ENTITY RELATION MINING APPARATUS AND METHOD

• US 20090112825A1
• Filed: 10/30/2008
• Published: 04/30/2009
• Est. Priority Date: 10/31/2007
• Status: Abandoned Application

• Current Assignee: NEC Company Limited
• Original Assignee: NEC Company Limited
• Inventors: XU, Liqin, FUKUSHIMA, Toshikazu, HU, Changjian
• Application Number:
  US12/261,852
• Publication Number:
  US 20090112825A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/36 Creation of semantic tools, e.g. ontology or thesauri

Abstract

The present invention provides a relation mining apparatus and method for mining data for time-series relations and events among texts in various forms such as news, blogs, industrial reports and technical papers which may refer to various relations. According to the present invention, it is possible to automatically extract entity relation instances from a large amount of the texts as described above originating from the Internet or other mediums, mine for time-series entity relations, relation scores and entity importances in various categories based on the extracted instances, and finally extract important events therefrom. Also, according to the present invention, it is possible to perform calculating on the above extracted time-series relations for the corporation entities and business relations, so as to achieve an analysis on Five Forces. Further, it is also possible to present the result to final users by a visualizing module.

Claims

1. An entity relation mining apparatus, comprising:

a time-series entity relation extracting means for reading entity relation instances to generate time-series scored entity relations.

2. The entity relation mining apparatus according to claim 1, wherein the time-series entity relation extracting means further generates time-series comprehensive entity relation scores based on the generated time-series scored entity relations.

3. The entity relation mining apparatus according to claim 2, further comprising:

a time-series entity importance extracting means for reading the time-series comprehensive entity relation scores generated by the time-series entity relation extracting means to generate time-series entity importances.

4. The entity relation mining apparatus according to claim 2, further comprising:

an event detecting means for reading the time-series entity relations and the time-series comprehensive entity relation scores generated by the time-series entity relation extracting means to generate events.

5. The entity relation mining apparatus according to claim 3, further comprising:

an event detecting means for reading the time-series entity relations, the time-series comprehensive entity relation scores, and the time-series entity importances generated by the time-series entity relation extracting means and the time-series entity importance extracting means respectively to generate events.

6. The entity relation mining apparatus according to claim 1, further comprising:

a relation instance extracting means for reading text information data to generate the entity relation instances.

7. The entity relation mining apparatus according to claim 1, wherein the time-series entity relation extracting means comprises:

a time-series interpolating unit for calculating a score of an entity relation by interpolation for the entity relation within a prescribed time duration during which no entity relation occurs so that finally any one of continuous relations between any entities within the prescribed time duration has its score at any time point.

8. The entity relation mining apparatus according to claim 7, wherein the time-series entity relation extracting means further comprises at least one of:

an entity relation instance strength calculating unit for calculating a strength of an entity relation within a corresponding time unit, i.e., a score of the entity relation, according to each entity relation instance; and

an event-like relation and conflict processing unit for processing event-like relations to obtain the time-series scored entity relations.

9. The entity relation mining apparatus according to claim 7, wherein for a time duration between two adjacent time points where the entity relations occur, the time-series interpolating unit performs the interpolation on the scores of the entity relation in a manner that the scores linearly or exponentially attenuate or increase over time.

10. The entity relation mining apparatus according to claim 3, wherein the time-series entity importance extracting means comprises:

a graph creating unit for creating an undirected graph for entities within each time unit, wherein in the undirected graph, vertices are respective entities, and edges connecting the vertices have respective weights which are the comprehensive entity relation scores between the two entities; and

a graph node importance calculating unit for calculating an importance for each node, that is, the entity importance, using a graph node importance calculating method.

11. The entity relation mining apparatus according to claim 10, wherein the graph node importance calculating method is a Page Rank method or a HITS algorithm.

12. The entity relation mining apparatus according to claim 3, wherein the time-series entity importance extracting means comprises:

a graph creating unit for creating an undirected graph for entities within each time unit, wherein in the undirected graph, vertices are respective entities, and edges connecting the vertices have respective weights which are the comprehensive entity relation scores between the two entities; and

a graph node connectivity calculating unit for calculating an importance for each node, that is, the entity importance, using a graph node connectivity calculating method.

13. The entity relation mining apparatus according to claim 12, wherein the graph node connectivity calculating method is:

calculating a sum of the number of the connections to each node or a sum of the weights of the connections to each node.

14. The entity relation mining apparatus according to claim 4, wherein the event detecting means comprises:

a rule-based event extracting unit, which detects all inputted data by using predefined rules related to the time-series entity relations and the time-series comprehensive entity relation scores, and outputs the events matching the predefined rules.

15. The entity relation mining apparatus according to claim 4, wherein the event detecting means comprises:

an entity exterior score calculating unit, which performs score calculations on auxiliary information to obtain exterior scores for the entities; and

a rule-based event extracting unit, which detects all inputted data by using predefined rules related to the time-series entity relations, the time-series comprehensive entity relation scores and the exterior scores for the entities, and outputs the events matching the predefined rules.

16. The entity relation mining apparatus according to claim 5, wherein the event detecting means comprises:

a rule-based event extracting unit, which detects all inputted data by using predefined rules related to the time-series entity relations, the time-series comprehensive entity relation scores and the time-series entity importances, and outputs the events matching the predefined rules.

17. The entity relation mining apparatus according to claim 5, wherein the event detecting means comprises:

an entity exterior score calculating unit, which performs score calculations on auxiliary information to obtain exterior scores for the entities; and

a rule-based event extracting unit, which detects all inputted data by using predefined rules related to the time-series entity relations, the time-series comprehensive entity relation scores, the time-series entity importances and the exterior scores for the entities, and outputs the events matching the predefined rules.

18. The entity relation mining apparatus according to claim 16, wherein for an acquisition event, the rule-based event extracting unit determines whether a full acquisition or a partial acquisition between two entities occurs based on the entity importances of the two entities upon acquisition and/or changes in the entity importances of the two entities after acquisition.

19. The entity relation mining apparatus according to claim 1, wherein the entities are corporations, and the relations are business relations.

20. The entity relation mining apparatus according to claim 19, further comprising:

a time-series Five Force analyzing means for generating time-series force data based on the time-series entity relations and the time-series entity importances.

21. The entity relation mining apparatus according to claim 20, wherein the time-series Five Force analyzing means comprises:

a trade dividing unit for dividing the inputted time-series entity relations and the time-series entity importances based on the required trades to output the time-series entity relations and the importances for individual trades; and

at least one of

a threat of entry analyzing unit for calculating the threat of entry at a given time t₀;

a power of supplier analyzing unit for calculating the power of supplier at the given time t₀;

a power of buyer analyzing unit for calculating the power of buyer at the given time t₀;

a competitive rivalry analyzing unit for calculating the competitive rivalry at the given time t₀; and

a threat of substitute analyzing unit for calculating the threat of substitute at the given time t₀.

22. The entity relation mining apparatus according to claim 21, wherein the threat of substitute analyzing unit obtains future potential all-round competitors by analyzing future competition trends, instead of calculating the threat of substitute at the given time t₀.

23. The entity relation mining apparatus according to claim 1, wherein the entities are products, persons or nations, and the relations are relations between products, persons or nations.

24. The entity relation mining apparatus according to claim 1, further comprising:

a visualizing means for generating a visualized interface based on at least one of the inputted time-series entity relations, the time-series comprehensive entity relation scores, the time-series entity importances, and the time-series force data.

25. The entity relation mining apparatus according to claim 24, wherein the visualizing means generates the visualized interface with nodes and connecting lines, wherein

each node represents an entity, and the connecting lines between the nodes represent the types and scores of the entity relations, wherein the sizes of the nodes correspond to the importances of the entities, the width or length parameters of the connecting lines correspond to the scores of the entity relations, and the colors of the connecting lines correspond to the types of the entity relations.

26. The entity relation mining apparatus according to claim 24, wherein the visualizing means generates the visualized interface with nodes and connecting lines, wherein

the starts of the relations are used as the nodes, the connecting lines are categorized into entity reference lines and event-start-associated lines, wherein the colors of the event-start-associated lines correspond to the types of the entity relations.

27. An entity relation mining method, comprising:

a time-series entity relation extracting step of reading entity relation instances to generate time-series scored entity relations.

28. The entity relation mining method according to claim 27, wherein in the time-series entity relation extracting step, time-series comprehensive entity relation scores are further generated based on the generated time-series scored entity relations.

29. The entity relation mining method according to claim 28, further comprising:

a time-series entity importance extracting step of reading the time-series comprehensive entity relation scores generated in the time-series entity relation extracting step to generate time-series entity importances.

30. The entity relation mining method according to claim 28, further comprising:

an event detecting step of reading the time-series entity relations and the time-series comprehensive entity relation scores generated in the time-series entity relation extracting step to generate events.

31. The entity relation mining method according to claim 29, further comprising:

an event detecting step of reading the time-series entity relations, the time-series comprehensive entity relation scores, and the time-series entity importances generated in the time-series entity relation extracting step and the time-series entity importance extracting step respectively to generate events.

32. The entity relation mining method according to claim 27, further comprising:

a relation instance extracting step of reading text information data to generate the entity relation instances.

33. The entity relation mining method according to claim 27, wherein the time-series entity relation extracting step comprises:

a time-series interpolating sub-step of calculating a score of an entity relation by interpolation for the entity relation within a prescribed time duration during which no entity relation occurs so that finally any one of continuous relations between any entities within the prescribed time duration has its score at any time point.

34. The entity relation mining method according to claim 33, wherein the time-series entity relation extracting step further comprises at least one of:

an entity relation instance strength calculating sub-step of calculating a strength of an entity relation within a corresponding time unit, i.e., a score of the entity relation, according to each entity relation instance; and

an event-like relation and conflict processing sub-step of processing event-like relations to obtain the time-series scored entity relations.

35. The entity relation mining method according to claim 33, wherein in the time-series interpolating sub-step, for a time duration between two adjacent time points where the entity relations occur, the interpolation on the scores of the entity relation is performed in a manner that the scores linearly or exponentially attenuate or increase over time.

36. The entity relation mining method according to claim 29, wherein the time-series entity importance extracting step comprises:

a graph creating sub-step of creating an undirected graph for entities within each time unit, wherein in the undirected graph, vertices are respective entities, and edges connecting the vertices have respective weights which are the comprehensive entity relation scores between the two entities; and

a graph node importance calculating sub-step of calculating an importance for each node, that is, the entity importance, using a graph node importance calculating method.

37. The entity relation mining method according to claim 36, wherein the graph node importance calculating method is a Page Rank method or a HITS algorithm.

38. The entity relation mining method according to claim 29, wherein the time-series entity importance extracting step comprises:

a graph creating sub-step of creating an undirected graph for entities within each time unit, wherein in the undirected graph, vertices are respective entities, and edges connecting the vertices have respective weights which are the comprehensive entity relation scores between the two entities; and

a graph node connectivity calculating sub-step of calculating an importance for each node, that is, the entity importance, using a graph node connectivity calculating method.

39. The entity relation mining method according to claim 38, wherein the graph node connectivity calculating method is:

calculating a sum of the number of the connections to each node or a sum of the weights of the connections to each node.

40. The entity relation mining method according to claim 30, wherein the event detecting step comprises:

a rule-based event extracting sub-step of detecting all inputted data by using predefined rules related to the time-series entity relations and the time-series comprehensive entity relation scores, and outputting the events matching the predefined rules.

41. The entity relation mining method according to claim 30, wherein the event detecting step comprises:

an entity exterior score calculating sub-step of performing score calculations on auxiliary information to obtain exterior scores for the entities; and

a rule-based event extracting sub-step of detecting all inputted data by using predefined rules related to the time-series entity relations, the time-series comprehensive entity relation scores and the exterior scores for the entities, and outputting the events matching the predefined rules.

42. The entity relation mining method according to claim 31, wherein the event detecting step comprises:

a rule-based event extracting sub-step of detecting all inputted data by using predefined rules related to the time-series entity relations, the time-series comprehensive entity relation scores and the time-series entity importances, and outputting the events matching the predefined rules.

43. The entity relation mining method according to claim 31, wherein the event detecting step comprises:

an entity exterior score calculating sub-step of performing score calculations on auxiliary information to obtain exterior scores for the entities; and

a rule-based event extracting sub-step of detecting all inputted data by using predefined rules related to the time-series entity relations, the time-series comprehensive entity relation scores, the time-series entity importances and the exterior scores for the entities, and outputting the events matching the predefined rules.

44. The entity relation mining method according to claim 42, wherein in the rule-based event extracting sub-step, for an acquisition event, it is determined whether a full acquisition or a partial acquisition between two entities occurs based on the entity importances of the two entities upon acquisition and/or changes in the entity importances of the two entities after acquisition.

45. The entity relation mining method according to claim 27, wherein the entities are corporations, and the relations are business relations.

46. The entity relation mining method according to claim 45, further comprising:

a time-series Five Force analyzing step of generating time-series force data based on the time-series entity relations and the time-series entity importances.

47. The entity relation mining method according to claim 46, wherein the time-series Five Force analyzing step comprises:

a trade dividing sub-step of dividing the inputted time-series entity relations and the time-series entity importances based on the required trades to output the time-series entity relations and the importances for individual trades; and

at least one of

a threat of entry analyzing sub-step of calculating the threat of entry at a given time t₀;

a power of supplier analyzing sub-step of calculating the power of supplier at the given time t₀;

a power of buyer analyzing sub-step of calculating the power of buyer at the given time t₀;

a competitive rivalry analyzing sub-step of calculating the competitive rivalry at the given time t₀; and

a threat of substitute analyzing sub-step of calculating the threat of substitute at the given time t₀.

48. The entity relation mining method according to claim 47, wherein in the threat of substitute analyzing sub-step, future potential all-round competitors are obtained by analyzing future competition trends, instead of calculating the threat of substitute at the given time t₀.

49. The entity relation mining method according to claim 27, wherein the entities are products, persons or nations, and the relations are relations between products, persons or nations.

50. The entity relation mining method according to claim 27, further comprising:

a visualizing step of generating a visualized interface based on at least one of the inputted time-series entity relations, the time-series comprehensive entity relation scores, the time-series entity importances, and the time-series force data.

51. The entity relation mining method according to claim 50, wherein in the visualizing step, the visualized interface is generated with nodes and connecting lines, wherein

each node represents an entity, and the connecting lines between the nodes represent the types and scores of the entity relations, wherein the sizes of the nodes correspond to the importances of the entities, the width or length parameters of the connecting lines correspond to the scores of the entity relations, and the colors of the connecting lines correspond to the types of the entity relations.

52. The entity relation mining method according to claim 50, wherein in the visualizing step, the visualized interface is generated with nodes and connecting lines, wherein

the starts of the relations are used as the nodes, the connecting lines are categorized into entity reference lines and event-start-associated lines, wherein the colors of the event-start-associated lines correspond to the types of the entity relations.