Relevance ranking for document retrieval

• US 20080208847A1
• Filed: 02/25/2008
• Published: 08/28/2008
• Est. Priority Date: 02/26/2007
• Status: Abandoned Application

• Current Assignee: Siemens Corp. (Siemens AG)
• Original Assignee: Siemens Corp. (Siemens AG)
• Inventors: Neubauer, Claus, Moerchen, Fabian, Brinker, Klaus
• Application Number:
  US12/072,222
• Publication Number:
  US 20080208847A1
• Time in Patent Office: Days
• US Class Current: 707/5
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06F 16/338 Presentation of query results

Abstract

Documents and/or document clusters are ranked with respect to their geographical locations and/or user specific (e.g., user input) relevance. Highly relevant documents and/or document clusters are assigned higher ranks than less relevant documents and/or clusters. In this way, ranked lists of documents and/or clusters, top clusters (e.g., top stories), top documents (e.g., most important articles), etc. may be served (e.g., presented, delivered, etc.) to users.

Claims

1. A method of sorting objects in document clustering systems comprising:

determining an object location;

determining a relevance factor for the object based at least in part on object information including the object location; and

ranking the object in relation to one or more other objects based on the relevance factor.

2. The method of claim 1 wherein the objects are documents and determining the document location comprises:

determining a frequency of each of one or more geographical coordinates associated with the object;

weighting the geographical coordinates based on the determined frequencies;

determining a mean of weighted geographical coordinates;

determining geographical distance measures between each of the geographical coordinates and the mean of weighted geographical coordinates; and

selecting the geographical coordinate of the closest geographical distance measure as the document location.

3. The method of claim 2 wherein determining a geographical distance measure between a geographical coordinate and the mean of weighted geographical coordinates comprises:

4. The method of claim 1 wherein the objects are documents and determining the document location comprises:

determining a frequency of each of the one or more geographical coordinates;

weighting the geographical coordinates based on the determined frequencies;

determining a mean of weighted geographical coordinates; and

selecting the mean of weighted geographical coordinates as the document location.

5. The method of claim 1 wherein the objects are clusters and ranking the cluster in relation to one or more other clusters further comprises determining a most relevant cluster.

6. The method of claim 5 wherein the information for the cluster includes a size of the cluster, an age of the cluster, a conciseness measure of the cluster, sources of the documents of the cluster, relevance measures of the sources of the documents of the cluster, and a diversity measure of the cluster and determining the most relevant cluster comprises:

applying a weighting factor to at least a portion of the information for the cluster; and

determining the relevance factor for the cluster of documents by determining

7. The method of claim 6 wherein determining the relevance factor for the cluster of documents further comprises

8. The method of claim 1 wherein the objects are documents in a cluster and determining the relevance factor for the document based on document information further comprises:

9. The method of claim 1 further comprising:

receiving a query input; and

wherein the object is a cluster comprising one or more documents and determining the relevance factor for the cluster based on cluster information further comprises determining;

a relevance factor of each of the one or more documents based on the received query input; and

10. The method of claim 1 further comprising:

receiving a query input; and

wherein the object is a document in a cluster comprising one or more documents and determining the relevance factor for the document based on document information further comprises determining;

11. The method of claim 10 wherein the query representation is a geographical coordinate of the query and the numerical distance Dist is determined as

12. A machine readable medium having program instructions stored thereon, the instructions capable of execution by a processor and defining the steps of:

determining an object location;

determining a relevance factor for the object based at least in part on object information including the object location; and

ranking the object in relation to one or more other objects based on the relevance factor.

13. The machine readable medium of claim 12 wherein the objects are documents and the instructions for determining the document location further define the steps of:

determining a frequency of each of one or more geographical coordinates associated with the object;

weighting the geographical coordinates based on the determined frequencies;

determining a mean of weighted geographical coordinates;

determining geographical distance measures between each of the geographical coordinates and the mean of weighted geographical coordinates; and

selecting the geographical coordinate of the closest geographical distance measure as the document location.

14. The machine readable medium of claim 13 wherein the instructions of determining a geographical distance measure between a geographical coordinate and the mean of weighted geographical coordinates further define the steps of:

15. The machine readable medium of claim 12 wherein the objects are documents and the instructions for determining the document location further define the steps of:

determining a frequency of each of the one or more geographical coordinates;

weighting the geographical coordinates based on the determined frequencies;

determining a mean of weighted geographical coordinates; and

selecting the mean of weighted geographical coordinates as the document location.

16. The machine readable medium of claim 12 wherein the objects are clusters and the instructions for ranking the cluster in relation to one or more other clusters further defines the step of:

determining a most relevant cluster.

17. The machine readable medium of claim 16 wherein the information for the cluster includes a size of the cluster, an age of the cluster, a conciseness measure of the cluster, sources of the documents of the cluster, relevance measures of the sources of the documents of the cluster, and a diversity measure of the cluster and the instructions for determining the most relevant cluster further define the steps of:

applying a weighting factor to at least a portion of the information for the cluster; and

determining the relevance factor for the cluster of documents by determining

18. The machine readable medium of claim 17 wherein the instructions for determining the relevance factor for the cluster of documents further define the step of:

19. The machine readable medium of claim 12 wherein the objects are documents in a cluster and the instructions for determining the relevance factor for the document based on document information further defines the step of:

20. The machine readable medium of claim 12 wherein the object is a cluster comprising one or more documents and the instructions further define the step of:

receiving a query input; and

the instructions for determining the relevance factor for the cluster based on cluster information further define the step of determining;

a relevance factor of each of the one or more documents based on the received query input; and

21. The machine readable medium of claim 12 wherein object is a document in a cluster comprising one or more documents and the instructions further define the steps of:

receiving a query input; and

the instructions for determining the relevance factor for the document based on document information further define the step of determining;

22. The machine readable medium of claim 21 wherein the query representation is a geographical coordinate of the query and the numerical distance Dist is determined as