METHOD AND SYSTEM FOR CALCULATING COMPETITIVENESS METRIC BETWEEN OBJECTS

• US 20090077126A1
• Filed: 09/18/2008
• Published: 03/19/2009
• Est. Priority Date: 09/19/2007
• Status: Abandoned Application

• Current Assignee: NEC China Co Ltd (NEC Corporation)
• Original Assignee: NEC China Co Ltd (NEC Corporation)
• Inventors: LI, Jianqiang, FUKUSHIMA, Toshikazu, ZHAO, Yu
• Application Number:
  US12/233,335
• Publication Number:
  US 20090077126A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

Method and System for calculating competitiveness metric between objects are provided. The method comprises the steps of: obtaining a first object and a second object, the first and second objects having a first profile and a second profile, each composed of a plurality of attributes, respectively; normalizing the first profile and the second profile with reference to ontology information; and calculating, based on the normalized first and second profiles, a competitiveness metric between the first and second objects. In one embodiment, the ontology information is a common attribute name vocabulary, and the step of normalizing is configured for adjusting the structures of the first and second profiles to a unified profile structure, computing the sub-metrics between the corresponding attributes in the unified profile, and computing the weighed sum of the sub-metrics as the final competitiveness metric of the first and second objects. In another embodiment, the ontology information is an object category tree, and the step of normalizing is configured for mapping the first and second profiles to one or more nodes in the object category tree, computing the probabilities of mapping the profiles to different nodes, and then, based on the obtained semantic distances between the nodes, computing the final competitiveness metric according the probabilities and the semantic distances.

Claims

1. A method for calculating competitiveness metric between objects, comprising:

obtaining a first object and a second object, the first and second objects having a first profile and a second profile, each composed of a plurality of attributes, respectively;

normalizing the first profile and the second profile with reference to ontology information; and

calculating, based on the normalized first and second profiles, a competitiveness metric between the first and second objects.

2. The method according to claim 1, wherein the ontology information is a common attribute name vocabulary, which includes the names of object'"'"'s attributes selected by importance for the competitiveness of the attributes, and

wherein normalizing the first and second profiles comprises:

determining profile types of the first and second profiles;

according to the determined profile types, generating a unified profile structure by referring to the common attribute name vocabulary; and

aligning the respective attributes in the first and second profiles with the corresponding attributes in the unified profile, and

wherein calculating the competitiveness metric comprises;

calculating a competitiveness sub-metric for each pair of corresponding attributes in the aligned first and second profiles; and

obtaining the competitiveness metric between the first and second objects by calculating the weighted sum of the competitiveness sub-metrics of all attributes in the first and second profiles.

3. The method according to claim 1, wherein the ontology information is an object category tree, of which each node represents an object category and includes one or more representative profiles, and

wherein normalizing the first and second profiles comprises:

mapping each of the first and second profiles to one or more nodes of the object category tree, and

wherein calculating the competitiveness metric comprises;

obtaining a semantic distance between each pair of nodes of the object category tree; and

calculating the competitiveness metric between the first and second objects based on the obtained semantic distances.

4. The method according to claim 3, wherein calculating the competitiveness metric further comprises calculating, for each of the first and second profiles, a probability of mapping it to each of corresponding nodes of the object category tree, and wherein the competitiveness metric between the first and second objects based on is calculated based on the calculated mapping probabilities of the first and second profiles and the obtained semantic distances between the nodes to which the first and second profiles are mapped.

5. The method according to claim 2, wherein calculating the competitiveness sub-metric comprises:

with respect to each pair of corresponding attributes in the first and second profiles, namely, a first attributes from the first profile and a second attribute from the second profile;

determining the type of the first and second attributes with reference to the common attribute name vocabulary;

selecting a competitiveness sub-metric measure according to the determined attribute type;

calculating the competitiveness sub-metric between the first and second attribute with the selected competitiveness sub-metric measure.

6. The method according to claim 5, wherein the competitiveness sub-metric measure is a Vector Space Model (VSM)-based measure or an attribute-based measure.

7. The method according to claim 6, wherein when the VSM-based measure is used to calculate the competitiveness sub-metric, the step of calculating the competitiveness sub-metric further comprises:

generating a first vector and a second vector, which are both based on words, representative of the first and second attributes respectively;

using the VSM-based measure to calculate a competitiveness metric between the first and second vectors as the competitiveness sub-metric between the first and second attributes.

8. The method according to claim 7, further comprising:

preprocessing the first and second attributes to delete named entities from text of each attribute'"'"'s value before generating the first and second vectors.

9. The method according to claim 8, wherein the named entities include proper none, company name and product name.

10. The method according to claim 7, further comprising:

performing a domain and part-of-speech (POS) analysis on the words in the first and second attributes; and

before generating the first and second vectors, according to the result of the domain and POS analysis, weighting the words in the first and second attributes with reference to a previously stored competitiveness weight coefficients rules table related to the competitiveness.

11. The method according to claim 7, wherein the competitiveness weight coefficients rules table is built manually by user.

12. The method according to claim 7, wherein the competitiveness weight coefficients rules table is built through an automatic way by performing keywords extraction based on the ontological object information from third party websites.

13. The method according to claim 7, wherein the competitiveness weight coefficients rules table is configured for storing a competitiveness weight coefficient associated with each word, which represents the importance of the word in calculating the competitiveness metric.

14. The method according to claim 13, wherein in the competitiveness weight coefficients rules table, a word un-related to the domain to which the compared first and second objects belong is provided a lower competitiveness weight coefficient than a word related to the domain, and for the words which each has a POS making no contribution to the calculation of the competitiveness metric, their competitiveness weight coefficients are set to zero.

15. The method according to claim 3, wherein the one or more representative profiles at each node correspond to different languages.

16. The method according to claim 3, wherein the one or more representative profiles at each node of the object category tree are used as a medium to perform the mapping of the first and second profiles to the nodes of the object category tree by using a VSM-based measure.

17. The method according to claim 3, wherein when each of the first and second profiles is mapped to a single node, the semantic distance between the mapped nodes is used directly as the competitiveness metric between the first and second objects.

18. The method according to claim 4, wherein when each of the first and second profiles is mapped to a plurality of nodes, a first category vector and a second category vector are generated based on the probabilities of mapping the first and second profiles to the respective nodes of the object category tree, and the competitiveness metric between the first and second objects is calculated by utilizing a cosine measure of the first and second category vectors.

19. The method according to claim 18, wherein the semantic distances between the nodes that the first and second profiles are mapped to are integrated into the cosine measure to calculate the competitiveness metric between the first and second objects.

20. The method according to claim 3, wherein the semantic distances between respective nodes of the object category tree are computed in advance and stored with the object category tree.

21. The method according to claim 3, wherein on the object category tree, a semantic distance between nodes in a higher level is bigger than that between nodes in a lower level, and a semantic distance between “

sibling”

nodes is bigger than that between a “

parent”

node and a “

child”

node.

22. A system for calculating competitiveness metric between objects, comprising:

an object obtaining means for obtaining a first object and a second object, the first and second objects having a first profile and a second profile, each composed of a plurality of attributes, respectively;

a ontology information base for storing ontology information;

a normalizing means for normalizing the first profile and the second profile using the ontology information from the ontology information base; and

a competitiveness metric calculator for calculating, based on the normalized first and second profiles, a competitiveness metric between the first and second objects.

23. The system according to claim 22, wherein the ontology information is a common attribute name vocabulary, which includes the names of object'"'"'s attributes selected by importance for the competitiveness of the attributes, and

wherein the normalizing means further comprises:

a determining unit for determining profile types of the first and second profiles;

a unified profile structure generation unit for generating, according to the determined profile types, a unified profile structure by referring to the common attribute name vocabulary; and

an alignment unit for aligning the respective attributes in the first and second profiles with the corresponding attributes in the unified profile,

wherein the competitiveness metric calculator further comprises;

a competitiveness sub-metric calculating unit for calculating a competitiveness sub-metric for each pair of corresponding attributes in the aligned first and second profiles; and

a competitiveness metric calculating unit for obtaining the competitiveness metric between the first and second objects by calculating the weighted sum of the competitiveness sub-metrics of all attributes in the first and second profiles,

wherein the system further comprises a competitiveness weighting policies base for storing weight coefficients required for the weighting.

24. The system according to claim 22, wherein the ontology information is an object category tree, of which each node represents an object category and includes one or more representative profiles, and

wherein the normalizing means further comprises:

a mapping unit for mapping each of the first and second profiles to one or more nodes of the object category tree, and

wherein the competitiveness metric calculator comprises;

a semantic distance obtaining unit for obtaining a semantic distance between each pair of nodes of the object category tree; and

a competitiveness metric calculating unit for calculating the competitiveness metric between the first and second objects based on the obtained semantic distances.

25. The system according to claim 24, wherein the competitiveness metric calculator further comprises:

a mapping probability calculating unit for calculating, for each of the first and second profiles, a probability of mapping it to each of corresponding nodes of the object category tree, and

wherein the competitiveness metric calculating unit is configured for calculating the competitiveness metric between the first and second objects based on the calculated mapping probabilities of the first and second profiles and the obtained semantic distances between the nodes to which the first and second profiles are mapped.

26. The system according to claim 23, wherein the competitiveness sub-metric calculating unit further comprises:

a attribute type determining unit for determining the type of a first and a second attributes with reference to the common attribute name vocabulary, the first and a second attributes being a pair of corresponding attributes in the first and second profiles and from the first and second profiles respectively;

a sub-metric measure selector for selecting a competitiveness sub-metric measure according to the determined attribute type; and

a sub-metric calculator for calculating the competitiveness sub-metric between the first and second attribute with the selected competitiveness sub-metric measure.

27. The system according to claim 26, wherein the sub-metric calculator uses a Vector Space Model (VSM)-based measure or an attribute-based measure to calculate the competitiveness sub-metric.

28. The system according to claim 27, wherein when the VSM-based measure is used to calculate the competitiveness sub-metric, the sub-metric calculator further comprises:

a vectoring unit for generating a first vector and a second vector, which are both based on words, representative of the first and second attributes respectively; and

a VSM-based sub-metric calculator for using the VSM-based measure to calculate a competitiveness metric between the first and second vectors as the competitiveness sub-metric between the first and second attributes.

29. The system according to claim 28, wherein the sub-metric calculator further comprises:

a preprocessing unit coupled to the vectoring unit for preprocessing the first and second attributes to delete named entities from text of each attribute'"'"'s value before generating the first and second vectors.

30. The system according to claim 29, wherein the named entities include proper none, company name and product name.

31. The system according to claim 28, wherein the sub-metric calculator further comprises:

a domain and POS analysis module for performing a domain and POS analysis on the words in the first and second attributes, and

wherein the vectoring unit is configured for before generating the first and second vectors, according to the result of the domain and POS analysis, weighting the words in the first and second attributes with reference to a previously stored competitiveness weight coefficients rules table related to the competitiveness.

32. The system according to claim 31, wherein the competitiveness weight coefficients rules table is stored in the competitiveness weighting policies base.

33. The system according to claim 31, wherein the competitiveness weight coefficients rules table is built manually by user.

34. The system according to claim 31, wherein the competitiveness weight coefficients rules table is built through an automatic way by performing keywords extraction based on the ontological object information from third party websites.

35. The system according to claim 31, wherein the competitiveness weight coefficients rules table is configured for storing a competitiveness weight coefficient associated with each word, which represents the importance of the word in calculating the competitiveness metric.

36. The method according to claim 35, wherein in the competitiveness weight coefficients rules table, a word un-related to the domain to which the compared first and second objects belong is provided a lower competitiveness weight coefficient than a word related to the domain, and for the words which each has a POS making no contribution to the calculation of the competitiveness metric, their competitiveness weight coefficients are set to zero.

37. The system according to claim 24, wherein the one or more representative profiles at each node correspond to different languages.

38. The system according to claim 24, wherein the mapping unit is configured for using the one or more representative profiles at each node of the object category tree as a medium to perform the mapping of the first and second profiles to the nodes of the object category tree by using a VSM-based measure.

39. The system according to claim 24, wherein when each of the first and second profiles is mapped to a single node, the competitiveness metric calculating unit is configured for using the semantic distance between the mapped nodes directly as the competitiveness metric between the first and second objects.

40. The system according to claim 25, wherein when each of the first and second profiles is mapped to a plurality of nodes, the competitiveness metric calculating unit is configured for generating a first category vector and a second category vector based on the probabilities of mapping the first and second profiles to the respective nodes of the object category tree, and calculating the competitiveness metric between the first and second objects by utilizing a cosine measure of the first and second category vectors.

41. The system according to claim 40, wherein the semantic distances between the nodes that the first and second profiles are mapped to are integrated into the cosine measure to calculate the competitiveness metric between the first and second objects.

42. The system according to claim 24, wherein the semantic distances between respective nodes of the object category tree are computed in advance and stored with the object category tree in the ontology information base.

43. The system according to claim 24, wherein on the object category tree, a semantic distance between nodes in a higher level is bigger than that between nodes in a lower level, and a semantic distance between “

sibling”

nodes is bigger than that between a “

parent”

node and a “

child”

node.