Method for handling anchor text

• US 7,499,913 B2
• Filed: 01/26/2004
• Issued: 03/03/2009
• Est. Priority Date: 01/26/2004
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Kraft, Reiner, Neumann, Andreas
• Primary Examiner(s): Lee; Wilson
• Application Number:
  US10/764,801
• Publication Number:
  US 20050165781A1
• Time in Patent Office: 1,863 Days
• Field of Search: 707 2- 6, 707/10
• US Class Current: 1/1
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9558 Details of hyperlinks; Management of linked annotations
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access

Abstract

Disclosed is a method for processing anchor text for information retrieval. A set of anchors that point to a target document is formed. Anchors with same anchor text are grouped together. Information is computed for each group. Context information is generated for the target document based on the computed information.

Claims

1. A method being executable in a computer for processing anchor text, comprising:

forming a set of anchors that point to a target document, wherein each anchor is a path from a source document to the target document;

grouping together anchors with same anchor text, wherein each anchor is associated with anchor text;

computing a relevance score for each group, wherein computing the relevance score includes computing a linguistic score for each group; and

generating context information for the target document based on the computed relevance score, wherein a title is composed from text of a group with a highest relevance score and a summary of the target document is composed from anchor texts of a number of groups with highest relevance scores.

2. The method of claim 1, further comprising:

determining a language of each document in a collection of documents;

determining rank of each document in the collection of documents; and

determining a proximity class of each document in the collection of documents, wherein the proximity class specifies how close a source document is to the target document.

3. The method of claim 1, further comprising:

determining a predominant language in the set of anchors; and

pruning anchors from the set that are not in the predominant language.

4. The method of claim 1, further comprising:

pruning anchors from the set that include at least a portion of a path to the target document.

5. The method of claim 1, further comprising:

pruning anchors based on a configurable set of words.

6. The method of claim 1, wherein computing the relevance score further comprises:

computing a weighted sum of occurrences for anchor text for anchors in each group, wherein a weight of each individual occurrence of the anchor text is determined by a proximity class of an anchor and a weight associated with that proximity class.

7. The method of claim 1, wherein computing the relevance score further comprises:

computing an accumulated rank for each group.

8. The method of claim 1, wherein computing the relevance score further comprises:

generating the relevance score for each group based on a weighted sum of occurrences, an accumulated rank, and a linguistic score.