Search Result Ranking and Presentation

• US 20140046934A1
• Filed: 08/08/2012
• Published: 02/13/2014
• Est. Priority Date: 08/08/2012
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Ding, Chen, Lou, JinYu, Zhou, Chen, Huynh, David Francois, Huang, Yanlai, Shen, Hongda, Li, Guanghua, Li, Yiming, Chai, Yangyang
• Granted Patent: US 9,390,174 B2
• Time in Patent Office: Days
• US Class Current: 707/723
• CPC Class Codes: G06F 16/24526 Internal representations for queries
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 16/9038 Presentation of query results
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques

Abstract

Methods and systems are provided for ranking search results and generating a presentation. In some implementations, a search system generates a presentation based on a search query. In some implementations, a search system ranks search results based on data stored in a knowledge graph. In some implementations, a search system identifies a modifying concept such as a superlative in a received search query, and determines ranking properties based on the modifying concept.

Claims

1. A computer implemented method for providing search results, the method comprising:

determining, using one or more processors, an entity reference from a search query;

identifying, using one or more processors, a ranked list of properties associated with a type of the entity reference based on a knowledge graph;

identifying, using one or more processors, a property for generating a presentation of search results from the ranked list of properties, based at least in part on the search query and on the type of the entity reference;

determining, using one or more processors, a presentation technique associated with the property for generating a presentation; and

causing to be presented, using one or more processors, search results based on the presentation technique.

2. The method of claim 1, the method further comprising causing to be presented, using one or more processors, search results in an arrangement according to user input.

3. The method of claim 2, wherein user input comprises a search query.

4. The method of claim 2, wherein user input comprises a selection of a presentation technique.

5. The method of claim 1, wherein determining an entity reference from a search query comprises determining an associated node in the knowledge graph.

6. The method of claim 1, wherein identifying a ranked list of properties comprises identifying a preprocessed schema table.

7. The method of claim 1, wherein the ranked list of properties is ranked based at least in part on popularity.

8. The method of claim 1, wherein the ranked list of properties is ranked based at least in part on co-occurrence.

9. The method of claim 1, wherein identifying a property for generating a presentation of search results comprises selecting a property from the ranked list of property based at least in part on the search query.

10. The method of claim 1, wherein the presentation technique is selected from the group consisting of gallery, timeline, map, list, chart, audio, and any combination thereof.

11. A computer implemented method for providing search results, the method comprising:

receiving a search query;

identifying, using one or more processors, a modifying concept in the search query;

generating, using one or more processors, a query tree based on the search query;

retrieving, using one or more processors, search results from a knowledge graph, based at least in part on the query tree;

determining, using one or more processors, at least one ranking property based at least on the modifying concept; and

ranking, using one or more processors, the search results based at least in part on the at least one ranking property.

12. The method of claim 11, wherein the at least one ranking property includes an ordering direction.

13. The method of claim 11, wherein generating the query tree further comprises:

generating a plurality of query trees based on the second portion of the search query;

determining a score for each query tree of the plurality of query trees; and

selecting a query tree from the plurality of query trees based at least in part on the score.

14. The method of claim 11, wherein the modifying concept is explicit in the search query.

15. The method of claim 11, wherein the modifying concept is implicit in the search query.

16. The method of claim 11, wherein the modifying concept is objective.

17. The method of claim 11, wherein the modifying concept is subjective.

18. A computer implemented method for search, the method comprising:

identifying, using one or more processors, a modifying concept based on a search query;

determining, using one or more processors, a rule for ranking search results based at least in part on the modifying concept and on a knowledge graph from which at least one of the search results was obtained; and

ranking, using one or more processors, search results based at least in part on the rule.

19. The method of claim 18, wherein determining the rule for ranking search results comprises:

identifying in the knowledge graph an entity type associated with the search query; and

retrieving data related to the modifying concept and the entity type, wherein the data comprises at least the rule.

20. The method of claim 18, wherein determining the rule for ranking search results comprises:

identifying, in the knowledge graph, a property associated with the search query; and

retrieving data related to the modifying concept and the property, wherein the data comprises at least the rule.

21. The method of claim 18, wherein identifying the modifying concept comprises identifying at least one desired ranking property from the search query.

22. The method of claim 18, wherein determining the rule for ranking search results comprises:

retrieving a quality score from the knowledge graph for each respective search result of the search results, wherein the quality is score is associated with the modifying concept; and

determining the rule for ranking search results based at least in part on the quality score.

23. The method of claim 18, wherein determining the rule for ranking search results comprises determining the frequency of occurrence of the modifying concept in each respective search result of the search results.

24. The method of claim 18, wherein determining the rule comprises:

identifying an entity reference in the knowledge graph based on the search query;

identifying two or more entity types associated with the entity reference;

determining a weight for each of the two or more entity types based at least in part on the relationship of the entity reference with each respective entity type; and

determining data based at least in part on the weight and the two or more entity types, wherein the data comprises at least the rule.

25. The method of claim 18, wherein determining the rule for ranking search results comprises:

identifying a first and second entity reference in a search query;

retrieving, from the knowledge graph, a set of search results based at least in part on the first entity reference;

determining a related entity score for each respective search result of the set of search results, wherein the related entity score is based at least in part on the co-occurrence of data related to the first entity reference and data related to the second entity reference in each respective search result; and

determining at least the rule based on the related entity score.

26. The method of claim 18, wherein the modifying concept comprises a range filter.

27. The method of claim 18, wherein the modifying concept is explicit.

28. The method of claim 18, wherein the modifying concept is implicit.

29. The method of claim 18, wherein the modifying concept is subjective.

30. The method of claim 18, wherein the modifying concept is objective.

31. A system for providing search results, the system comprising:

a database comprising a knowledge graph; and

one or more computers configured to perform operations comprising;

determining an entity reference from a search query;

identifying a ranked list of properties associated with a type of the entity reference based on the knowledge graph;

identifying a property for generating a presentation of search results from the ranked list of properties, based at least in part on the search query and on the type of the entity reference;

determining a presentation technique associated with the property for generating a presentation; and

causing to be presented search results based on the presentation technique.

32. The system of claim 31, wherein the one or more computers are further configured to cause search results to be presented in an arrangement according to user input.

33. The system of claim 32, wherein user input comprises a search query.

34. The system of claim 32, wherein user input comprises a selection of a presentation technique.

35. The system of claim 31, wherein determining an entity reference from a search query comprises determining an associated node in the knowledge graph.

36. The system of claim 31, wherein identifying a ranked list of properties comprises identifying a preprocessed schema table.

37. The system of claim 31, wherein the ranked list of properties is ranked based at least in part on popularity.

38. The system of claim 31, wherein the ranked list of properties is ranked based at least in part on co-occurrence.

39. The system of claim 31, wherein identifying a property for generating a presentation of search results comprises selecting a property from the ranked list of property based at least in part on the search query.

40. The system of claim 31, wherein the presentation technique is selected from the group consisting of gallery, timeline, map, list, chart, and any combination thereof.

41. A system for providing search results, the system comprising:

a database comprising a knowledge graph; and

one or more computers configured to perform operations comprising;

receiving a search query;

identifying a modifying concept in a search query;

generating a query tree based on the search query;

retrieving search results from the knowledge graph, based at least in part on the query tree;

determining at least one ranking property based at least on the modifying concept; and

ranking the search results based at least in part on the at least one ranking property.

42. The system of claim 41, wherein the at least one ranking property includes an ordering direction.

43. The system of claim 41, wherein the one or more computers are further configured to perform operations comprising:

generating a plurality of query trees based on the second portion of the search query;

determining a score for each query tree of the plurality of query trees; and

selecting a query tree from the plurality of query trees based at least in part on the score.

44. The system of claim 41, wherein the modifying concept is explicit in the search query.

45. The system of claim 41, wherein the modifying concept is implicit in the search query.

46. The system of claim 41, wherein the modifying concept is objective.

47. The system of claim 41, wherein the modifying concept is subjective.

48. A system for search, the system comprising:

a database comprising a knowledge graph; and

one or more computers configured to perform operations comprising;

identifying a modifying concept based on a search query;

determining a rule for ranking search results based at least in part on the modifying concept and on the knowledge graph from which at least one of the search results was obtained; and

ranking search results based at least in part on the rule.

49. The system of claim 48, wherein the one or more computers are further configured to perform operations comprising:

identifying in the knowledge graph an entity type associated with the search query; and

retrieving data related to the modifying concept and the entity type, wherein the data comprises at least the rule.

50. The system of claim 48, wherein the one or more computers are further configured to perform operations comprising:

identifying, in the knowledge graph, a property associated with the search query; and

retrieving data related to the modifying concept and the property, wherein the data comprises at least the rule.

51. The system of claim 48, wherein identifying the modifying concept comprises identifying at least one desired ranking property from the search query.

52. The system of claim 48, wherein the one or more computers are further configured to perform operations comprising:

retrieving a quality score from the knowledge graph for each respective search result of the search results, wherein the quality is score is associated with the modifying concept; and

determining the rule for ranking search results based at least in part on the quality score.

53. The system of claim 48, wherein determining the rule for ranking search results comprises determining the frequency of occurrence of the modifying concept in each respective search result of the search results.

54. The system of claim 48, wherein the one or more computers are further configured to perform operations comprising:

identifying an entity reference in the knowledge graph based on the search query;

identifying two or more entity types associated with the entity reference;

determining a weight for each of the two or more entity types based at least in part on the relationship of the entity reference with each respective entity type; and

determining data based at least in part on the weight and the two or more entity types, wherein the data comprises at least the rule.

55. The system of claim 48, wherein the one or more computers are further configured to perform operations comprising:

identifying a first and second entity reference in a search query;

retrieving, from the knowledge graph, a set of search results based at least in part on the first entity reference;

determining a related entity score for each respective search result of the set of search results, wherein the related entity score is based at least in part on the co-occurrence of data related to the first entity reference and data related to the second entity reference in each respective search result; and

determining at least the rule based on the related entity score.

56. The system of claim 48, wherein the modifying concept comprises a range filter.

57. The system of claim 48, wherein the modifying concept is explicit.

58. The system of claim 48, wherein the modifying concept is implicit.

59. The system of claim 48, wherein the modifying concept is subjective.

60. The system of claim 48, wherein the modifying concept is objective.

61. A non-transitory computer-readable medium for using in providing search results, the computer-readable medium having computer program instructions recorded thereon for:

determining, using one or more processors, an entity reference from a search query;

identifying, using one or more processors, a ranked list of properties associated with a type of the entity reference based on a knowledge graph;

identifying, using one or more processors, a property for generating a presentation of search results from the ranked list of properties, based at least in part on the search query and on the type of the entity reference;

determining, using one or more processors, a presentation technique associated with the property for generating a presentation; and

causing to be presented, using one or more processors, search results based on presentation technique.

62. The non-transitory computer-readable medium of claim 61, the computer-readable medium having further instructions for causing search results to be presented in an arrangement according to user input.

63. The non-transitory computer-readable medium of claim 62, wherein user input comprises a search query.

64. The non-transitory computer-readable medium of claim 62, wherein user input comprises a selection of a presentation technique.

65. The non-transitory computer-readable medium of claim 61, wherein determining an entity reference from a search query comprises determining an associated node in the knowledge graph.

66. The non-transitory computer-readable medium of claim 61, wherein identifying a ranked list of properties comprises identifying a preprocessed schema table.

67. The non-transitory computer-readable medium of claim 61, wherein the ranked list of properties is ranked based at least in part on popularity.

68. The non-transitory computer-readable medium of claim 61, wherein the ranked list of properties is ranked based at least in part on co-occurrence.

69. The non-transitory computer-readable medium of claim 61, wherein identifying a property for generating a presentation of search results comprises selecting a property from the ranked list of property based at least in part on the search query.

70. The non-transitory computer-readable medium of claim 61, wherein the presentation technique is selected from the group consisting of gallery, timeline, map, list, chart, and any combination thereof.

71. A non-transitory computer-readable medium for using in providing search results, the computer-readable medium having computer program instructions recorded thereon for:

receiving a search query;

identifying, using one or more processors, a modifying concept in the search query;

generating, using one or more processors, a query tree based on the search query;

retrieving, using one or more processors, search results from a knowledge graph, based at least in part on the query tree;

determining, using one or more processors, at least one ranking property based at least on the modifying concept; and

ranking, using one or more processors, the search results based at least in part on the at least one ranking property.

72. The non-transitory computer-readable medium of claim 71, wherein the at least one ranking property includes an ordering direction.

73. The non-transitory computer-readable medium of claim 71, wherein generating the query tree comprise:

generating a plurality of query trees based on the second portion of the search query;

determining a score for each query tree of the plurality of query trees; and

selecting a query tree from the plurality of query trees based at least in part on the score.

74. The non-transitory computer-readable medium of claim 71, wherein the modifying concept is explicit in the search query.

75. The non-transitory computer-readable medium of claim 71, wherein the modifying concept is implicit in the search query.

76. The non-transitory computer-readable medium of claim 71, wherein the modifying concept is objective.

77. The non-transitory computer-readable medium of claim 71, wherein the modifying concept is subjective.

78. A non-transitory computer-readable medium for search, the computer-readable medium having computer program instructions recorded thereon for:

identifying, using one or more processors, a modifying concept based on a search query;

determining, using one or more processors, a rule for ranking search results based at least in part on the modifying concept and on a knowledge graph from which at least one of the search results was obtained; and

ranking, using one or more processors, search results based at least in part on the rule.

79. The non-transitory computer-readable medium of claim 78, wherein determining the rule for ranking search results comprises:

identifying in the knowledge graph an entity type associated with the search query; and

retrieving data related to the modifying concept and the entity type, wherein the data comprises at least the rule.

80. The non-transitory computer-readable medium of claim 78, wherein determining the rule for ranking search results comprises:

identifying, in the knowledge graph, a property associated with the search query; and

retrieving data related to the modifying concept and the property, wherein the data comprises at least the rule.

81. The non-transitory computer-readable medium of claim 78, wherein identifying the modifying concept comprises identifying at least one desired ranking property from the search query.

82. The non-transitory computer-readable medium of claim 78, wherein determining the rule for ranking search results comprises:

retrieving a quality score from the knowledge graph for each respective search result of the search results, wherein the quality is score is associated with the modifying concept; and

determining the rule for ranking search results based at least in part on the quality score.

83. The non-transitory computer-readable medium of claim 78, wherein determining the rule for ranking search results comprises determining the frequency of occurrence of the modifying concept in each respective search result of the search results.

84. The non-transitory computer-readable medium of claim 78, wherein determining the rule comprises:

identifying an entity reference in the knowledge graph based on the search query;

identifying two or more entity types associated with the entity reference;

determining a weight for each of the two or more entity types based at least in part on the relationship of the entity reference with each respective entity type; and

determining data based at least in part on the weight and the two or more entity types, wherein the data comprises at least the rule.

85. The non-transitory computer-readable medium of claim 78, wherein determining the rule for ranking search results comprises:

identifying a first and second entity reference in a search query;

retrieving, from the knowledge graph, a set of search results based at least in part on the first entity reference;

determining a related entity score for each respective search result of the set of search results, wherein the related entity score is based at least in part on the co-occurrence of data related to the first entity reference and data related to the second entity reference in each respective search result; and

determining at least the rule based on the related entity score.

86. The non-transitory computer-readable medium of claim 78, wherein the modifying concept comprises a range filter.

87. The non-transitory computer-readable medium of claim 78, wherein the modifying concept is explicit.

88. The non-transitory computer-readable medium of claim 78, wherein the modifying concept is implicit.

89. The non-transitory computer-readable medium of claim 78, wherein the modifying concept is subjective.

90. The non-transitory computer-readable medium of claim 78, wherein the modifying concept is objective.