System and method for query temporality analysis

• US 8,892,541 B2
• Filed: 06/15/2011
• Issued: 11/18/2014
• Est. Priority Date: 12/01/2009
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Topsy Labs, Inc (Apple Inc.)
• Inventors: Ghosh, Rishab Aiyer, Emerson, Thomas James, Cui, Lun Ted
• Primary Examiner(s): Mofiz, Apu
• Assistant Examiner(s): Khoshnoodi, Fariborz
• Application Number:
  US13/161,143
• Publication Number:
  US 20110313986A1
• Time in Patent Office: 1,252 Days
• Field of Search: 707/706, 707/709
• US Class Current: 707/706
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/338 Presentation of query results
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

A new approach is proposed that contemplates systems and methods to determine temporality of a query in order to generate a search result including a list of objects that are not only based on matching of the objects to the query but also based on temporality analysis of the query. Here, the temporality of the query can be defined as the distribution over time of the objects matching the query, i.e., the chronology histogram of the query. Such distribution can be analyzed to provide a classification of the intent of the query. Classification of the intent of the query can result either in discrete classification of the query into categories, or in continuous classification of the query which may be a scalar or vector value resulting from transformations of the chronology histogram.

Claims

1. A system, comprising:

a search engine with one or more processors,

the one or more processors accepting a query from a user as part of a search request;

the one or more processors retrieving a plurality of objects that match the query;

the one or more processors determining a distribution over time of the objects matching the query for temporality analysis of the query;

the one or more processors analyzing the distribution over time of the objects to provide a classification of the query;

the one or more processors generating a search result including the objects that are not only based on matching of the objects to the query but also based on the classification of the query;

the one or more processors providing a classification of the query into various categories, a query with constant or even distribution of objects over time is a knowledge or canonical query and is classified by the search engine, while the query with distribution of the objects concentrated at particular points is directed on a specific event, and a query with distribution of the objects increased over recent time reflecting recent events;

one or more processors selecting a time window based on the time distribution of the objects matching the query among a given set of time windows;

the one or more processors determining a temporality of a query to generate a search result including a list of objects that are based on matching of the objects to the query and on temporality analysis of the query, where the temporality of the query is a distribution over the time window of the objects matching the query based on timestamp metadata of the objects matching the query; and

the one or more processors ranking the list of objects.

2. The system of claim 1, wherein:

each of the plurality of objects is one of;

Internet web sites, blogs, videos, books, films, music, image, video, documents, data files, objects for sale, objects that are reviewed or recommended or cited, subjects/authors, natural or legal persons, citations, or any entities that are associated with a Uniform Resource Identifier (URI).

3. The system of claim 1, wherein:

the one or more processors provide provides a discrete classification of the query into various categories.

4. The system of claim 3, wherein:

the one or more processors classify the query with constant or even distribution of the objects over time as for knowledge/canonical.

5. The system of claim 3, wherein:

the one or more processors classify the query with the distribution of the objects concentrated at particular points as focused on a specific event.

6. The system of claim 3, wherein:

the one or more processors classify the query with the distribution of the objects increased over time as reflecting the recent interest of the user.

7. The system of claim 1, wherein:

the one or more processors provide a continuous classification of the query.

8. The system of claim 7, wherein:

the continuous classification of the query is a scalar or vector value resulting from transformations of a chronology histogram of the query, which represents the distribution over time of the objects matching the query.

9. The system of claim 1, wherein:

the one or more processors utilize the temporality analysis of the query to select a time window that provides the best search result for the query.

10. The system of claim 9, wherein:

the one or more processors select a combination of multiple time windows that provides the best search result for the query.

11. The system of claim 1, wherein:

the one or more processors enable a citation centric search process that retrieves a plurality of citations composed by a plurality of subjects citing the plurality of objects.

12. The system of claim 11, wherein:

each of the plurality of subjects has an opinion wherein expression of the opinion is explicit, expressed, implicit, or imputed through any other technique.

13. The system of claim 11, wherein:

each of the plurality of citations includes one or more of;

expression of opinions on the objects, expressions of authors in the form of Tweets, blog posts, reviews of objects on Internet web sites Wikipedia entries, postings to social media, postings to websites, postings in the form of reviews, recommendations, or any other form of citation made to mailing lists, newsgroups, discussion forums, comments to websites or any other form of Internet publication.

14. The system of claim 11, wherein:

the one or more processors accept and enforces a plurality of criteria on citation searching, retrieving and ranking, each of which is either be explicitly described by a user or best guessed by the system based on internal statistical data.

15. The system of claim 14, wherein:

the plurality of criteria include one or more of constraints for the plurality of citations, type of the plurality of objects cited, and ranking bias of the cited objects.

16. The system of claim 11, further comprising:

an object selection engine with one or more processors,

the one or more processors of the object selection engine determining temporalities and classifications of one or more of the citing subjects and the cited objects of the citations in addition to temporality of the query;

the one or more processors of the object selection engine providing the list of selected objects based on one or more of these temporalities and classifications.

17. The system of claim 16, wherein:

the one or more processors of the object selection engine determine the temporality of a subject based on time distribution of the citations from the subject that match the query.

18. The system of claim 16, wherein:

the one or more processors of the object selection engine determine the temporality of a subject based on time distribution of all of the citations from the subject.

19. The system of claim 16, wherein:

the one or more processors of the object selection engine utilize the temporalities to identify whether the subjects of the citations are either evenly or concentratedly distributed over time.

20. The system of claim 16, wherein:

the one or more processors of the object selection engine classify the subjects either into discrete classification of categories or in continuous classification.

21. The system of claim 16, wherein:

the one or more processors of the object selection engine determine the temporality of an object based on time distribution of the citations for the object that match the query.

22. The system of claim 16, wherein:

the one or more processors of the object selection engine determine the temporality of an object based on time distribution of all of the citations for the object.

23. The system of claim 16, wherein:

the object selection engine utilizes the temporalities to identify whether the objects of the citations are either evenly or concentratedly distributed over time.

24. The system of claim 16, wherein:

the one or more processors of the object selection engine classify the objects either into discrete classification of categories or in continuous classification.

25. The system of claim 16, wherein:

the one or more processors of the object selection engine weigh the time distribution a query, a subject or an object based on attributes associated with each citation, or attributes associated with the subject or object of each citation.

26. The system of claim 25, wherein:

the attributes include one or more of language, location, source, and time of the citation or the subject or object of the citation.

27. The system of claim 16, wherein:

the one or more processors of the object selection engine utilize the classifications of the query, the subject, and/or the object to perform one or more of choosing different forms of displaying the search result to the user, choosing different methods to determine the search result, and as an input to the search result computation.

28. A method, comprising:

using one or more processors to accept a query from a user as part of a search request;

using one or more processors to retrieve a plurality of objects that match the query;

using one or more processors to determine distribution over time of the objects matching the query for temporality analysis of the query;

using one or more processors to analyze the distribution over time of the objects to provide a classification of the query;

the one or more processors generating a search result including the objects that are not only based on matching of the objects to the query but also based on the classification of the query;

the one or more processors providing a classification of the query into various categories, a query with constant or even distribution of objects over time is a knowledge or canonical query and is classified by the search engine, while the query with distribution of the objects concentrated at particular points is directed on a specific event, and a query with distribution of the objects increased over recent time reflecting recent events;

using one or more processors to select a time window based on the time distribution of the objects matching the query among a given set of time windows;

using one or more processors to generate a search result including the objects that are not only based on matching of the objects to the query but also based on the classification of the query within the time window based on timestamp metadata of the objects matching the query; and

the one or more processors ranking the list of objects.

29. The method of claim 28, further comprising:

using one or more processors to provide a discrete classification of the query into various categories.

30. The method of claim 29, further comprising:

using one or more processors to classify the query with constant or even distribution of the objects over time as for knowledge/canonical.

31. The method of claim 29, further comprising:

using one or more processors to classify the query with the distribution of the objects concentrated at particular points as focused on a specific event.

32. The method of claim 29, further comprising:

using one or more processors to classify the query with the distribution of the objects increased over time as reflecting the recent interest of the user.

33. The method of claim 28, further comprising:

using one or more processors to provide a continuous classification of the query.

34. The method of claim 28, further comprising:

using one or more processors to utilize the temporality analysis of the query to select a time window that provides the best search result for the query.

35. The method of claim 34, further comprising:

using one or more processors to select a combination of multiple time windows that provides the best search result for the query.

36. The method of claim 28, further comprising:

using one or more processors to enable a citation centric search process that retrieves a plurality of citations composed by a plurality of subjects citing the plurality of objects.

37. The method of claim 36, further comprising:

using one or more processors to accept and enforce a plurality of criteria on citation searching, retrieving and ranking, each of which is either be explicitly described by a user or best guessed by the system based on internal statistical data.

38. The method of claim 36, further comprising:

using one or more processors to determine temporalities and classifications of one or more of the citing subjects and the cited objects of the citations in addition to temporality of the query;

using one or more processors to provide the list of selected objects based on one or more of these temporalities and classifications.

39. The method of claim 38, further comprising:

using one or more processors to determine the temporality of a subject based on time distribution of the citations from the subject that match the query.

40. The method of claim 38, further comprising:

using one or more processors to determine the temporality of a subject based on time distribution of all of the citations from the subject.

41. The method of claim 38, further comprising:

using one or more processors to utilize the temporalities to identify whether the subjects of the citations are either evenly or concentratedly distributed over time.

42. The method of claim 38, further comprising:

using one or more processors to classify the subjects either into discrete classification of categories or in continuous classification.

43. The method of claim 38, further comprising:

using one or more processors to determine the temporality of an object based on time distribution of the citations for the object that match the query.

44. The method of claim 38, further comprising:

using one or more processors to determine the temporality of an object based on time distribution of all of the citations for the object.

45. The method of claim 38, further comprising:

using one or more processors to utilize the temporalities to identify whether the objects of the citations are either evenly or concentratedly distributed over time.

46. The method of claim 38, further comprising:

using one or more processors to classify the objects either into discrete classification of categories or in continuous classification.

47. The method of claim 38, further comprising:

using one or more processors to weigh the time distribution a query, a subject or an object based on attributes associated with each citation, or attributes associated with the subject or object of each citation.

48. The method of claim 38, further comprising:

using one or more processors to utilize the classifications of the query, the subject, and/or the object to perform one or more of choosing different forms of displaying the search result to the user, choosing different methods to determine the search result, and as an input to the search result computation.

49. A non-transitory machine readable medium having software instructions stored thereon that when executed cause a system to:

accept a query from a user as part of a search request;

retrieve a plurality of objects that match the query;

determine distribution over time of the objects matching the query for temporality analysis of the query;

analyze the distribution over time of the objects to provide a classification of the query;

generate a search result including the objects that are not only based on matching of the objects to the query but also based on the classification of the query;

select a time window based on the time distribution of the objects matching the query among a given set of time windows;

provide a classification of the query into various categories, a query with constant or even distribution of objects over time is a knowledge or canonical query and is classified by the search engine, while the query with distribution of the objects concentrated at particular points is directed on a specific event, and a query with distribution of the objects increased over recent time reflecting recent events; and

determine a temporality of the query to generate a search result including the objects that are not only based on matching of the objects to the query but also based on the classification of the query within the time window based on timestamp metadata of the objects matching the query; and

rank the list of objects.