Structured document type determination system and structured document type determination method

• US 20030194689A1
• Filed: 10/23/2002
• Published: 10/16/2003
• Est. Priority Date: 04/12/2002
• Status: Abandoned Application

• Current Assignee: Mitsubishi Denki Kabushiki Kaisha (Mitsubishi Electric Corporation)
• Original Assignee: Mitsubishi Denki Kabushiki Kaisha (Mitsubishi Electric Corporation)
• Inventors: Kitsuki, Junichi, Kimura, Toshiyuki, Higuchi, Tsuyoshi, Tamura, Takayuki, Kamasaka, Hitoshi
• Application Number:
  US10/277,820
• Publication Number:
  US 20030194689A1
• Time in Patent Office: Days
• US Class Current: 434/350
• CPC Class Codes: G06F 40/221 Parsing markup language streams streaming G06F40/149

Abstract

A structured document type determination system is provided with a feature value extraction unit for extracting a value of each of a plurality of features included in a feature list which is disposed in advance from each of a plurality of structured documents and a determination rule creating unit for creating a determination rule from extracted feature values by using a data mining tool. The structured document type determination system makes an evaluation of the determination rule by comparing results of determining the types of structured documents according to the determination rule and teacher data, and repeatedly delivers a tuning parameter to the data mining tool so as to create a plurality of determination rules and to derive an optimum determination rule.

Claims

1. A structured document type determination system comprising:

a structured document database for storing a plurality of structured documents collected by way of a network;

a teacher data input means for inputting, as teacher data, a type of each of the plurality of structured documents stored in said structured document database;

a determination rule creating means for creating a determination rule used for determining a type of each of the plurality of structured documents based on a plurality of structured documents stored in said structured document database and the teacher data; and

a determination rule applying means for determining the type of a structured document that exists on said network according to the determination rule created by said determination rule creating means.

2. The structured document type determination system according to claim 1, wherein said determination rule creating means creates a plurality of determination rules and then determines the type of each of a plurality of structured documents according to each of the plurality of determination rules, and wherein said structured document type determination system is provided with a determination rule selecting means for making an evaluation of each of the plurality of determination rules based on determination results from said determination rule applying means and the teacher data so as to select one determination rule from among the plurality of determination rules based on an evaluation result.

3. The structured document type determination system according to claim 2, further comprising:

a structured document sampling means for sampling a plurality of arbitrary structured documents from said structured document database;

a sampled structured document database for storing the plurality of structured document sampled by said structured document sampling means;

a structured document feature information database for storing a list of features each of which is a measure to classify a plurality of structured documents into a plurality of predetermined types and each of which can be extracted from structured documents;

a feature value extraction means for extracting a value of each of the plurality of features (referred to as a feature value from here on) from each of the plurality of structured documents stored in said sampled structured document database according to the list of features stored in said structured document feature information database;

a feature value and teacher data database including feature values extracted by said feature value extraction means and the teacher data input by said teacher data input means for each of the plurality of structured documents stored in said sampled structured document database;

a made-for-machine-learning feature value and teacher data database that is a part of said feature value and teacher data database; and

a made-for-verification feature value and teacher data database that is the remainder of said feature value and teacher data database, wherein said determination rule creating means creates the plurality of determination rules each of which is used to classify each of the plurality of structured documents into one of the plurality of types based on said made-for-machine-learning feature value and teacher data database, and said determination rule applying means determines which one of the plurality of types each of the plurality of structured documents whose feature values and teacher data are stored in said made-for-verification feature value and teacher data database is classified into according to each of the plurality of determination rules, and wherein said determination rule selecting means includes a determination rule evaluation means for making an evaluation of each of the plurality of determination rules by comparing the determination results acquired by said determination rule applying means with the teacher data stored in said made-for-verification feature value and teacher data database, a tuning pattern database for storing a list of tuning patterns used for tuning of the creation of the plurality of determination rules, and an optimum determination rule deriving means for selecting a tuning pattern from said tuning pattern database one by one so as to deliver the selected tuning pattern to said determination rule creating means, and for repeating a series of processes, such as causing said determination rule creating means to create a determination rule again according to the selected tuning pattern, causing said determination rule applying means to make a determination of the type of each of the plurality of structured documents stored in said made-for-verification feature value and teacher data database again according to the created determination rule and causing said determination rule evaluation means to make an evaluation of the created determination rule, until the determination rule creation and the evaluation are completed for all of the plurality of tuning patterns stored in said tuning pattern database, so as to derive an optimum determination rule from among a plurality of determination rules acquired during the above processes.

4. The structured document type determination system according to claim 3, further comprising a structured document feature information database editing means for editing the list of features stored in said structured document feature information database.

5. The structured document type determination system according to claim 3, further comprising a collection means for collecting structured documents by way of a network and for updating contents of said structured document database, a control means of starting said structured document type determination system in order to update contents of said sampled structured document database and to acquire a new optimum determination rule, a teacher data inputter database for storing information on one or more inputters who can input teacher data, a notification means for making a request of one or more teacher data inputters registered in said teacher data inputter database for inputting of teacher data by way of said teacher data input means, and a previous determination result database for storing previous determination results acquired by said determination rule applying means according to a previous optimum determination rule, wherein said optimum determination rule deriving means makes a evaluation of the new optimum determination rule by comparing the previous determination results stored in said previous determination result database with new determination results acquired by said determination rule applying means according to the new optimum determination rule.

6. The structured document type determination system according to claim 5, wherein said teacher data input means acquires the contents of said sampled structured document database by way of the network, and stores input teacher data in said feature value and teacher data database by way of the network.

7. The structured document type determination system according to claim 5, wherein said control means starts said structured document sampling means every time it is instructed by a manager or at predetermined intervals so as to update the contents of said sampled structured document database.

8. The structured document type determination system according to claim 5, wherein said control means checks whether or not all data are provided in said feature value and teacher data database every time it is instructed by a manager or at predetermined intervals, and starts said notification means when all data are provided in said feature value and teacher data database.

9. The structured document type determination system according to claim 5, wherein said notification means provides an instruction to input teacher data for all of part of structured documents stored in said sampled structured document database for one or more teacher data inputters registered in said teacher data inputter database.

10. The structured document type determination system according to claim 5, wherein said optimum determination rule deriving means determines whether either the previous optimum determination rule or the new optimum determination rule has a high degree of accuracy by comparing the new determination results stored in said determination result database with the previous determination results stored in said previous determination result database.

11. The structured document type determination system according to claim 5, wherein when there are different teacher data input by a plurality of teacher data inputters for a same structured document, said control means determines only one of them based on majority rule.

12. The structured document type determination system according to claim 5, wherein said collection means collects only structured documents that are classified into either one of the plurality of predetermined types according to the current optimum determination rule from the network, and stores them in said structured document database.

13. The structured document type determination system according to claim 1, wherein said determination rule creating means creates the determination rule by using a data mining tool.

14. The structured document type determination system according to claim 1, wherein the plurality of structured documents are Web pages.

15. The structured document type determination system according to claim 14, further comprising a specific site information database for storing a list of URLs (Uniform Resource Locators) of specific Web pages, wherein said feature value extraction means extracts a feature value associated with a link to each URL, which is included in the list stored in said specific site information database, from each Web page stored in said sampled structured document database.

16. The structured document type determination system according to claim 14, wherein the list of features stored in said structured document feature information database includes either one or plural ones of following features:

(1) A number of use of each of all tags which can constitute Web pages

(2) A number of use of each of all tags which can constitute Web pages and which includes each attribute

(3) A number of use of each of all tags which can constitute Web pages and which includes an attribute having a predetermined continuous value or discrete value

(4) A size of each Web page

(5) A size of display of each Web page

(6) Character code type used in each Web page

(7) A number of use of half-width kana characters

(8) A number of use of image characters (“

emoji”

)

(9) Image file format type

(10) Presence or absence of each predetermined character string pattern included in a URL which is an identifier of each Web page

(11) A length of the URL which is an identifier of each Web page

(12) A extension of the URL which is an identifier of each Web page

(13) A number of external links

(14) A number of internal links

17. The structured document type determination system according to claim 14, wherein the list of features stored in said structured document feature information database includes presence or absence of a predetermined tag sequence.

18. The structured document type determination system according to claim 14, wherein the list of features stored in said structured document feature information database includes a number of link sources which are determined to be each Web page type and a number of link destinations which are determined to be each Web page type.

19. The structured document type determination system according to claim 14, wherein the list of features stored in said structured document feature information database includes presence or absence of change in contents of each Web page when access source information is changed.

20. The structured document type determination system according to claim 14, wherein the list of features stored in said structured document feature information database includes a number of links to each Web page stored in a specific site information database and a number of links from each Web page stored in said specific site information database.

21. The structured document type determination system according to claim 14, wherein the plurality of types include at least a Web page type intended for i-mode (registered trademark) mobile phones and a Web page type intended for personal computers.

22. A structured document type determination method comprising the steps of:

sampling a plurality of arbitrary structured documents from a structured document database for storing structured documents so as to create a sampled structured document database;

providing a list of features each of which is a measure to classify a plurality of structured document into a plurality of predetermined types and each of which is to be extracted from each of the plurality of structured documents;

by extracting a value of each of the plurality of features (referred to as a feature value from here on) from each of the plurality of structured documents stored in said sampled structured document database according to the list of features and by inputting teacher data which is a result of determining which one of the plurality of types each of the plurality of structured documents stored in said sampled structured document database is classified into, creating a feature value and teacher data database including the input teacher data and extracted feature values for each of the plurality of structured documents stored in said sampled structured document database;

by dividing said feature value and teacher data database into two portions, creating both a made-for-machine-learning feature value and teacher data database and a made-for-verification feature value and teacher data database;

creating a determination rule used for determining which one of the plurality of types a structured document is classified into based on said made-for-machine-learning feature value and teacher data database by using a data mining tool;

determining which one of the plurality of types each of a plurality of structured documents whose feature values and teacher data are stored in said made-for-verification feature value and teacher data database is classified into according to the determination rule so as to produce determination results;

making an evaluation of the determination rule by comparing the determination results with the teacher data stored in said made-for-verification feature value and teacher data database; and

selecting a tuning pattern from a list of tuning patterns used for tuning of the creation of the determination rule one by one so as to deliver the selected tuning pattern to said determination rule creating step, and repeating a series of processes, such as causing said determination rule creating step to create a determination rule again according to the selected tuning pattern, causing said determining step to make a determination of the type of each of the plurality of structured documents stored in said made-for-verification feature value and teacher data database again according to the created determination rule and causing said determination rule evaluation step to make an evaluation of the created determination rule, until the determination rule creation and the evaluation are completed for all the tuning patterns in said tuning pattern list, so as to derive an optimum determination rule from among a plurality of determination rules acquired during the above processes.