Method and system of classifying, ranking and relating information based on networks

• US 20080249966A1
• Filed: 03/25/2008
• Published: 10/09/2008
• Est. Priority Date: 04/03/2007
• Status: Active Grant

• Current Assignee: Ondore, Inc.
• Original Assignee: Fernando Luege Mateos
• Inventors: Luege Mateos, Fernando
• Granted Patent: US 8,156,056 B2
• Time in Patent Office: Days
• US Class Current: 706/11
• CPC Class Codes: G06F 16/3347 using vector based model
• CPC Class Codes: G06F 16/337 Profile generation, learning or modification

Abstract

What is disclosed is an automatic method of classification, qualification and relation of information that is based on a network built starting from the semantic, numeric, conceptual, etc. relationships between elements of different sets that are structurally alike. Through the topological properties of the network, we generate a vector of relations for each element that allows us to analyze the similarity among them, improve and facilitate the clustering processes, carry out personalized dynamic ranking, analyze conduct patterns as well as maintain the auto evolving dynamic of the network through the interaction and feedback of its elements, among other characteristics.

Claims

1. A computer-implemented method, comprising:

identifying a first data set and a second data set,

establishing a plurality of relationships, each of the plurality of relationships linking an element of the first data set to an element of the second data set,

assigning a link weight to each of the plurality of relationships, said link weight being modifiable by interaction between the elements linked by the each relationship,

computing a similarity quotient for the each relationship of the plurality of relationships, the similarity quotient being a measure of similarity between the elements linked by the each relationship, and

classifying the plurality of relationships based on values of the similarity quotients.

2. The method of claim 1, further comprising:

ranking the plurality of relationships based on values of the similarity quotients.

3. The method of claim 1, further comprising:

applying a clustering operation to the plurality of relationships to generate clusters of similarity quotients,

wherein the relationships are classified based on the clusters of similarity quotients.

4. The method of claim 3, wherein the plurality of relationships form a network of interconnected elements of the first and second data sets, the network including indirect links between elements of the same data set and between elements of different data sets;

and wherein the clustering operation is applied to a section of the network.

5. The method of claim 1, further comprising:

identifying a set of categories for categorizing elements of the first and second data sets,

initializing a plurality of first ranking vectors, each of the first ranking vectors corresponding to an element of the first data set, each component of the each first ranking vector representing a degree to which the element is related to one of the categories,

initializing a plurality of second ranking vectors, each of the second ranking vectors corresponding to an element of the second data set, each component of the each second ranking vector representing a degree to which the element is related to one of the categories,

wherein the similarity quotient computed for the each relationship is computed from the first ranking vector u of the element of the first data set and the second ranking vector a of the element of the second data set.

6. The method of claim 5, wherein components of the first and second ranking vectors are obtained using statistical information about the first and second data sets.

7. The method of claim 5, wherein the similarity quotient is computed from a formula:

$1-\frac{1}{2}<br><br>\underset{j=0}{\overset{M-1}{\sum <br><br>}}<br><br><br><br>a^j-u^j<br><br>$

wherein a^j and u^j are components of the first and second ranking vectors a and u, respectively, and the first and second ranking vectors a and u are M-dimensional and normalized.

8. The method of claim 5, wherein each interaction between any pair of elements modifies at least one of the ranking vectors corresponding to the pair of interacting elements, the pair of interacting elements being 1) any two elements of the first data set, 2) any two elements of the second data set, or 3) an element of the first data set and an element of the second data set.

9. The method of claim 8, wherein the ranking vectors are ranking vectors a and u, and each component a^j of the at least one ranking vector a is incremented by an amount proportional to a difference between components a^j and u^j of the ranking vectors a and u, the amount being computed from κ

*(u_j−

a^j), κ

being an interaction coefficient.

10. The method of claim 9, further comprising:

keeping a record of the at least one ranking vector a after each interaction of the interactions,

generating a trajectory of the at least one ranking vector a as a function of the interactions, and

predicting values of components of the at least one ranking vector a by extrapolating the trajectory to extrapolate the at least one ranking vector a and evaluating the extrapolated at least one ranking vector a.

11. The method of claim 9, wherein the interaction coefficient varies during an interaction.

12. The method of claim 5, further comprising:

assigning each element of the first data set to represent a person in a community,

associating an interest profile of each person in the community with the first ranking vector of the each person, and

wherein persons in the community are classified based on values of the similarity quotients computed from the interest profiles and the second ranking vectors.

13. The method of claim 12, further comprising:

associating a search query to an element of the first data set,

associating the second data set to a set of documents returned from a search using the search query, and

classifying the set of documents based on values of the similarity quotients.

14. The method of claim 13, further comprising:

ranking the classified set of documents based on values of the similarity quotients.

15. The method of claim 1, further comprising:

representing the plurality of relationships in a graphical form, the graphical form including links between elements of the first and second data sets and weights assigned to each of the plurality of relationships, and

displaying the graphical form on a graphical user interface.

16. The method of claim 15, further comprising:

providing a graphical navigation tool, the graphical navigation tool allowing a user to access elements of the first and second data sets represented in the graphical form.

17. The method of claim 1, further comprising:

representing the plurality of relationships in a textual form, the textual form including links between elements of the first and second data sets and link weights assigned to each of the plurality of relationships, and

displaying the textual form on a user interface.

18. The method of claim 17, further comprising:

providing a navigation tool, said navigation tool allowing a user to access elements of the first and second data sets represented in the textual form.

25. A computer-readable medium having computer-readable instructions stored thereon, the computer-readable instructions, when executed by a computer, causing the computer to perform the computer-implemented method of claim 1.

26. The computer-readable medium of claim 25, the computer-implemented method further comprising:

ranking the plurality of relationships based on values of the similarity quotients.

27. The computer-readable medium of claim 25, the computer-implemented method further comprising:

applying a clustering operation to the plurality of relationships to generate clusters of similarity quotients,

wherein the relationships are classified based on the clusters of similarity quotients.

19. A computer-implemented method of modeling interest profiles of users, comprising:

generating an interest profile of a user,

identifying a data set, at least a subset of elements of the data set being categorized,

associating a ranking vector with a categorized element of the data set for all elements of the data set to generate a plurality of ranking vectors, each component of the ranking vector being proportional to a degree of the categorized element that is related to a category,

computing a similarity quotient for each ranking vector of the plurality of ranking vectors from the interest profile and each ranking vector,

modifying the interest profile upon an interaction of the user with the categorized element by an amount determined by the interaction, and

re-computing the similarity quotient for the categorized element from the modified interest profile.

20. The method of claim 19, wherein the amount is proportional to a difference between the interest profile and the ranking vector of the categorized element.

21. The method of claim 20, further comprising:

modifying the ranking vector by the amount determined by the interaction.

22. The method of claim 19, further comprising:

generating a second interest profile of a second user,

modifying the interest profile upon a second interaction of the user with the second user by a second amount determined by the second interaction.

23. The method of claim 19, further comprising:

keeping a record of the interest profile to generate a trajectory of the interest profile in a space, and

predicting the user'"'"'s behavior by extrapolating the trajectory to obtain an extrapolated interest profile and evaluating the extrapolated interest profile.

24. The method of claim 19, further comprising:

establishing a model of the interaction, and

determining the amount from the model.

28. A computer-readable medium having computer-readable instructions stored thereon, the computer-readable instructions, when executed by a computer, causing the computer to perform the computer-implemented method of claim 19.