Search of sources and targets based on relative expertise of the sources

• US 10,025,860 B2
• Filed: 07/13/2015
• Issued: 07/17/2018
• Est. Priority Date: 12/01/2009
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Ghosh, Rishab Aiyer, Emerson, Thomas James, Cui, Lun Ted
• Primary Examiner(s): Waldron, Scott A.
• Application Number:
  US14/798,240
• Publication Number:
  US 20160042071A1
• Time in Patent Office: 1,100 Days
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/334 Query execution G06F16/335 takes precedence
• CPC Class Codes: G06F 16/382 using citations hypermedia G06F16/94
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/953 Querying, e.g. by the use of web search engines

Abstract

A new approach is proposed that contemplates systems and methods to provide a ranking of cited objects and citing subjects identified as results of a search, where the relative expertise of subjects or sources of citations to said targets or objects is considered. The relative expertise is a function of the share of the subject'"'"'s citations matching the query term or search criteria relative to the share of all subjects'"'"' citations matching the query term, weighted by the influence of the subjects. This allows the identification of “experts” on “topics” without any pre-defined categorization of topics or pre-computation of expertise. Under this novel approach, expertise can be determined on any query term in real-time.

Claims

1. A system, comprising:

at least one processor;

a memory coupled to the processor through a bus; and

a process executed from the memory by the processor causes the at least one processor to retrieve a plurality of citations composed by a plurality of subjects citing a plurality of objects that fit one or more search criteria, determine an influence of each of the subjects and the objects using a citation graph, wherein the citation graph including includes the plurality of citations each describing an opinion of an object by a subject, the citation graph further including nodes or entities that are subjects that have an opinion or make citations and objects cited by citations relative to subjects that have opinions or make citations, determine an expertise of a subject as a measure of the subjects expertise in a topic relative to a larger population of multiple subjects and allow for determination of expertise on any query term in real-time, rank the cited objects of the plurality of citations using the influence and relative expertise of the subjects, and select objects as a search result for a user based on the matching of the objects with the search criteria as well as the relative expertise of the citing subjects.

2. The system of claim 1, 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.

3. The system of claim 1, wherein:

each of the plurality of subjects is one of;

representation of a person, web log, and entities representing Internet authors or users of social media services, user of microblogging services, users of social networks, reviewer who provides expressions of opinion, reviews, or other information useful for the estimation of influence.

4. 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).

5. The system of claim 1, wherein:

each of the plurality of citations describes an opinion by a subject on an object matching the search criteria in the search result.

6. The system of claim 1, 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.

7. The system of claim 1, wherein the process further causes the processor to enable a citation centric search process that focuses on influence of the plurality subjects that cite the plurality of objects.

8. The system of claim 1, wherein the process further causes the processor to accept and enforce a plurality of criteria on citation searching, retrieving and ranking, each of which is either explicitly described by a user or best guessed by the system based on internal statistical data.

9. The system of claim 1, wherein the process further causes the processor to compute the relative expertise of a subject as a measure of the subject'"'"'s expertise in a topic relative to a larger population of multiple sources and subjects.

10. The system of claim 1, wherein the process further causes the processor to adopt a semantic graph of related terms, which allows match to the query term used for computing the relative expertise of the subject to be broader than a match of the query term itself.

11. The system of claim 1, wherein the process further causes the processor to rank the objects in the search result based on one or more of a ranking function of the citations for the objects matching the search criteria, the influence of the subjects of each matching citation, and the relative expertise on of each of the citing subjects.

12. A non-transitory machine-readable medium having executable instructions to cause one or more processing units to perform a method comprising:

retrieving a plurality of citations composed by a plurality of subjects citing a plurality of objects that fit one or more search criteria;

determining an influence of each of the subjects and the objects using a citation graph, the citation graph including the plurality of citations each describing an opinion of an object by a subject, the citation graph including nodes or entities that are subjects that have an opinion or make citations, and objects cited by citations relative to subjects that have opinions or make citations;

determining an expertise of a subject as a measure of the subjects expertise in a topic relative to a larger population of multiple subjects and allow for determination of expertise on any query term in real-time;

ranking the cited objects of the plurality of citations using the influence and relative expertise of the subjects; and

selecting objects as a search result for a user based on the matching of the objects with the search criteria as well as the relative expertise of the citing subjects.

13. The non-transitory machine-readable medium of claim 12, 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.

14. The non-transitory machine-readable medium of claim 12, wherein the method further causes the processor to enable a citation centric search process that focuses on influence of the plurality of subjects that cite the plurality of objects.

15. The non-transitory machine-readable medium of claim 12, wherein the method further causes the processor to accept and enforce a plurality of criteria on citation searching, retrieving and ranking, each of which is either explicitly described by a user or best guessed by the system based on internal statistical data.

16. The non-transitory machine-readable medium of claim 12, wherein the method further causes the processor to compute the relative expertise of a subject as a measure of the subject'"'"'s expertise in a topic relative to a larger population of multiple sources and subjects.

17. The non-transitory machine-readable medium of claim 12, wherein the method further causes the processor to adopt a semantic graph of related terms, which allows match to the query term used for computing the relative expertise of the subject to be broader than a match of the query term itself.

18. The non-transitory machine-readable medium of claim 12, wherein the method further causes the processor to rank the objects in the search result based on one or more of a ranking function of the citations for the objects matching the search criteria, the influence of the subjects of each matching citation, and the relative expertise on of each of the citing subjects.

19. A system comprising:

at least one processor;

a memory coupled to the processor through a bus; and

a process executed from the memory by the processor causes the at least one processor to retrieve a plurality of citations composed by a plurality of subjects citing a plurality of objects that fit one or more search criteria, determine an influence of each of the subjects and the objects using the citation graph, the citation graph including the plurality of citations each describing an opinion of an object by subject, the citation graph including nodes or entities that are subjects that have an opinion or make citations, and objects cited by citations relative to subjects that have opinions or make citations, determine an-expertise of a subject as a measure of the subjects expertise in a topic relative to a larger population of multiple subjects and allow for determination of expertise on any query term in real-time, rank the citing subjects of the plurality of citations using the influence and relative expertise of the subjects, and select subjects as a search result for a user based on the influence scores and/or the relative expertise of the citing subjects.

20. A non-transitory machine-readable medium having executable instructions to cause one or more processing units to perform a method comprising:

retrieving a plurality of citations composed by a plurality of subjects citing a plurality of objects that fit one or more search criteria;

using a citation graph to determine an influence of each of the subjects and the objects, the citation graph including the plurality of citations each describing an opinion of an object by subject, the citation graph including nodes or entities that are subjects that have an opinion or make citations, and objects cited by citations relative to subjects that have opinions or make citations;

determining an expertise of a subject as a measure of the subjects expertise in a topic relative to a larger population of multiple subjects and allow for determination of expertise on any query term in real-time;

ranking the citing subjects of the plurality of citations using the influence and relative expertise of the subjects; and

selecting subjects as a search result for a user based on the influence scores and/or the relative expertise of the citing subjects.