Using tags in an enterprise search system

• US 8,204,888 B2
• Filed: 12/07/2010
• Issued: 06/19/2012
• Est. Priority Date: 07/14/2006
• Status: Active Grant

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: Frieden, Kurt, Farhang, Dax, Mahendra, Samir, Quezada, Jose
• Primary Examiner(s): Vital, Pierre
• Assistant Examiner(s): Rostami, Mohammad S
• Application Number:
  US12/961,823
• Publication Number:
  US 20110125760A1
• Time in Patent Office: 560 Days
• Field of Search: 707/748, 707/754, 707/723
• US Class Current: 707/748
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques

Abstract

An interface can allow for associating documents with tags. A search system can use connections between the tags and documents to determine search-independent rank values for the documents. The search-independent rank values can be combined with term matching indications to get a total relevance of the document.

Claims

1. A method comprising:

associating, by a computer system, a first plurality of tags with documents in a enterprise;

creating, by the computer system, a tag for a document in the enterprise by;

retrieving a first term associated with the document, wherein the first term corresponds to a location of the document or to metadata associated with the document; and

applying a translation rule to convert the first term into a second term used in the tag;

associating, by the computer system, a second plurality of tags with individuals in the enterprise;

receiving, by the computer system, a search query including a first tag;

determining, by the computer system, a list of documents based on the search query, the list of documents including one or more documents in the enterprise that are associated with the first tag;

determining, by the computer system, a list of experts based on the search query and the list of documents, the list of experts including;

one or more individuals in the enterprise that are associated with the first tag, and

one or more individuals in the enterprise that are knowledgeable about subject matter described in one or more documents in the list of documents; and

generating, by the computer system, a user interface including the list of documents and the list of experts,

wherein a rank value is determined for each document in the list of documents at least partially based on tags that define knowledge encapsulated by the content of each document, wherein the tags are linked with documents by content users,

wherein each document in the list of documents is ordered according to its rank value,

wherein one or more coefficients are calculated for the one or more documents using user actions,

wherein the rank value for the document is calculated using the one or more coefficients;

wherein an expert rank value is determined for each expert in the list of experts at least partially based on tags applied to the experts by content users in the enterprise and one or more connections between the expert and other individuals in the enterprise, and

wherein the experts in the list of experts are ordered according to their expert rank values.

2. The method of claim 1 wherein calculating the rank value for the document based on the one or more coefficients comprises:

(a) for each row of a core data structure;

reading a row of the core data structure into a local memory,

inflating the row,

converting the row into a row of a damped matrix, and

multiplying the row of the damped matrix by a current vector to get a value of a next vector;

(b) comparing the next vector to the current vector, wherein if a difference between the next vector and the current vector is greater than an error value, setting the next vector as the current vector and repeating step (a), and wherein if the difference is less than the error value, determining rank values from the next vector.

3. The method of claim 2 wherein the damped matrix is column stochastic.

4. The method of claim 2 wherein the damped matrix is positive.

5. The method of claim 1 wherein the one or more connections between the document and individuals in the enterprise include an authoring relationship.

6. The method of claim 1 wherein the list of experts includes an author of the document and a user that has tagged the document.

7. The method of claim 1 wherein the rank value for each expert in the list of experts is calculated based on one or more tags associated with the expert.

8. A non-transitory machine-readable storage medium having stored thereon program code executable by a computer system, the program code comprising:

code that causes the computer system to associate a first plurality of tags with documents in an enterprise;

code that cause the computer system to create a tag for a document in the enterprise by;

retrieving a first term associated with the document, wherein the first term corresponds to a location of the document or to metadata associated with the document; and

applying a translation rule to convert the first term into a second term used in the tag;

code that causes the computer system to associate a second plurality of tags with individuals in the enterprise;

code that causes the computer system to receive a search query including a first tag;

code that causes the computer system to determine a list of documents based on the search query, the list of documents including one or more documents in the enterprise that are associated with the first tag;

code that causes the computer system to determine a list of experts based on the search query and the list of documents, the list of experts including one or more individuals in the enterprise that are associated with the first tag and one or more individuals in the enterprise that are knowledgeable about subject matter described in one or more documents in the list of documents; and

code that causes the computer system to generate a user interface including the list of documents and the list of experts,

wherein a rank value is determined for each document in the list of documents at least partially based on tags that define knowledge encapsulated by the content of each document, wherein the tags are linked with documents by content users,

wherein each document in the list of documents is ordered according to its rank value,

wherein one or more coefficients are calculated for the one or more documents using tags,

wherein the rank value for the document is calculated using the one or more coefficients;

wherein an expert rank value is determined for each expert in the list of experts at least partially based on tags applied to the experts by content users in the enterprise and one or more connections between the expert and other individuals in the enterprise, and

wherein the experts in the list of experts are ordered according to their expert rank values.

9. The non-transitory machine-readable storage medium of claim 8 wherein calculating the rank value for the document based on the one or more coefficients comprises:

(a) for each row of a core data structure;

reading a row of the core data structure into a local memory,

inflating the row,

converting the row into a row of a damped matrix, and

multiplying the row of the damped matrix by a current vector to get a value of a next vector;

(b) comparing the next vector to the current vector, wherein if a difference between the next vector and the current vector is greater than an error value, setting the next vector as the current vector and repeating step (a), and wherein if the difference is less than the error value, determining rank values from the next vector.

10. The non-transitory machine-readable storage medium of claim 9 wherein the damped matrix is column stochastic.

11. The non-transitory machine-readable storage medium of claim 9 wherein the damped matrix is positive.

12. The non-transitory machine-readable storage medium of claim 8 wherein the one or more connections between the document and individuals in the enterprise include an authoring relationship.

13. The non-transitory machine-readable storage medium of claim 8 wherein the list of experts includes an author of the document and a user that has tagged the document.

14. The non-transitory machine-readable storage medium of claim 8 wherein the rank value for each expert in the list of experts is calculated based on one or more tags associated with the expert.

15. A system comprising:

a processing component configured to;

associate a first plurality of tags with documents in an enterprise;

create a tag for a document in the enterprise by;

retrieving a first term associated with the document, wherein the first term corresponds to a location of the document or to metadata associated with the document; and

applying a translation rule to convert the first term into a second term used in the tag;

associate a second plurality of tags with individuals in an enterprise;

receive a search query including a first tag;

determine a list of documents based on the search query, the list of documents including one or more documents in the enterprise that are associated with the first tag;

determine a list of experts based on the search query and the list of documents, the list of experts including one or more individuals in the enterprise that are associated with the first tag and one or more individuals in the enterprise that are knowledgeable about subject matter described in one or more documents in the list of documents; and

generate a user interface including the list of documents and the list of experts,

wherein a rank value is determined for each document in the list of documents at least partially based on tags that define knowledge encapsulated by the content of each document, wherein the tags are linked with documents by content users,

wherein each document in the list of documents is ordered according to its rank value,

wherein one or more coefficients are calculated for the one or more documents at least partially based on user actions,

wherein the rank value for the document is calculated using the one or more coefficients;

wherein an expert rank value is determined for each expert in the list of experts at least partially based on tags applied to the experts by content users in the enterprise and one or more connections between the expert and other individuals in the enterprise, and

wherein the experts in the list of experts are ordered according to their expert rank values.