ESTABLISHING DOCUMENT RELEVANCE BY SEMANTIC NETWORK DENSITY

• US 20080086465A1
• Filed: 10/09/2006
• Published: 04/10/2008
• Est. Priority Date: 10/09/2006
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Schoop, Joel Howard, Fontenot, Nathan D., Moilanen, Jacob Lorien, Strosaker, Michael Thomas
• Application Number:
  US11/539,753
• Publication Number:
  US 20080086465A1
• Time in Patent Office: Days
• US Class Current: 707/5
• CPC Class Codes: G06F 16/36 Creation of semantic tools, e.g. ontology or thesauri

Abstract

A computer implemented method, data processing system, and computer program product for establishing document relevance by semantic network density. When a search query is received, one or more semantic networks are identified which contain nodes matching one or more terms in the search query. An edge density is determined for each node matching a term in the search query. A relevancy score is then calculated for each of the one or more semantic networks based on the edge densities of the nodes matching a term in the search query. Based on the relevancy score, the relevancy to the search query of a document associated with the one or more semantic networks may then be determined.

Claims

1. A computer implemented method for establishing document relevance by semantic network density, the computer implemented method comprising:

responsive to receiving a search query, identifying one or more semantic networks containing nodes matching one or more terms in the search query;

determining an edge density for each node matching a term in the search query;

calculating a relevancy score for each of the one or more semantic networks based on the edge densities of the nodes matching a term in the search query; and

determining a relevancy, to the search query, of a document associated with the one or more semantic networks based on the relevancy score.

2. The computer implemented method of claim 1, wherein calculating a relevancy score for a semantic network further comprises:

determining a total number of nodes in the semantic network which match a term in the search query;

determining a total number of edges for all of the nodes in the semantic network which match a term in the search query; and

multiplying the total number of nodes by the total number of edges to obtain the relevancy score for the semantic network.

3. The computer implemented method of claim 2, further comprising:

responsive to a determination that a document is associated with one or more semantic networks, adding the relevancy scores of each of the semantic networks together to determine the relevancy of the document.

4. The computer implemented method of claim 1, wherein a semantic network having a higher edge density is more relevant to the search query.

5. The computer implemented method of claim 1, further comprising:

prior to receiving the search query, indexing a repository of documents to form an index; and

generating one or more semantic networks for each document in the repository.

6. The computer implemented method of claim 5, wherein terms in the one or more semantic networks are stored within a symbol table in the repository.

7. The computer implemented method of claim 1, wherein the edge density for a node is a number of edges incident to the node.

8. The computer implemented method of claim 1, wherein the semantic network consists only of a list of nodes and a number of edges incident to each node.

9. A data processing system for establishing document relevance by semantic network density, the data processing system comprising:

a bus;

a storage device connected to the bus, wherein the storage device contains computer usable code;

at least one managed device connected to the bus;

a communications unit connected to the bus; and

a processing unit connected to the bus, wherein the processing unit executes the computer usable code to identify one or more semantic networks containing nodes matching one or more terms in a search query in response to receiving the search query, determine an edge density for each node matching a term in the search query, calculate a relevancy score for each of the one or more semantic networks based on the edge densities of the nodes matching a term in the search query, and determine a relevancy, to the search query, of a document associated with the one or more semantic networks based on the relevancy score.

10. The data processing system of claim 9, wherein the processing unit further executes the computer usable code to calculate a relevancy score for a semantic network by determining a total number of nodes in the semantic network which match a term in the search query, determining a total number of edges for all of the nodes in the semantic network which match a term in the search query, and multiplying the total number of nodes by the total number of edges to obtain the relevancy score for the semantic network.

11. The data processing system of claim 10, wherein the processing unit further executes the computer usable code to add the relevancy scores of each of the semantic networks together to determine the relevancy of a document in response to a determination that the document is associated with one or more semantic networks.

12. The data processing system of claim 9, wherein a semantic network having a higher edge density is more relevant to the search query.

13. The data processing system of claim 9, wherein the processing unit further executes the computer usable code to index a repository of documents to form an index prior to receiving the search query, and generate one or more semantic networks for each document in the repository.

14. A computer program product for establishing document relevance by semantic network density, the computer program product comprising:

a computer usable medium having computer usable program code tangibly embodied thereon, the computer usable program code comprising;

computer usable program code for identifying one or more semantic networks containing nodes matching one or more terms in a search query in response to receiving the search query;

computer usable program code for determining an edge density for each node matching a term in the search query;

computer usable program code for calculating a relevancy score for each of the one or more semantic networks based on the edge densities of the nodes matching a term in the search query; and

computer usable program code for determining a relevancy, to the search query, of a document associated with the one or more semantic networks based on the relevancy score.

15. The computer program product of claim 14, wherein the computer usable program code for calculating a relevancy score for a semantic network further comprises:

computer usable program code for determining a total number of nodes in the semantic network which match a term in the search query;

computer usable program code for determining a total number of edges for all of the nodes in the semantic network which match a term in the search query; and

computer usable program code for multiplying the total number of nodes by the total number of edges to obtain the relevancy score for the semantic network.

16. The computer program product of claim 15, further comprising:

computer usable program code for adding the relevancy scores of each of the semantic networks together to determine the relevancy of a document in response to a determination that the document is associated with one or more semantic networks.

17. The computer program product of claim 14, wherein a semantic network having a higher edge density is more relevant to the search query.

18. The computer program product of claim 14, further comprising:

computer usable program code for indexing a repository of documents to form an index prior to receiving the search query;

computer usable program code for generating one or more semantic networks for each document in the repository.

19. The computer program product of claim 14, wherein the semantic network consists only of a list of nodes and a number of edges incident to each node.

20. The computer program product of claim 14, wherein the edge density for a node is a number of edges incident to the node.