Systems, methods, and software for hyperlinking names

• US 9,002,764 B2
• Filed: 12/19/2007
• Issued: 04/07/2015
• Est. Priority Date: 12/21/2001
• Status: Expired due to Fees

• Current Assignee: Thomson Reuters Enterprise Centre GmbH (The Woodbridge Co. Ltd.)
• Original Assignee: Thomson Reuters Global Resources
• Inventors: Dozier, Christopher C.
• Primary Examiner(s): Gaffin, Jeffrey A
• Assistant Examiner(s): Bharadwaj, Kalpana
• Application Number:
  US11/960,054
• Publication Number:
  US 20080301074A1
• Time in Patent Office: 2,666 Days
• Field of Search: 706/21
• US Class Current: 706/21
• CPC Class Codes: G06F 16/3338 Query expansion
• CPC Class Codes: G06F 16/94 Hypermedia Hyperlinking G06F40/134
• CPC Class Codes: G06F 16/9558 Details of hyperlinks; Management of linked annotations
• CPC Class Codes: Y10S 707/99932 Access augmentation or optimizing

Abstract

Hyperlinking or associating documents to other documents based on the names of people in the documents has become more desirable. Although there is an automated system for installing such hyperlinks into judicial opinions, the system is not generally applicable to other types of names and documents, nor well suited to determine hyperlinks for names that might refer to two or more similarly named persons. Accordingly, the inventor devised systems, methods, and software that facilitate hyperlinking names in documents, regardless of type. One exemplary system includes a descriptor module and a linking module. The descriptor module develops descriptive patterns for selecting co-occurent document information that is useful in recognizing associations between names and professional classes. The linking module tags names in an input document, extracts co-occurent information using the descriptive patterns, and uses a Bayesian inference network that processes a (non-inverse-document-frequency) name-rarity score for each name along with the name and selected co-occurent document information to determine appropriate hyperlinks to other documents, such as entries in professional directories.

Claims

1. A computer-implemented method comprising:

identifying a name in a document;

determining a rarity indicator for the name, the rarity indicator representing a measure of how rare the name is in a population, and wherein determining a rarity indicator for the name further comprises the formula;

P(nameUniqueness)=1/((H*P(name))+1)

wherein H denotes the size of the human population likely to be referenced in the document, P(name) is the name match probability score, and P(nameUniqueness) is the probability of name uniqueness; and

defining a hyperlink for the name based on the rarity indicator.

2. The computer-implemented method of claim 1, wherein the name is a person name.

3. The computer-implemented method of claim 1, wherein the rarity indicator is a quantity based on a probability of drawing at least a portion of the name at random from a set of sample names representative of a relevant human population.

4. The computer-implemented method of claim 1, wherein the rarity indicator is a quantity based on a size of a human population, a probability of drawing a first portion of the name at random, and a probability of drawing a second portion of the name at random.

5. The computer-implemented method of claim 1, wherein the first portion is a first name portion of the name and the second portion is a last name portion of the name.

6. The computer-implemented method of claim 1, wherein defining a hyperlink for the name based on the rarity indicator, comprises:

identifying one or more non-person-name terms from the document;

identifying one or more candidate records in a database based on at least a portion of the name;

comparing the non-person-name terms for each of the candidate records to the non-person-name terms from the document;

calculating one or more quantities, each based on the rarity indicator for the person name and the comparison of the non-person-name terms for one of the candidate records; and

defining the hyperlink based on the one or more calculated quantities.

7. The computer-implemented method of claim 6, wherein calculating one or more quantities, each based on the rarity indicator for the person name and the comparison of the non-person-name terms for one of the candidate records, includes using a Bayesian inference engine.

8. The computer-implemented method of claim 6, wherein defining the hyperlink based on the one or more calculated quantities comprises:

comparing the quantities to a threshold; and

defining the hyperlink based on a greatest one of the quantities exceeding the threshold.

9. The computer-implemented method of claim 8, wherein defining the hyperlink based on the greatest one of the quantities exceeding the threshold comprises defining a hyperlink to designate the candidate record corresponding to the greatest one of the quantities.

10. A non-transitory machine-readable medium comprising machine executable instructions for performing the computer-implemented method of claim 1.

11. A non-transitory machine-readable medium comprising machine executable instructions for performing the computer-implemented method of claim 9.

12. A system for adding a hyperlink to a document including a person name, the system comprising:

at least one processor;

a memory coupled to the processor, the memory including instructions for;

identifying a name in a document;

determining a rarity indicator for the name, the rarity indicator representing a measure of how likely the name is to refer to more than one entity in a population, and

wherein determining a rarity indicator for the name further comprises the formula;

P(nameUniqueness)=1/((H*P(name))+1)

wherein H denotes the size of the human population likely to be referenced in the document, P(name) is the name match probability score, and P(nameUniqueness) is the probability of name uniqueness; and

defining a hyperlink for the name based on the rarity indicator.

13. The system of claim 12, wherein the name is a person name.

14. The system of claim 12, wherein the rarity indicator is a quantity based on a probability of drawing at least a portion of the name at random from a set of sample names representative of a relevant human population.

15. The system of claim 12, wherein the rarity indicator is a quantity based on a size of a human population, a probability of drawing a first portion of the name at random, and a probability of drawing a second portion of the name at random from a set of sample names representative of a relevant human population.

16. The system of claim 12, wherein defining a hyperlink for the name based on the rarity indicator, comprises:

identifying one or more non-person-name terms from the document;

identifying one or more candidate records in a database based on at least a portion of the name;

comparing the non-person-name terms for each of the candidate records to the non-person-name terms from the document;

calculating one or more quantities, each based on the rarity indicator for the person name and the comparison of the non-person-name terms for one of the candidate records; and

defining the hyperlink based on the one or more calculated quantities.

17. In a database of electronic records, a set of biographical records, each biographical record comprising:

a name representing a person associated with a biographical record; and

a name-rarity indicator representing a measure of how likely the name is to refer to more than one entity in a population, wherein the name-rarity indicator is determined using the formula;

P(nameUniqueness)=1/((H*P(name))+1)

wherein H denotes the size of the human population likely to be referenced in the document, P(name) is the name match probability score, and P(nameUniqueness) is the probability of name uniqueness.

18. The data structure of claim 17, further comprising:

one or more organizations co-existent in a document with the name; and

one or more locations co-existent in a document with the name.

19. The data structure of claim 18, further including positional information indicating relative position of each organization and each location relative to the name.

20. A computer-implemented method comprising:

receiving a search query including a name of an entity;

determining a measure of how rare the name is in a population, and wherein determining a measure of how rare the name is in a population further comprises the formula;

P(nameUniqueness)=1/((H*P(name))+1)

wherein H denotes the size of the human population likely to be referenced in the document, P(name) is the name match probability score, and P(nameUniqueness) is the probability of name uniqueness; and

obtaining additional information to assist in answering the query, in response to the determined measure.

21. The computer-implemented method of claim 20, wherein obtaining additional information to assist in answering the query in response to the determined measure comprises:

comparing the measure to a threshold; and

requesting additional information if the measure is less than the threshold.

22. The computer-implemented method of claim 20, further comprising:

updating the search query based on the additional information.

23. The computer-implemented method of claim 20, wherein requesting additional information comprises requesting information is related to a profession, a location, and/or an organization.

24. The computer-implemented method of claim 20, wherein obtaining additional information to assist in answering the query in response to the determined measure comprises:

comparing the measure to a threshold;

searching one or more databases based on the name; and

updating or supplementing the query based on results of searching the one or more databases.

25. The computer-implemented method of claim 20, wherein updating or supplementing the query comprises:

defining one or more sub-queries, with each sub-query including information about a professional title, organization, or location associated with the name.

26. A computer-implemented method comprising:

identifying a name included within a set of text;

determining rarity measure representing a likelihood of the identified name occurring in a population based at least in part on co-occurrence of a non-person-name term located in a document proximal to the name, and wherein determining rarity of a name in a population further comprises the formula;

P(nameUniqueness)=1/((H*P(name))+1)

wherein H denotes the size of the human population likely to be referenced in the document, P(name) is the name match probability score, and P(nameUniqueness) is the probability of name uniqueness; and

associating the identified name with a record in a database based at least in part on the rarity measure.

27. The computer-implemented method of claim 26, further comprising:

associating a hyperlink with the name, with at least a portion of the hyperlink defined based on the determined rarity.

28. The computer-implemented method of claim 26, further comprising:

receiving a document including at least one occurrence of the name; and

identifying the name in the document before determining its rarity.

29. The computer-implemented method of claim 26, wherein the name is a person name.