Intelligent retrieval and classification of information from a product manual

• US 7,197,503 B2
• Filed: 11/26/2002
• Issued: 03/27/2007
• Est. Priority Date: 11/26/2002
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Joseph, Sindhu, Venkataraman, Sreedharan, Palanisamy, Lingathurai
• Primary Examiner(s): Cottingham; John
• Assistant Examiner(s): Lu; Kuen S.
• Application Number:
  US10/305,809
• Publication Number:
  US 20040103116A1
• Time in Patent Office: 1,582 Days
• Field of Search: 707/102, 707/6, 707/4, 707/101
• US Class Current: 1/1
• CPC Class Codes: G06F 16/258 Data format conversion from or to a database
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06F 2216/03 Data mining
• CPC Class Codes: Y10S 707/99934 Query formulation, input preparation, or translation
• CPC Class Codes: Y10S 707/99936 Pattern matching access
• CPC Class Codes: Y10S 707/99942 Manipulating data structure, e.g. compression, compaction, compilation
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface

Abstract

An intelligent information-mining system extracts desired intelligent information from unstructured text data in documents to categorize the unstructured text data based on the extracted desired intelligent information. This is accomplished by obtaining unstructured text data from documents in an electronic format. The obtained unstructured text data is then transformed into unstructured neutral format data. The transformed unstructured neutral format data is then converted into structured data based on desired intelligent information. Desired intelligent information is then extracted from the structured data by using a multilayer self-organizing maps (MSOM) algorithm.

Claims

1. A method of extracting intelligent information from text sources in electronic format, comprising:

receiving unstructured data from text sources in an electronic format;

transforming the unstructured data into unstructured neutral format data;

converting the transformed unstructured neutral format data into structured data based on desired intelligent information;

extracting desired intelligent information from the converted structured data by using a multilayer self-organizing maps (MSOM) algorithm; and

storing the desired intelligent information, wherein converting the transformed unstructured neutral format data into structured data based on desired intelligent information, comprises;

extracting multiple packets from the transformed unstructured neutral format data based on desired intelligent information, wherein each extracted packet includes multiple sentence structured text data that is translated to single sentence structured text data including information that facilitates in categorizing and classifying the extracted multiple packets; and

forming multiple preprocessed packets by transforming each single sentence structured data into context-based data based on contextual information and occurrence frequency.

2. The method of claim 1, wherein the received unstructured data comprises data selected from the group consisting of text data and signal components.

3. The method of claim 2, wherein the text sources are selected from the group consisting of the electronic formats of product manuals, sales manuals, maintenance manuals, service manuals, text documents, repair procedures, fault isolation procedures, troubleshooting manuals, wiring diagrams and legacy systems.

4. The method of claim 1, further comprising:

storing the converted structured data in a database.

5. The method of claim 1, further comprising:

sequentially inputting the multiple preprocessed packets into the MSOM algorithm to extract the desired intelligent information from each preprocessed packet.

6. A method of extracting intelligent information from unstructured text data in an electronic data format, comprising:

transforming the unstructured text data into unstructured neutral format text data;

converting the transformed unstructured neutral format text data into structured text data based on desired intelligent information by extracting multiple packets of finer resolution information from the transformed unstructured neutral format text data, wherein each extracted packet includes a single sentence structure data including fault information that facilitates in categorizing and classifying the extracted multiple packets;

inputting the extracted multiple packets into a multi-layer self-organizing maps (MSOM) algorithm;

extracting desired fault information from each inputted packet by using the MSOM algorithm; and

categorizing and classifying each extracted packet based on the extracted desired fault information; and

storing such categorization and classification of each extracted packet.

7. The method of claim 6, wherein inputting the converted neutral format text data into the MSOM algorithm comprises:

sequentially inputting the multiple extracted packets into the MSOM algorithm for extracting the desired information.

8. The method of claim 7, wherein sequentially inputting the one or more extracted packets into the MSOM algorithm for extracting the desired intelligent information comprises:

obtaining a predetermined number of packets from the multiple extracted packets;

generating a template based on a two-dimensional structured document map using the obtained predetermined number of packets from the multiple extracted packets; and

extracting the desired intelligent information from each remaining packet in the multiple extracted packets by projecting the remaining packet onto the generated template.

9. The method of claim 8, wherein generating the template based on a two-dimensional structured document map using the obtained predetermined number of packets, comprises:

extracting key phrases by sequentially inputting the obtained predetermined number of packets;

generating a first layer contextual relation map by mapping each of the extracted key phrases to a two-dimensional map using a self-organizing map and a function approximation neighborhood technique;

forming phrase clusters for the generated first layer contextual relation map;

constructing a first key phrase frequency histogram consisting of the frequency of occurrences of key phrases from the generated first layer contextual relation map; and

generating the template based on the two-dimensional structured document map of the predetermined number of packets from the constructed first key phrase frequency histogram and the generated first layer contextual relation map by using the function approximation neighborhood technique in the self-organizing map.

10. The method of claim 9, further comprising:

transforming each extracted key phrase into a unique numerical representation.

11. The method of claim 6, further comprising:

storing the multiple extracted packets in a database.

12. A physical computer-readable medium having computer-executable instructions for extracting desired intelligent information from unstructured data in aircraft maintenance manuals, comprising:

receiving unstructured text data in the aircraft maintenance manuals in an electronic format;

extracting multiple packets of finer resolution from the unstructured text data, wherein each extracted packet includes multiple sentence structured text data that is translated to single sentence structured text data, wherein each single sentence structured text data includes aircraft fault information that can facilitate in categorizing and classifying the extracted multiple packets based on type of aircraft fault;

obtaining a predetermined number of packets from the multiple extracted packets;

generating a template based on a two-dimensional structured document reap using the obtained predetermined number of packets from the multiple extracted packets; and

extracting the desired category and classification information from each extracted packet by projecting the extracted packet onto the generated template; and

storing such categorization and classification of each extracted packet, wherein generating the template based on a two-dimensional structured document map using the obtained predetermined number of packets, comprises;

extracting key phrases by sequentially inputting the obtained predetermined number of packets;

generating a first layer contextual relation man by mapping each of the extracted key phrases to a two-dimensional map using a self-organizing map and a function approximation neighborhood technique;

forming phrase clusters for the generated first layer of contextual relation map; and

constructing a first key phrase frequency histogram consisting of the frequency of occurrences of key phrases from the generated first layer contextual relation map; and

generating the template based on the two-dimensional structured document map of the predetermined number of packets from the constructed first key phrase frequency histogram and the generated first layer contextual relation map by using the function approximation neighborhood technique in the self-organizing map.

13. The computer-readable medium of claim 12, further comprising:

transforming the extracted key phrases into a unique numerical representation.

14. A computer system to perform data transfer operations between a remotely located communication module and one or more security devices in a COM-based computer network system having multiple users, comprising:

a processor,

an output device; and

a storage device to store instructions that are executable by the processor for extracting desired intelligent information from unstructured data in aircraft maintenance manuals, comprising;

receiving unstructured text data in the aircraft maintenance manuals in an electronic format;

extracting multiple packets of finer resolution from the unstructured text data, wherein each extracted packet includes multiple sentence structured text data that is translated to single sentence structured text data, wherein each single sentence structured text data includes aircraft fault information that can facilitate in categorizing and classifying the extracted multiple packets based on type of aircraft fault;

obtaining a predetermined number of packets from the multiple extracted packets;

generating a template based on a two-dimensional structured document map using the obtained predetermined number of packets from the multiple extracted packets; and

extracting the desired category and classification information from each extracted packet by projecting the extracted packet onto the generated template; and

storing such categorization and classification of each extracted packet.

15. The system of claim 14, wherein generating the template based on a two-dimensional structured document map using the obtained predetermined number of packets, comprises:

extracting key phrases by sequentially inputting the obtained predetermined number of packets;

generating a first layer contextual relation map by mapping each of the extracted key phrases to a two-dimensional map using a self-organizing map and a function approximation neighborhood technique;

forming phrase clusters for the generated first layer contextual relation map;

constructing a first key phrase frequency histogram consisting of the frequency of occurrences of key phrases from the generated first layer contextual relation map; and

generating the template based on the two-dimensional structured document map of the predetermined number of packets from the constructed first key phrase frequency histogram and the generated first layer contextual relation map by using the function approximation neighborhood technique in the self-organizing map.

16. The system of claim 15, further comprising:

transforming the extracted key phrases into a unique numerical representation.

17. The system of claim 15, wherein generating the first layer contextual relation maps comprises:

generating the first layer of contextual relation maps by mapping each of the transformed multiple key phrases to a two-dimensional map using the associated self-organizing maps and the function approximation neighborhood technique.