JP7290391B2 - Information processing device and program - Google Patents

Description

The present invention relates to an information processing apparatus and program.

In recent years, the need for microcontents has increased, and sales of microcontents such as books, magazines, and newspapers are expanding. However, content data such as books are often so-called unstructured data, and do not hold information indicating what each element in the content is. Therefore, it is necessary to perform the work of micro-contents that extracts each micro-content from the content data. In many cases, creating microcontents is done manually, and there is the problem of high costs.

For example, Patent Document 1 discloses a document image processing apparatus or the like that converts a document image into micro-contents. A document image processing apparatus and the like that generate document data described in a markup language have been disclosed.

JP-A-2002-41497

However, the invention according to Patent Literature 1 requires the designer to design rules for assigning tags and attribute values in advance, which is not necessarily efficient.

An object of one aspect is to provide an information processing apparatus and the like that can efficiently manage content.

In one aspect, the information processing device includes an acquisition unit that acquires teacher information that associates a sample of unstructured data consisting of at least one or more elements with tag information that consists of meta information that defines each element for each sample. a feature extracting unit for extracting features of each element of the sample based on features of the layout of each element for the sample indicated by the acquired teacher information and features of data in each element; A setting unit for setting rules for identifying the tag information according to the characteristics of the elements, based on the characteristics of the elements and the tag information indicated by the teacher information , and acquiring content, which is unstructured data. a content acquisition unit, an extraction unit for extracting each element from the acquired content, an addition unit for adding the tag information to the element by referring to the rule, and a price for the entire content from the content an accompanying information acquisition unit that acquires accompanying information including information about the A generating unit that structures and generates structured data in which the associated information is stored in association with each of the elements, and an output unit that refers to the generated structured data and outputs each element and the price of each element. and

In one aspect, in the information processing device, the samples and contents are document data consisting of text or images, the feature extraction unit extracts information related to the format or layout of the elements, and the setting unit The rule is set to indicate the correspondence relationship between the format or layout of the element and the tag information.

In one aspect, in the information processing device, the acquisition unit acquires a plurality of pieces of the teacher information, and the setting unit learns the correspondence relationship between the feature of the element and the tag information from each piece of the teacher information. and generating an identifier for identifying the tag information corresponding to the element.

In one aspect, the information processing device includes a reception unit that receives an output request for the content corresponding to the element output by the output unit, and when the output request is received, the output unit It is characterized by outputting the content.

In one aspect, an information processing apparatus includes a collection unit that collects data related to a web page via a network, and a page extraction unit that extracts the web page having content that matches the element from the collected web pages. and a notification unit for notifying the extracted Web page.

In one aspect, the program obtains teacher information that associates a sample of unstructured data consisting of at least one or more elements with tag information consisting of meta information that defines each element for each sample, and obtains the above extracting the features of the elements of the sample based on the features of the layout of the elements and the features of the data in the elements for the sample indicated by the teacher information; Based on the tag information indicated by the information, a rule for identifying the tag information according to the characteristics of the element is set, content that is unstructured data is acquired, and each element is extracted from the acquired content. assigning the tag information to the element with reference to the rule; acquiring accompanying information including information on the price of the entire content from the content; calculating a price, structuring the content based on the tag information attached to each of the elements, generating structured data in which the accompanying information is stored in association with each of the elements, and storing the generated structured data It is characterized by causing a computer to execute a process of referring to and outputting each element and the price of each element .

In one aspect, content management can be made more efficient.

1 is an explanatory diagram showing an overview of an information processing system; FIG. It is a block diagram which shows the structural example of a server. FIG. 4 is an explanatory diagram showing an example of a record layout of a structured table; FIG. FIG. 10 is an explanatory diagram relating to a structuring rule setting process; FIG. 10 is an explanatory diagram regarding structuring processing; 4 is a flow chart showing an example of a processing procedure executed by a server; FIG. 7 is a schematic diagram showing a configuration example of an information processing system according to Embodiment 2; FIG. 10 is an explanatory diagram showing an overview of Embodiment 2; FIG. 11 is an explanatory diagram of a process of calling up original data according to the second embodiment; 9 is a flowchart showing an example of a processing procedure executed by an information processing system according to Embodiment 2; FIG. 11 is an explanatory diagram showing an overview of Embodiment 3; 10 is a flow chart showing an example of a processing procedure executed by a server according to Embodiment 3; It is a functional block diagram which shows operation|movement of the server of the form mentioned above.

Hereinafter, the present invention will be described in detail based on the drawings showing its embodiments.
(Embodiment 1)
FIG. 1 is an explanatory diagram showing an outline of an information processing system. In this embodiment, an information processing system that creates a data archive of micro-contents will be described as an example. The information processing system includes an information processing device 1 and a terminal 2 . The information processing device 1 and the terminal 2 are connected for communication via a network N such as the Internet.

The information processing device 1 is a device that performs various types of information processing and information transmission/reception, and is, for example, a server device, a personal computer, a multifunctional terminal, or the like. In the present embodiment, the information processing device 1 is assumed to be a server device, and is replaced with the server 1 below for the sake of simplicity. The server 1 acquires digital content, which is unstructured data, such as document data such as books, newspapers, and magazines, extracts micro content from the digital content, and performs processing to generate structured data.

The terminal 2 is a client terminal that communicates with the server 1, and is a terminal device operated by an administrator who performs micro-content work. The server 1 acquires digital content from the terminal 2 and creates a data archive according to a request from the terminal 2 .

FIG. 2 is a block diagram showing a configuration example of the server 1. As shown in FIG. The server 1 includes a control section 11 , a main storage section 12 , a communication section 13 and an auxiliary storage section 14 .
The control unit 11 has an arithmetic processing unit such as one or more CPU (Central Processing Unit), MPU (Micro-Processing Unit), etc. By reading and executing the program P stored in the auxiliary storage unit 14, It performs various information processing, control processing, and the like related to the server 1 . The main storage unit 12 is an SRAM (Static Random Access Memory), a DRAM (Dynamic Random Access Memory), a flash memory, or the like, and temporarily stores data necessary for the control unit 11 to perform arithmetic processing. The communication unit 13 includes a processing circuit and the like for performing processing related to communication, and transmits and receives information to and from the terminal 2 and the like.

The auxiliary storage unit 14 is a large-capacity memory, hard disk, or the like, and stores programs P and other data necessary for the control unit 11 to execute processing. The auxiliary storage unit 14 also stores a structured table 141 and a content DB 142 . The structured table 141 defines structured rules for converting digital content, which is unstructured data, into structured data. The content DB 142 is a database that stores structured data in which digital content is structured.

Incidentally, the auxiliary storage unit 14 may be an external storage device connected to the server 1 . Moreover, the server 1 may be a multi-server consisting of a plurality of computers, or may be a virtual machine virtually constructed by software.

FIG. 3 is an explanatory diagram showing an example of the record layout of the structured table 141. As shown in FIG. The structured table 141 includes an element name column and a rule column. The element name column stores the element name (tag information described later) of each element forming the document page, such as "title", "subtitle", and "text". The rule string stores information characterizing the element corresponding to each element name in association with the element name. For example, the rule string stores data such as the font, character size, and layout (coordinate values) of each element.

FIG. 4 is an explanatory diagram of a structuring rule setting process. FIG. 4 illustrates how the server 1 extracts the basic elements that make up the sample document from a sample of unstructured data, specifically a sample document made up of text and images, and learns the features. .
The server 1 acquires sample documents for teachers from the terminal 2 and sets (learns) structuring rules based on the sample data. The sample document is unstructured document data, such as a PDF (Portable Document Format, registered trademark) file. The server 1 sets structuring rules for structuring unstructured data based on one sample document.

For example, the server 1 acquires teacher information in which a document sample, which is unstructured data, is associated with the correct value of the tag information of each element included in the sample. Elements of unstructured data are data obtained by dividing the original data into predetermined areas. is an element. The tag information is meta information that defines each element, and is an element name or attribute value tagged to each element in an XML (Extensible Markup Language) file. In this embodiment, the server 1 acquires information on the element name of each element included in the sample as teacher information. For example, as shown in FIG. 4, if the element corresponds to the title of the document, the element name "title" is acquired, and if the element corresponds to the subtitle, the element name "subtitle" is acquired. In this way, the server 1 acquires from the terminal 2 the teacher information that holds the correct value of the element name of each element for one document sample.

The server 1 extracts each element such as the title, subtitle, text, and figure from the document sample. The server 1 then extracts features of each element extracted from the sample. Specifically, the server 1 extracts features such as the format of the text in each element and the layout of each element. For example, the server 1 performs character recognition on the text written in the rectangular area shown in FIG. 4, and determines the font, character size, etc. of the text. The server 1 also determines the coordinate values of the area occupied by each element within the document page based on the position and range of each rectangular area.

The server 1 associates the feature of each element extracted above with the element name (tag information) of each element indicated by the teacher information, and stores them in the structured table 141 . As a result, the server 1 sets a structuring rule for identifying which element name each element corresponds to, according to the tendency (feature) of each element such as format and layout.

In the above description, format and layout are mentioned as information characterizing each element, but the present embodiment is not limited to this. For example, the server 1 may extract and learn the lines that make up the table as features so that the table included in the document can be identified. Thus, element characteristics are not limited to formatting and layout.

In the above description, for the sake of simplification, rules are set by learning the characteristics of each element from one document sample, but the present embodiment is not limited to this. The server 1 may perform machine learning to learn the correspondence relationship between element features and tag information from a plurality of pieces of teacher information, and generate model data (identifier) for identifying tag information from the element features. That is, the server 1 extracts the feature amount of each element from each of a plurality of teacher samples, and compares it with the correct value of the tag information of each element. The server 1 performs comparison processing for all teacher samples, and performs processing for learning from a plurality of samples, for example, what kind of parameter the feature amount of the element corresponding to the title is. Through this process, the server 1 generates a discriminator for discriminating tag information from the feature amount of the element, and stores it in the auxiliary storage unit 14 . Machine learning enables the server 1 to identify each element more accurately.

FIG. 5 is an explanatory diagram of the structuring process. The server 1 performs processing for converting digital content, which is unstructured data, into structured data based on the structuring rules set above. Specifically, the server 1 extracts each element such as a title, subtitle, text, and diagram from the target document, and analyzes the format, layout, and the like of each element. The server 1 then refers to the structured table 141 to identify tag information corresponding to the format, layout, etc. of each element. For example, when extracting a text portion corresponding to a title in a document, the server 1 recognizes that the text is the title based on the font, character size, layout, etc. of the text. The server 1 similarly processes each element of the digital content and identifies each element.

The server 1 assigns tag information (element name) to each element, and generates structured data in which each element is layered based on the tag information. For example, the server 1 generates a text file in XML format. For example, when the server 1 extracts the text element of the title of the document, it assigns the element name "title" to the element and stores it in the text file. Similarly, the server 1 assigns an element name to each element of the subtitle, text, etc. of the document, and stores them in a file. When extracting an image (drawing) from a document, the server 1 associates the extracted image with the file name of the text file and stores it in the image folder. As shown in FIG. 5, the server 1 hierarchizes each element in the text file by linking one element (for example, page number) and storing other elements. As a result, the server 1 generates structured data by structuring the digital content (document), which is unstructured data. The server 1 stores the generated structured data in the content DB 142 .

Moreover, the server 1 may not only identify the element name of each element and store it in the structured data, but may also associate accompanying information associated with the digital content with each element and store it in the structured data. The accompanying information is, for example, information such as the author name, price, and publisher of the content. For example, the server 1 sets rules for extracting text elements such as author names, prices, publishers, etc. from the covers of sample documents based on teacher information, in the same way as the rules relating to element names. When the server 1 acquires a cover image (not shown) of a document related to digital content, it extracts various information from the cover. When storing each element extracted from the digital content in a file, the server 1 stores accompanying information in association with each element. For example, the server 1 describes the information as an attribute value in the tag of each element. This allows the server 1 to give detailed meaning to each element.

In the above description, the server 1 mechanically extracts the accompanying information such as the name of the author and the price from the content data. For example, when the administrator operates the terminal 2 to transfer the content data to the server 1, the information such as the name of the author and the price is input and transferred together. That is, the server 1 only needs to be able to acquire information associated with the content, and the information may be acquired automatically from the content data or through manual input.

The server 1 similarly processes each digital content transferred from the terminal 2, and converts each content, which is unstructured data, into structured data. In this way, the server 1 assigns meaning to each element extracted from digital content and creates a digital archive of micro content. By setting (learning) structuring rules in advance from teacher data, the server 1 automatically identifies what each element included in the digital content is and generates structured data. can be done. As a result, it is possible to improve the efficiency of the work of creating micro-contents.

FIG. 6 is a flow chart showing an example of a processing procedure executed by the server 1. As shown in FIG. Based on FIG. 6, the contents of processing executed by the server 1 will be described.
The control unit 11 of the server 1 acquires teacher information including a sample of unstructured data and the correct value of the tag information of each element included in the sample (step S11). The tag information is meta information that defines each element, such as an element name or attribute value in an XML file. For example, the control unit 11 acquires teacher data in which the element name of each element of the unstructured data is known. The control unit 11 extracts features of each element included in the sample (step S12). For example, the control unit 11 extracts information on the format and layout of each element included in the sample document.

The control unit 11 identifies the tag information to be added according to the features of the elements included in the unstructured data, based on the extracted features of each element and the correct value of the tag information of each element indicated by the teacher information. A structuring rule is set (step S13). Specifically, the control unit 11 associates the characteristics such as the format and layout of each element extracted in step S 12 with the element name of each element indicated by the teacher information, and stores them in the structured table 141 .

The control unit 11 acquires digital content, which is unstructured data, from the terminal 2 (step S14). Digital content is unstructured document data, such as PDF files.

The control unit 11 extracts each element included in the digital content acquired in step S14 (step S15). Then, the control unit 11 refers to the structuring rule set in step S13 and assigns tag information to each extracted element (step S16). For example, the control unit 11 assigns an element name to each extracted element according to its characteristics such as format and layout. Also, for example, the control unit 11 assigns accompanying information such as the author, price, and publisher extracted from the cover image of the digital content (document) as attribute values of each element.

The control unit 11 generates structured data in which the digital content is structured based on the tag information attached to each element (step S17). For example, the control unit 11 generates an XML format file. The control unit 11 hierarchically stores each element according to the element name given in step S16, and generates structured data. Also, for example, the control unit 11 describes accompanying information of the content as an attribute value of each element in the tag information and stores it in the structured data. The control unit 11 stores the generated structured data in the content DB 142 (step S18), and ends the series of processes.

In the above description, it is assumed that the content to be analyzed is a document, but the content to be analyzed may be unstructured data, such as audio data.

In addition, although the XML file is generated in the above description, it is of course possible to generate a file in a format such as HTML or SGML.

As described above, according to the first embodiment, the server 1 extracts the characteristics of each element from the teacher sample data, and sets structured rules associated with the tag information. As a result, even when the server 1 acquires digital content whose structure is unknown, the server 1 can refer to the set structuring rule to automatically identify each element of the content and attach tag information. Since the server 1 automatically sets the rules from the teacher data, the administrator does not need to consider the content of the rules through trial and error. As a result, it is possible to reduce the work load in creating micro-contents and improve the efficiency of content management.

Further, according to the first embodiment, by identifying the attribute of each element based on the format or layout of each element of the document, the server 1 can give appropriate attributes to each element.

Further, according to Embodiment 1, accuracy can be improved by performing machine learning for extracting the correspondence between the feature of each element and the tag information from each of a plurality of teacher data.

Further, according to the first embodiment, by structuring the digital content based on the attribute of each identified element, it is possible to facilitate the use of the micro content.

(Embodiment 2)
In the first embodiment, a form of extracting each element from digital contents such as books and creating a digital archive of micro contents has been described. In this embodiment, a form of selling micro contents using a created digital archive will be described. In addition, the same code|symbol is attached|subjected about the content which overlaps with Embodiment 1, and description is abbreviate|omitted.

FIG. 7 is a schematic diagram showing a configuration example of an information processing system according to the second embodiment. The information processing system according to this embodiment includes a sales management server 3 . The sales management server 3 is a server device that manages an EC site that sells microcontents, and refers to the data in the content DB 142 generated by the server 1 to generate and output an EC site screen that displays each microcontent. , accepting applications for purchase of micro-contents, etc.

FIG. 8 is an explanatory diagram showing an overview of the second embodiment. The left side of FIG. 8 conceptually illustrates how the server 1 converts digital content into structured data and stores the structured data in the content DB 142, as described in the first embodiment. In this embodiment, the server 1 uses the structured data to output each element, that is, micro content to the sales management server 3 . The sales management server 3 outputs the data of each micro content to the EC site as shown on the right side of FIG.

Specifically, the sales management server 3 uses each element extracted from the digital content, which is the original data, as the material of the Web page, generates a Web screen in which each element is rearranged, and provides the EC site user with a Web screen. output to the client terminal of For example, as shown in FIG. 8, the sales management server 3 rearranges elements such as images extracted from the original data, image captions, and titles, and displays them on the Web screen.

Moreover, the server 1 not only outputs each element (micro-content) to the sales management server 3, but also outputs accompanying information associated with each element, that is, information such as author name and price. Note that the server 1 acquires only the price of the entire digital content, which is the original data, and does not acquire the price of each individual microcontent. Calculated by

P=α(A/N)+β (1)

A is the price of the entire digital content, N is the total number of elements included in the digital content, and α and β are price adjustment parameters. α and β are determined according to, for example, the amount of data of each element. The server 1 calculates the price of each element from the price of the entire content based on Equation (1). Specifically, by dividing the price of the entire content by the number of elements and adjusting the price according to the amount of data of the corresponding element, the price of each micro content is calculated.

In addition to the price calculated above, the sales management server 3 acquires accompanying information such as the author name of the content from the server 1 and outputs it on the Web screen. The sales management server 3 accepts a purchase application for each micro-content displayed on the Web screen via the client terminal of the user of the EC site. The sales management server 3 determines a billing fee based on the price calculated above for each microcontent sold, and bills the user.

FIG. 9 is an explanatory diagram of the original data call processing according to the second embodiment. The sales management server 3 not only sells microcontents extracted from documents such as books, newspapers, and magazines, but also sells documents from which microcontents are extracted, that is, digital content itself. For example, when the sales management server 3 accepts a designation input for microcontents (images) on the screen shown on the left side of FIG. 9, it transitions to the screen shown on the right side of FIG. 9, and outputs information about the original data of the designated microcontents. do.

Specifically, when receiving a call request (output request) for original data from a client terminal, the sales management server 3 makes an inquiry to the server 1 . The server 1 receives the inquiry, reads the information of the digital content, which is the original data, from the content DB 142 and outputs it to the sales management server 3 . The sales management server 3 acquires the original data, generates a web screen showing the information of the original data, and outputs it to the client terminal. For example, as shown in FIG. 9, the sales management server 3 outputs the cover page of the original data, bibliographic information (associated information), recorded micro-content information, and the like. For example, the sales management server 3 accepts a purchase application for the entire digital content, which is the original data, via the screen.

10 is a flowchart illustrating an example of a processing procedure executed by an information processing system according to Embodiment 2. FIG. Processing contents executed by the server 1 and the sales management server 3 will be described with reference to FIG.
The control unit 11 of the server 1 reads structured data of digital content from the content DB 142 (step S201). Specifically, the control unit 11 collects the data of each element (micro-content) extracted from the digital content and the associated information stored as the attribute value of the element, that is, information such as the author of the content and the price of the content as a whole. read out. The control unit 11 calculates the price of each element from the read price of the entire content (step S202). Specifically, the control unit 11 calculates the price of each micro content from the price of the entire content according to formula (1). The control unit 11 outputs each element and accompanying information of the digital content corresponding to each element to the sales management server 3 (step S203). For example, the control unit 11 outputs information such as the author and the price of the microcontent in addition to the data of each microcontent.

The sales management server 3 acquires element data from the server 1, generates and outputs a Web screen related to the EC site (step S204). For example, the sales management server 3 generates and outputs a Web screen displaying each microcontent and accompanying information of each microcontent, as described above. The sales management server 3 determines whether or not an output request for the digital content, which is the original data of the elements, has been received from the client terminal (step S205). When determining that the output request has not been received (S205: NO), the sales management server 3 terminates the series of processes. When determining that the output request has been received (S205: YES), the sales management server 3 transfers the output request to the server 1 (step S206).

The control unit 11 of the server 1 accepts the output request for the original data (step S207). When receiving the output request, the control unit 11 outputs the digital content, which is the original data, to the sales management server 3 (step S208). The sales management server 3 acquires the original data from the server 1, displays the original data on the web screen (step S209), and ends the series of processes.

In the above description, the EC site was explained as an example, but the server 1 only needs to be able to output each element in the document using structured data, and the application target of this embodiment is limited to the EC site. not.

As described above, according to the second embodiment, by referring to the structured data and outputting each element, it is possible to practically use the micro-contents.

Further, according to the second embodiment, by associating accompanying information of original data with each element, it is possible to enhance the convenience of using micro-contents.

Moreover, according to the second embodiment, by automatically calculating the price of each element from the price of the entire content, it is possible to calculate an appropriate price for each micro-content. In addition, it is not necessary for the seller to set the price of each microcontent individually, and the troublesome price calculation can be eliminated.

Moreover, according to the second embodiment, it is possible to promote the use of the entire digital content by using the micro content as an incentive.

(Embodiment 3)
In the present embodiment, a mode of performing crawling monitoring for checking unauthorized use of copyrighted content will be described.
FIG. 11 is an explanatory diagram showing an overview of the third embodiment. The server 1 performs a crawling process of collecting website information via the network N, and a process of monitoring whether or not the digital content stored in the content DB 142 is illegally used in the web pages of each site. I do.

For example, the server 1 periodically accesses each website on the Internet and collects the data of the web page of each site. Then, the server 1 determines whether or not there is a Web page in which each element extracted from the digital content, that is, the content matching the micro content is posted among the collected Web pages. If it is determined that there is a Web page on which matching content is posted, the server 1 notifies the administrator of the corresponding page. Specifically, the server 1 notifies the address information of the Web page and the corresponding portion within the page.

FIG. 12 is a flow chart showing an example of a processing procedure executed by the server 1 according to the third embodiment. Based on FIG. 12, the contents of processing executed by the server 1 in this embodiment will be described.
The control unit 11 of the server 1 accesses each website via the network N and collects data of web pages (step S301). The control unit 11 determines whether or not there is a Web page containing content matching elements stored in the content DB 142 from the collected Web pages (step S302). If it is determined that there is no corresponding web page (S302: NO), the control unit 11 terminates the series of processes.

If it is determined that there is a relevant web page (S302: YES), the control unit 11 extracts the relevant web page (step S303). Specifically, the control unit 11 extracts the address information and the like of the web page, and also extracts the corresponding portion where the matching content is posted on the web page. The control unit 11 notifies the administrator of the extracted web page (step S304), and ends the series of processes.

As described above, according to the third embodiment, unauthorized use of a copyrighted work can be monitored for each micro-content.

(Embodiment 4)
FIG. 13 is a functional block diagram showing the operation of the server 1 of the form described above. When the control unit 11 executes the program P, the server 1 operates as follows. The acquisition unit 131 acquires teacher information including a sample of unstructured data and tag information defining each element included in the sample. A feature extractor 132 extracts features of each element of the sample. The setting unit 133 sets a rule for identifying the tag information according to the feature of the element, based on the feature of each extracted element and the tag information indicated by the teacher information. The content acquisition unit 134 acquires content that is unstructured data. The extraction unit 135 extracts each element from the acquired content. The adding unit 136 refers to the rule and adds the tag information to the element.

The fourth embodiment is as described above, and the other parts are the same as those of the first to third embodiments.

The embodiments disclosed this time are illustrative in all respects and should not be considered restrictive. The scope of the present invention is indicated by the scope of the claims rather than the meaning described above, and is intended to include all modifications within the scope and meaning equivalent to the scope of the claims.

1 server (information processing device)
REFERENCE SIGNS LIST 11 control unit 12 main storage unit 13 communication unit 14 auxiliary storage unit P program 141 structured table 142 content DB
2 terminal 3 sales management server

Claims (6)

an acquisition unit that acquires teacher information that associates a sample of unstructured data consisting of at least one or more elements with tag information that consists of meta information that defines each element of each sample;
a feature extraction unit that extracts features of each element of the sample based on features of the layout of the elements and features of data in each element for the sample indicated by the acquired teacher information;
a setting unit for setting a rule for identifying the tag information according to the characteristics of the elements, based on the characteristics of the extracted elements and the tag information indicated by the teacher information ;
a content acquisition unit that acquires content that is unstructured data;
an extraction unit that extracts each of the elements from the acquired content;
a granting unit that refers to the rule and grants the tag information to the element;
an accompanying information acquisition unit that acquires accompanying information including price information for the entire content from the content;
a calculation unit that calculates the price of each of the elements from the price of the entire content;
a generation unit that structures the content based on the tag information assigned to each of the elements and generates structured data that stores the accompanying information in association with each of the elements;
an output unit that refers to the generated structured data and outputs each element and the price of each element;
An information processing device comprising: The samples and contents are document data consisting of text or images,
The feature extraction unit extracts information related to the format or layout of the element,
The information processing apparatus according to claim 1 , wherein the setting unit sets the rule indicating the correspondence between the format or layout of the element and the tag information. The acquisition unit acquires a plurality of pieces of teacher information,
The setting unit learns a correspondence relationship between the feature of the element and the tag information from each of the teacher information to generate an identifier for identifying the tag information corresponding to the element. Item 3. The information processing device according to Item 1 or 2 . a receiving unit that receives an output request for the content corresponding to the element output by the output unit;
4. The information processing apparatus according to any one of claims 1 to 3 , wherein the output unit outputs the content when an output request is received. a collection unit that collects data related to web pages via a network;
a page extracting unit that extracts the web page having content that matches the element from the collected web pages;
5. The information processing apparatus according to any one of claims 1 to 4 , further comprising: a notification unit that notifies the extracted web page. Acquiring teacher information that associates a sample of unstructured data consisting of at least one or more elements with tag information consisting of meta information that defines each element for each sample,
extracting the features of each element of the sample based on the features of the layout of each element and the features of the data in each element for the sample indicated by the acquired teacher information;
setting a rule for identifying the tag information according to the feature of the element based on the extracted feature of each element and the tag information indicated by the teacher information;
Get content that is unstructured data,
extracting each element from the acquired content;
Referencing the rule, giving the tag information to the element,
Acquiring accompanying information including price information for the entire content from the content;
calculating the price of each of the elements from the price of the entire content;
Structuring the content based on the tag information assigned to each of the elements, and generating structured data in which the accompanying information is stored in association with each of the elements;
A program that causes a computer to execute a process of referring to the generated structured data and outputting each element and the price of each element .

Images