Enhancing cluster analysis using document metadata

• US 9,043,321 B2
• Filed: 03/02/2012
• Issued: 05/26/2015
• Est. Priority Date: 06/29/2011
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Bonanni, Cristina, Dauri, Francesco, Di Balsamo, Arcangelo, Donatelli, Alessandro, Falchetti, Giovanni, Lazzaro, Luca
• Primary Examiner(s): Arjomandi, Noosha
• Application Number:
  US13/411,126
• Publication Number:
  US 20130006990A1
• Time in Patent Office: 1,180 Days
• Field of Search: 707/722, 707/728, 707/731
• US Class Current: 707/731
• CPC Class Codes: G06F 16/35 Clustering; Classification

Abstract

A search query including search criteria can be received. The search criteria can be a text string. An enhanced search against an enhanced index can be executed. The enhanced index can be metadata associated with an enhanced cluster. The enhanced cluster can be a document cluster associated with the metadata. The enhanced cluster can be aggregated into a merged document. The merged document can be a document including the enhanced cluster contents. The ranking algorithm can be executed on the merged document to obtain a final ranking of content within the single document.

Claims

1. A method comprising:

receiving a search query comprising of at least one search criteria, wherein the at least one search criteria is a text string;

executing a search against an enhanced index, wherein the enhanced index is comprised of metadata associated with an enhanced cluster, wherein the enhanced cluster is a document cluster generated utilizing the metadata associated with documents, wherein the metadata is used to indicate conceptually similar documents;

aggregating the enhanced cluster each into a merged document, wherein the merged document is a document comprising of the enhanced cluster contents; and

executing a ranking algorithm on the merged document to obtain a final ranking of content within the merged document, wherein the metadata is a keyword tag, wherein the keyword tag is a product platform value, audience value, and a version revision.

2. The method of claim 1, further comprising:

performing a keyword search against a standard index, wherein the standard index is comprised of keywords obtained from a document within a standard cluster, wherein the standard cluster is a document cluster sharing a plurality of keywords; and

filtering the standard cluster against the enhanced cluster.

3. The method of claim 1, further comprising:

presenting a search result associated with the search query within an interface, wherein the search result is a document cluster comprising of a plurality of documents associated with a standard cluster and an enhanced cluster.

4. The method of claim 3, wherein the presenting conforms to a visual presentation comprising of a graph having a plurality of nodes, wherein the plurality of nodes is associated with an element of the search result.

5. The method of claim 3, wherein the search results conforms to an Extensible Markup Language format.

6. The method of claim 3, wherein the interface comprises a visual search result section and a textual search result section, wherein a tree graph is presented within the visual search result section for the search result when a user interacts with the search result.

7. The method of claim 1, further comprising:

populating a database with a tag, a link, and a table of contents associated with a document, wherein the document is associated with the enhanced cluster.

8. The method of claim 7, wherein the link is associated with a title attribute, a path attribute, and a category attribute.

9. The method of claim 7, wherein the database comprises of a cluster entity and document entity, wherein the cluster entity is associated with a name attribute, wherein the document entity is associated with a title attribute and a path attribute.

10. The method of claim 7, wherein the table of contents comprises a tree structure, wherein documents are associated with each tree leaf, wherein children of a root node of the tree structure are classified as a main topic.

11. The method of claim 1, wherein the merged document is generated through concatenating contents of documents of the enhanced cluster into a single document.

12. A method comprising:

receiving a search query comprising of at least one search criteria, wherein the at least one search criteria is a text string;

executing a search against an enhanced index, wherein the enhanced index is comprised of metadata associated with an enhanced cluster, wherein the enhanced cluster is a document cluster generated utilizing the metadata associated with documents, wherein the metadata is used to indicate conceptually similar documents;

aggregating the enhanced cluster each into a merged document, wherein the merged document is a document comprising of the enhanced cluster contents;

executing a ranking algorithm on the merged document to obtain a final ranking of content within the merged document; and

populating a database with a tag, a link, and a table of contents associated with a document, wherein the document is associated with the enhanced cluster.

13. The method of claim 12, wherein the link is associated with a title attribute, a path attribute, and a category attribute.

14. The method of claim 12, wherein the database comprises of a cluster entity and document entity, wherein the cluster entity is associated with a name attribute, wherein the document entity is associated with a title attribute and a path attribute.

15. A method comprising:

receiving a search query comprising of at least one search criteria, wherein the at least one search criteria is a text string;

executing a search against an enhanced index, wherein the enhanced index is comprised of metadata associated with an enhanced cluster, wherein the enhanced cluster is a document cluster generated utilizing the metadata associated with documents, wherein the metadata is used to indicate conceptually similar documents;

aggregating the enhanced cluster each into a merged document, wherein the merged document is a document comprising of the enhanced cluster contents;

executing a ranking algorithm on the merged document to obtain a final ranking of content within the merged document; and

presenting a search result associated with the search query within an interface, wherein the search result is a document cluster comprising of a plurality of documents associated with a standard cluster and an enhanced cluster, wherein the interface comprises a visual search result section and a textual search result section, wherein a tree graph is presented within the visual search result section for the search result when a user interacts with the search result.