achieve a certain minimum similarity with respect to each other, and wherein in case of a match and/or given minimum similarity between the one word and all other words of the sets of words a weighted link having the weight 0.1 will be created, wherein the weight of the link will be increased by 0.1 if the link already exists and wherein if a weight of 1.0 is exceeded, the weight will be reset to 0.9 and all other links will be reduced to a value of 90%;

6. Retrieving the links of each word on the created list of words;

7. Storing the links in a list of words;

8. Selecting another level of the category tree on the base of the retrieved links and/or stored list of words;

9. Retrieving the links of each word on the created list of words and at least one stored list of words;

10. Storing the links in a list of words;

11. Iteratively repeating the process steps 8 through 10 for at least one other level of the category tree until the number of the links retrieved in process step 9 is equal to zero.

20. The method according to claim 19, characterized in that the list of words created in process step 3 will be at least partially displayed by means of a display device of a computer, preferably in graphic form.

21. The method according to claim 19, characterized in that the category tree will be consolidated for displaying by means of a display device, wherein preferably a similarity check will be realized.

22. The method according to claim 21, characterized in that within the scope of the similarity check, words having different word endings but the same word stem will be summarized in the shortest possible variant.

23. The method according to claim 21, characterized in that within the scope of the similarity check, two words having different lengths will be respectively compared to each other, in that the longer word will be shortened by two letters, the shorter word will then be brought to the length of the other word and both words will then be checked on a concordance.

30. A data processing device for the electronic processing of data, comprising a control and/or computer unit, an input unit and an output unit, characterized in that the device is adapted to carry out a method according to claim 19.