Method and apparatus for concept-based searching across a network

• US 7,392,238 B1
• Filed: 08/23/2000
• Issued: 06/24/2008
• Est. Priority Date: 08/23/2000
• Status: Expired due to Fees

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Zhou, Joe F., Liu, Weiquan
• Primary Examiner(s): Mofiz; Apu
• Assistant Examiner(s): Nguyen; Cindy
• Application Number:
  US10/018,518
• Time in Patent Office: 2,862 Days
• Field of Search: 707/2, 707/3, 707/10
• US Class Current: 1/1
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9532 Query formulation
• CPC Class Codes: G06F 16/9538 Presentation of query results
• CPC Class Codes: Y10S 707/99932 Access augmentation or optimizing
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching

Abstract

According to one embodiment of the invention, a method includes receiving a search term for a query. The method also includes searching a network of concept terms for terms related to the search term. Additionally, the query is reformulated using the search term and the related terms. A local database is searched for data terms that match the search term and the related terms. The data terms are from documents residing on websites located on servers across a network. Moreover, the method includes retrieving the documents from the websites whose data terms match the search term and the related terms.

Claims

1. A method comprising:

receiving a search term for a query;

searching a network of concept terms for terms related to the search term, wherein each related term and the search term appear together in at least one sentence in a web page;

reformulating the query using the search term and the related terms before performing a search for documents based on the search term;

searching a local database for data terms that match the search term and the related terms, wherein the data terms are generated based on occurrence frequencies within a document residing on the web sites located on server connected to, wherein the occurrence frequencies include mutual information associated with a first term and a second term within a given web page using a predetermined algorithm, wherein the mutual information is determined based on one or more weight factors of the first and second terms, the one or more weight factors representing occurrence frequencies of the respective term, and wherein the mutual information (MI) of the first term x and the second term y is determined by MI(x, y)=f(x,y)/f(x)+f(y)−

f(x, y), wherein f(x, y) corresponds to an occurrence frequency of both the first term and the second term, wherein f(x) corresponds to an occurrence frequency of the first term, and wherein f(y) corresponds to an occurrence frequency of the second term; and

in response to matching data terms with the search terms and related terms corresponding to the data terms, retrieving the documents from the respective websites.

2. The method of claim 1, further comprising displaying the retrieved documents, the search terms and the related terms, wherein at least one of the related terms includes a link, when activated, a further search of concept terms is conducted and one or more further related terms are presented, and wherein searching the local database and retrieving the documents are literately performed based on the further related terms.

3. The method of claim 1, further comprising generating a summary of the documents for the searched terms that match the search term and the related terms.

4. The method of claim 3, wherein the summary includes the searched terms and a beginning portion of the documents.

5. The method of claim 1, wherein the network is the Internet.

6. The method of claim 1, wherein the network of concept terms includes links between related terms, wherein the links are based on semantic relationships.

7. The method of claim 6, wherein the semantic relationships are selected from a group consisting of canonical, synonym, hyponym, hypernym, part, product and member.

8. The method of claim 1, wherein related terms are more specific than the search term.

9. The method of claim 1, wherein the occurrence frequencies include mutuality between words within the documents.

10. The method of claim 1, wherein the related terms are different than the search term and have similar meaning of the search term.

11. The method of claim 1, wherein the search term includes a name of an organization, and wherein the related terms include at least one of a name of subsidiaries of the organization, a product name of the organization, and a stock symbol of the organization.

12. The method of claim 1, wherein the occurrence frequencies include mutual information associated with a first term and a second term within a given web page based on a predetermined algorithm.

13. The method of claim 12, wherein the mutual information is determined based on one or more weight factors of the first and second terms, the one or more weight factors representing occurrence frequencies of the respective term.

14. A machine-readable storage medium having instructions, when executed by a machine, causes the machine to perform a method, the method comprising;

receiving a search term for a query;

searching a network of concept terms for terms related to the search term, wherein each related term and the search term appear together in at least one sentence in a web page;

reformulating the query using the search term and the related terms before performing a search for documents based on the search term;

searching a local database for data terms that match the search term and the related terms, wherein the data terms are generated based on occurrence frequencies within a document residing on web sites located on server connected to, wherein the occurrence frequencies include mutual information associated with a first term and a second term within a given web page using a predetermined algorithm, wherein the mutual information is determined based on one or more weight factors of the first and second terms, the one or more weight factors representing occurrence frequencies of the respective term, and wherein the mutual information (MI) of the first term x and the second term y is determined by MI(x, y)=f(x,y)/f(x)+f(y)−

f(x, y), wherein f(x, y) corresponds to an occurrence frequency of both the first term and the second term, wherein f(x) corresponds to an occurrence frequency of the first term, and wherein f(y) corresponds to an occurrence frequency of the second term; and

in response to matching data terms with the search terms and related terms corresponding to the data terms, retrieving the documents from the respective websites.

15. The machine-readable storage medium of claim 14, further comprising displaying the retrieved documents, the search terms and the related terms, wherein at least one of the related terms includes a link, when activated, a further search of concept terms is conducted and one or more further related terms are presented, and wherein searching the local database and retrieving the documents are literately performed based on the further related terms.

16. The machine-readable storage medium of claim 14, further comprising generating a summary of the documents for the searched terms that match the search term and the related terms.

17. The machine-readable storage medium of claim 16, wherein the summary includes the searched terms and a beginning portion of the documents.

18. The machine-readable storage medium of claim 14, wherein the network is the Internet.

19. The machine-readable storage medium of claim 14, wherein the network of concept terms includes links between related terms, wherein the links are based on semantic relationships.

20. The machine-readable storage medium of claim 19, wherein the semantic relationships are selected from a group consisting of canonical, synonym, hyponym, hypernym, part, product and member.

21. The machine-readable storage medium of claim 14, wherein related terms are more specific than the search term.

22. The machine-readable storage medium of claim 14, wherein the occurrence frequencies include mutuality between words within the documents.

23. The machine-readable storage medium of claim 14, wherein the related terms are different than the search term and have similar meaning of the search term.

24. The machine-readable storage medium of claim 14, wherein the search term includes a name of an organization, and wherein the related terms include at least one of a name of subsidiaries of the organization, a product name of the organization, and a stock symbol of the organization.

25. The machine-readable storage medium of claim 14, wherein the occurrence frequencies include mutual information associated with a first term and a second term within a given web page based on a predetermined algorithm.

26. The machine-readable storage medium of claim 25, wherein the mutual information is determined based on one or more weight factors of the first and second terms, the one or more weight factors representing occurrence frequencies of the respective term.