Analysis and transformation tools for structured and unstructured data

• US 20070011183A1
• Filed: 07/05/2005
• Published: 01/11/2007
• Est. Priority Date: 07/05/2005
• Status: Abandoned Application

• Current Assignee: Clarabridge Incorporated
• Original Assignee: Clarabridge Incorporated
• Inventors: Langseth, Justin, Sohn, Gene, Vivatrat, Nithi
• Application Number:
  US11/172,957
• Publication Number:
  US 20070011183A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing

Abstract

A system and method of making unstructured data available to structured data analysis tools. The system includes middleware software that can be used in combination with structured data tools to perform analysis on both structured and unstructured data. Data can be read from a wide variety of unstructured sources. The data may then be transformed with commercial data transformation products that may, for example, extract individual pieces of data and determine relationships between the extracted data. The transformed data and relationships may then be passed through an extraction/transform/load (ETL) layer and placed in a structured schema. The structured schema may then be made available to commercial or proprietary structured data analysis tools.

Claims

1. A section extractor comprising:

code that looks for specific document headers;

code that extracts the specific document headers;

code that stores the specific document header in a schema; and

code that extracts and stores a specific section of a document or a series of specific sections from a document in a schema.

2. The section extractor of claim 1, further comprising code that removes HTML, other tags, or special characters.

3. The section extractor of claim 1, further comprising code that performs character conversion throughout the document.

4. The section extractor of claim 1, further comprising code that determines the start of a section by matching document text to a set of predetermined character strings.

5. The section extractor of claim 4, further comprising start code that can

(i) search from the top of the document down, or from the bottom of the document up;

(ii) search for the first match of any string of the set, or first search the whole document for the first string in the set, moving on to the next string if the first string is not found;

(iii) search in a case-sensitive or case-insensitive manner;

(iv) skip the document if a start string is not found;

or

(v) treat the entire document as one section if a start string is not found.

6. The section extractor of claim 4, further comprising end code that can

(i) search from a section start point, or from the start of the document, or from the end of the document;

(ii) search up or down from a start point;

(iii) stop section extraction after a predetermined number of characters;

(iv) stop section extraction up or down from a stop point;

(v) skip the document if an end string is not found;

(vi) save the rest of the document if an end string is not found;

or

(vii) extract a certain number of characters if an end string is not found.

7. A proximity transformer comprising:

code that looks for a first group of predetermined entities or relationship entries in a analysis schema; and

code that looks for the closest instance of a second predetermined entity for each matching entity or relationship entry in the first group of predetermined entities or relationship entries.

8. The proximity transformer of claim 7, further comprising code that looks for the closest instance of plurality of predetermined entities for each matching entity or relationship entry in the first group of predetermined entities or relationship entries.

9. The proximity transformer of claim 7, wherein a new relationship entry is added to the analysis schema, the new relationship associated with at least an entity in the first group of predetermined entities.

10. A table parser comprising:

code to identify a table in a source document, the code determining the columns and rows according to the amount of whitespace between characters or by reading HTML tags;

code to extract column headers, row headers, data points, and order of magnitude indicators; and

code to convert the table to structured rows, columns, cells, headers and order of magnitude multipliers,

wherein the table parser can adapt dynamically to different formats and to a plurality of combinations of columns and rows.

11. The table parser of claim 10, wherein row headers are determined by looking for table rows that have a label on the left side of the table but do not have corresponding numerical values, or have summary values in columns.

12. The table parser of claim 10, wherein row headers are differentiated from multi-line row labels by analyzing the indentation of a potential header and the row below.

13. The table parser of claim 10, wherein column headers are identified based on their position on tip of columns that substantially contain numerical values.

14. The table parser of claim 10, further comprising code to store the extracted table data in a capture schema in a normalized table.

15. The table parser of claim 14, further comprising code to store the extracted table data in an analysis schema.

16. A confidence analysis routine comprising:

code adapted to calculate a weighted confidence score for a data element, the code weighing

(i) a confidence score provided by a transformation tool used to generate the data element if provided by the transformation tool;

(ii) the number of relationships found in the source document per size of the source document;

compared to the average number of relationships found per kilobyte or other size measure of a document;

(iii) the number of entities found to be associated with the relationship, compared to the average number of entities for relationships in the same hierarchy;

(iv) the number of times similar relationships have been found in the past;

(v) the number of entities that are grouped together to form a master entity;

(vi) the number of times the entity occurs in the document compared to the average number of occurrences for entities in the same hierarchy;

(vii) weighted confidences based on hierarchy of relationship or entity.

17. The confidence analysis routine of claim 16, further comprising commercially available measures of data extraction confidence.

18. A search module comprising:

code to index data in an analysis schema, the index generated by creating data dump reports using a reporting tool that create a list of each entity, topic, or relationship discussed in a document along with a link back to the source document;

or

code to periodically and/or automatically run analytical reports to be included in an indexing process;

or

code to index metadata contained in a definition of a dimensional model of the analysis schema, definitions of facts, definitions of metrics, definitions of measures, data contained within the dimensions and measures.

19. The search module of claim 18, wherein the data dump report is run periodically and/or automatically.

20. The search module of claim 18, further comprising code to rate and rank results of a search.

21. The search module of claim 18, further comprising code to provide links to analytical reports interspersed within standard links back to source documents.

22. The search module of claim 21, further comprising code to index report headers, titles and comments.