JP5877775B2 - Content management apparatus, content management system, content management method, program, and storage medium - Google Patents

Description

  The present invention relates to a content management apparatus, a content management system, a content management method, a program, and a storage medium. For example, the present invention relates to a technique for efficiently providing content features.

  In recent years, the use of ICT has advanced in the education field, and the need for effective use of digital content is increasing. For example, software such as CMS (Content Management System) that supports the use of content by teachers by sharing files of digital teaching materials and learning teaching plans within a school or between a plurality of schools (for example, see Non-Patent Document 1). Proposed. In such software, in order to share content effectively, it is necessary to give appropriate metadata to each content. For example, when searching for content effectively in a search using categories or texts, or when making a decision to select the content easily without checking all of the content, the category or keyword to which the content belongs Must be granted. Therefore, a function to display a metadata registration screen for the user (hereinafter also referred to as a user) to manually input a title, a school division, a grade, a subject, a keyword, etc. with respect to content registered in the CMS. There is something to have. In addition, a technique for automatically extracting characteristic words from text in content (for example, see Non-Patent Document 2) has been proposed.

Hitachi Solutions, Inc., MEANS file server slimming solution, http://hitachisoft.jp/products/means/slimserver/ Teruo Koyama, “Extracting Compound Words from Japanese Text”, Journal of Information Knowledge Society, vol.19, No.4, pp.306-315, 2010

However, it is necessary to input metadata for each content on the metadata registration screen while confirming the content of the content to be registered. For this reason, the registration work becomes enormous as the content increases. In addition, when assigning keywords that indicate the contents of content, it is best suited to search for important words in the text in the content, or to express content that is not in the text, under the subjectivity of the registrant. Because keywords are considered, there is a difference in vocabulary given by registered workers. Furthermore, in the content for education field, especially the diagrams used when teachers explain to students, many of them are created by using images etc. according to the teacher's class style. ) Is not necessarily a vocabulary sufficient to understand textual search and content.
The present invention has been made in view of such a situation, and provides a technique that enables a user to easily and accurately register digital contents in a shared server or the like even if the user has poor IT literacy. is there.

  In order to solve the above problems, in the present invention, the content management device is used to classify the content when registering the content in the storage device, and a plurality of types of content management information prepared in advance, The feature word information prepared in advance associated with each content information is stored, and the metadata of the content to be registered is stored based on the content management information and the feature word information stored in the storage device. Obtain and output as support information. Each content management information includes an explanatory text related to the content category. At this time, the content management apparatus calculates a keyword included in the content to be registered, calculates an appearance frequency of the extracted keyword in the description of the content management information, and determines a plurality of types based on the appearance frequency. Acquires feature word information associated with content management information having the highest score value obtained by scoring content management information and scoring processing, and outputs the feature word information as support information (metadata) And processing to execute.

According to the present invention, even if the user is not familiar with the IT technology, the user can register his / her content in a shared server or the like simply by assigning an appropriate keyword.
Further features related to the present invention will become apparent from the description of the present specification and the accompanying drawings. The embodiments of the present invention can be achieved and realized by elements and combinations of various elements and the following detailed description and appended claims.
It should be understood that the description herein is merely exemplary and is not intended to limit the scope of the claims or the application of the invention in any way.

It is a functional block diagram which shows the schematic structural example of the content management apparatus (content management system) by embodiment of this invention. It is a figure which shows the data structure example of a curriculum and a feature word. It is a figure which shows the data structure of content information. It is a figure which shows the example of a screen displayed by the registration content selection screen display part (109). It is an example of the content containing text information. It is a flowchart for demonstrating the whole outline | summary of the process performed in a content management apparatus. 5 is a flowchart for explaining details of a metadata estimation processing unit 111; It is a flowchart for demonstrating the process which scores the curriculum in the metadata estimation process part. 5 is a flowchart for explaining details of a metadata input screen display unit 112. It is a figure which shows the example of a screen displayed by the metadata input screen display part. It is a figure which shows the example of a screen displayed by the metadata input screen display part. It is a figure which shows the example of a screen displayed by the metadata input screen display part.

  DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. 1-12 is a figure which illustrates embodiment of this invention. In these drawings, parts denoted by the same reference numerals represent the same items, and the basic configuration and operation are the same. In addition, in embodiment of this invention, the apparatus, method, etc. which are used are examples, and of course, this invention is not limited to these.

  Furthermore, as will be described later, the embodiment of the present invention may be implemented by software running on a general-purpose computer, or may be implemented by dedicated hardware or a combination of software and hardware.

  In the following description, each information of the present invention will be described in a “table” format. However, the information does not necessarily have to be expressed in a data structure by a table, such as a data structure such as a list, a DB, a queue, or the like. It may be expressed as Therefore, “table”, “list”, “DB”, “queue”, etc. may be simply referred to as “information” to indicate that they do not depend on the data structure.

  In addition, when explaining the contents of each information, the expressions “identification information”, “identifier”, “name”, “name”, “ID” can be used, and these can be replaced with each other. It is.

  In the following, each process in the embodiment of the present invention will be described using “program” as a subject (operation subject). However, a program is executed by a processor and a process determined by a memory and a communication port (communication control device). Since it is performed while being used, the description may be made with the processor as the subject. Further, the processing disclosed with the program as the subject may be processing performed by a computer such as a management server or an information processing apparatus. Part or all of the program may be realized by dedicated hardware, or may be modularized. Various programs may be installed in each computer by a program distribution server or a storage medium.

<Content management system configuration>
FIG. 1 is a functional block diagram showing a schematic configuration of a content management system (content management device). In FIG. 1, the content management system 1 is shown as a single computer, but each component may be remotely arranged and connected via a network. In this case, for example, the content management system 1 may be configured by a client terminal device and a shared server (content management server device). In this case, the client terminal device used by the user who wants to register content is composed of a computer having the display device 106 and the input device 107, and components other than the display device 106 and the input device 107 are arranged on the shared server side. Is possible.

  The content management system 1 includes a curriculum DB 100, a feature word DB 101, a content information DB 102, a central processing unit 103, a program memory 104, and a data memory that stores data necessary for processing in the central processing unit (processor) 103. 105, a display device 106 for displaying data, an input device 107 for performing operations such as selecting a menu for the displayed data, and a file server 108 for storing contents in a file system ,have.

  The central processing unit 103 includes a registered content selection screen display unit 109, a content information extraction processing unit 110, a metadata estimation processing unit 111, and a metadata input screen display unit 112. In the case of this embodiment, it is configured by a computer, and the registered content selection screen display unit 109, the content information extraction processing unit 110, the metadata estimation processing unit 111, and the metadata input screen display unit 112 are all on the computer. Realized as part of the functionality of the program being executed. These programs are stored in the program memory 104 so that the central processing unit 103 reads them into the internal memory when executing the processing.

  The data memory 105 stores a curriculum (information) 113 read from the curriculum DB 100, a feature word (information) 114 read from the feature word DB 101, and content information 115 read from the content information DB 102. .

<Data structure>
FIG. 2 is a diagram illustrating a data structure of the curriculum 113 and the feature word 114 included in the data memory 105. For example, the curriculum 113 holds information on a plurality of documents in a curriculum that describes specific instruction contents in the education field as text information classified into a plurality of categories (FIG. 2A). The feature word 114 is a feature word in the education field associated with each curriculum, and holds information closely related to the subject of each curriculum (FIG. 2B). Each curriculum has at least one characteristic word. Specific examples of curriculum for the educational field include study guidance proposals issued by publishers of textbooks and educational institutions (such as learning cram schools).

  As shown in FIG. 2A, the curriculum 113 includes an ID 200, a school division 201, a grade 202, a subject 203, a subject 204, an explanatory note 205, and a score 206, and holds such information in the form of an array, for example. The ID 200 holds a value uniquely assigned to the curriculum 113. The school division 201 holds a character string of a category of a school division such as an elementary school, a junior high school, and a high school. The grade 202 stores a character string of a grade category having the school division 203 as an upper hierarchy. The subject 203 holds a category of subjects having the grade 202 as an upper hierarchy. The subject 204 holds a character string of the outline of each text further subdivided for each teaching content in a part of the text of the curriculum classified under the conditions of the school division 201, the school year 202, and the subject 203. The explanatory note 205 holds text corresponding to the subject 204. The score 206 holds a value indicating the degree to which the content of the registered content is included in the content of the explanatory note 205, and the initial value is 0. In the example shown in FIG. 2A, a sentence describing the content when a subject of a junior high school first grader teaches about the state of volcanic activity in science, the information defining the subject as “state of volcanic activity” is shown.

  As shown in FIG. 2B, the feature word 114 includes an ID 207, a related ID 208, a keyword 209, and an important flag 210, and holds such information in the form of an array, for example. The ID 207 holds a value uniquely assigned to the feature word 114. The related ID 208 indicates that any one ID 200 in the list of the curriculum 113 is held, and the feature word 114 is associated with the curriculum 113 in a multi: 1 relationship. The keyword 209 holds a character string such as a matter / word to be taught defined in the description 205 of the associated curriculum 113. The importance flag 210 holds “true” when the word of the keyword 209 is determined to be important for the registered content, and the initial value is “false”. In the present invention, the characteristic words are not necessarily the words (keywords) included in the text information included in the curriculum theme 204, the explanation sentence 205, and the user content. As will be described later, the user does not have to think about the keyword for the content he / she wants to register, but the feature word presented as metadata (higher-relevance words are highlighted in bold, etc.) The keyword of the content is selected from (presented). The characteristic words (consisting of a plurality of words) associated with each curriculum have keywords that are not included in the words (keywords) included in the text information included in the text information included in the curriculum theme 204, the explanatory text 205, and the user content. doing.

  FIG. 3 is a diagram illustrating a data structure of the content information 115 included in the data memory 105. This content information is information used when a user registers his / her content, and is managed in the data memory 105 in a data structure as shown in FIG. When the registration of the corresponding content is completed, the corresponding content information 115 is deleted from the data memory.

  As shown in FIG. 3, the content information 115 includes an ID 300, a file 301, a title 302, a school division 303, a grade 304, a subject 305, a content text 306, a content keyword 307, and a grant keyword 308. Indicates data information. The ID 300 stores a value uniquely assigned to the content information 115. The file 301 stores the file name of the content to be registered. The title 302 stores a character string indicating the title of the content. School division 303 shows the category of the school division to which the content to register corresponds. The grade 304 indicates the category of the grade to which the content to be registered corresponds. A subject 305 indicates a category of a subject corresponding to the content to be registered. The content text 306 stores text information in the content. In the content keyword 307, keywords extracted from the content text 306 are stored in an array format. The assigned keyword 308 suggests a keyword estimated in the metadata estimation process described later, and the keyword selected by the user is stored in an array format. Note that the initial values are all null except for the ID 300 and the file 301.

<Registered content selection screen>
FIG. 4 is a diagram illustrating an example of a registered content selection screen that is processed by the registered content selection screen display unit 109 of FIG. 1 and displayed on the display device 106.

  As shown in FIG. 4, the registered content selection screen has a school category designation menu 400 that is a pull-down menu, a file path input form 401, a registration button 402, and an end button 403. The user designates the school division corresponding to the content to be registered from the school division designation menu 400. The school category designation menu 400 holds values designated in advance as a list of school categories such as “elementary school”, “junior high school”, and “high school”.

  Next, the user inputs the file path of the content to be registered, which is stored in the HDD or file server 108 (not shown), and presses the registration button 402. Thereby, the registration process of the metadata of the content is started.

  The content file to be registered is a file including text information such as Word, Excel, PowerPoint (registered trademark) file or Flash file provided by Microsoft (registered trademark). As a content file to be registered, FIG. 5 shows an example of PowerPoint (registered trademark) including text information. The metadata registration process will be described later in detail with reference to FIG. When the user presses the end button 403, the processing in the content management apparatus is ended.

<Outline of metadata registration process>
FIG. 6 is a flowchart for explaining an outline of processing performed in the metadata registration processing. This flowchart shows processing for inferring metadata for a content file designated by the user on the registered content selection screen (FIG. 4) and displaying it as a candidate on the metadata registration screen (FIG. 10). Then, the metadata input by the user is stored in a storage device (not shown) (for example, a storage device on the shared server side).

  In FIG. 6, first, the content information extraction processing unit 110 stores, as the content information 115, the school division selected by the user in the school division 303, the file path in the file 301, and the text in the content in the content text 306 ( Step 600).

  Next, the content information extraction processing unit 110 acquires a curriculum 113 having the same values as the school division 303, the grade 304, and the subject 305 in the content information 115 (step 601). However, members having null in the school information 303, grade 304, and subject 305 of the content information 115 are not included in the acquisition conditions. That is, when the school division 303 is “junior high school” and the grade 304 and the subject 305 are null (that is, in the case of the first metadata registration processing, not at the time of recalculation described later, the user selects the registered content selection screen (FIG. Only the school division 303 designated by 4) is known), and all curriculums 113 whose registered school division 201 is “junior high school” are acquired.

  Then, the metadata estimation processing unit 111 estimates a grade, a subject, and a related keyword corresponding to the content from the file path and the text in the content (step 602). The process for step 602 will be described later in detail with reference to FIG.

  Subsequently, the metadata input screen display unit 112 displays a metadata input screen that reflects the estimation result of the metadata of the content (step 603). The process for step 603 will be described later in detail with reference to FIG.

  In addition, when the user inputs or changes any of the title, grade, and subject values on the screen displayed by the processing of FIG. 9, in order to re-estimate the metadata considering the input information, The process returns to step 601 (step 604). If the user does not enter or change any value of title, grade, or subject on the screen displayed in the process of step 604, the metadata registration process ends and the registered content selection in FIG. Return to the screen.

<Details of Metadata Estimation Processing Unit (Step 602)>
FIG. 7 is a flowchart for explaining details of the metadata estimation processing unit 111 in step 602 of FIG. This flowchart shows a process of extracting a keyword from a content text, and further inferring which subject the registered content corresponds to in the curriculum using the keyword. The feature word associated with the curriculum indicating the highest score is a metadata candidate for the content.

  In FIG. 7, first, the metadata estimation processing unit 111 performs morphological analysis on the file 301 and the content text 306 in the content information 115 (step 700).

  Next, the metadata estimation processing unit 111 checks whether the title 302 in the content information 115 is null (step 701).

  If it is determined in step 701 that the title 302 is null, the metadata estimation processing unit 111 extracts keywords from the morphological analysis result in step 700 (step 703).

  If it is determined in step 701 that the title 302 is not null, the metadata estimation processing unit 111 performs a morphological analysis on the character string of the title 302 (step 702), and determines the keyword from the morphological analysis results in the steps 700 and 702. Extraction is performed (step 703). Here, the keyword means a group of words having a noun role included in the content text. For example, in the character string “light refraction”, there are three patterns of keywords “light”, “refraction”, and “light refraction”. The character string “function of convex lens” has four patterns of keywords “convex”, “lens”, “convex lens”, and “function of convex lens”. In step 703, a keyword is extracted from the character string by combining such a series of nouns and verbs with a suffix. Various existing techniques can be applied to the method of extracting keywords. As a typical technique, for example, a morphological analysis is performed on a target character string for keyword extraction, and a concatenation according to the type of part of speech of extracted words is regarded as a keyword (for example, non-patent document 1). Basically, it is a technique that regards a character string including one or more nouns as a keyword, and is generally used. Many techniques for analyzing extracted keywords in more detail and improving the keyword extraction accuracy have been proposed. In this embodiment, such a keyword extraction technique is used.

  Subsequently, the metadata estimation processing unit 111 acquires a morpheme in the content text whose part of speech is a verb, normalizes it to a prototype, and adds it to the content keyword 307 (step 704). For example, when there is a character string “Visible”, “Visible” can be acquired as a verb from this, and the prototype becomes “visible”.

  Then, the metadata estimation processing unit 111 estimates which curriculum 113 the registered content corresponds to by scoring using the content keyword 307 (step 705). Details of the processing in step 705 will be described later with reference to FIG.

<Details of Curriculum Scoring Processing (Step 705)>
FIG. 8 is a flowchart for explaining the details of the curriculum scoring process in step 705 of FIG. This flowchart shows a process for calculating which document of the curriculum classified into categories such as grades and subjects prepared in advance as a dictionary in the contents to be registered. A score is calculated by using the appearance frequency of the content keyword 307 in each curriculum 113 with respect to the degree of which document in the curriculum matches the content. The score is calculated by multiplying and multiplying the frequency of each word in the content keyword 307 appearing in the explanatory sentence 205 and the frequency of the curriculum having the word in the content keyword 307 in the explanatory sentence 205. The frequency at which each word in the content keyword 307 appears in the explanatory text 205 is that the content keyword is often used in the explanatory text when the content explained in the curriculum matches the content. The higher the total appearance frequency is, the more the content is matched with the curriculum. The frequency of the curriculum having each word in the content keyword 307 in the description sentence 205 is assumed to be “general words” in all curriculum documents for words appearing in more curriculums 113. The higher the index, the less effective the word is in identifying the corresponding curriculum. In other words, this is a measure for preventing the score value of a too general word from being increased. Based on these two frequencies, the score of each curriculum is calculated, and the curriculum corresponding to the content to be registered is estimated.

  In FIG. 8, first, the metadata estimation processing unit 111 acquires one word (hereinafter referred to as i) as a processing target from the content keyword 307 (step 800).

  Next, the metadata estimation processing unit 111 initializes a variable total_frequency for storing the appearance frequency of i in the explanatory text 205 of all the curriculums 113 stored in the memory with 0 (step 801).

  Further, the metadata estimation processing unit 111 acquires one curriculum from the curriculum 113 stored in the memory (step 802).

  The metadata estimation processing unit 111 then counts up the number of occurrences of i in the acquired curriculum description (step 803), and adds the number of appearances to total_frequency (step 804). Note that when the number of occurrences of i in the curriculum explanation text in step 803 is tabulated, morphological analysis is performed on the curriculum explanation text, and words corresponding to the verbs are normalized to the prototype.

  The metadata estimation processing unit 111 confirms whether or not the processing in steps 803 and 804 has been performed for all the curriculums on the memory (step 805). If all the curriculums in the memory have not been processed in step 805, the process returns to step 802 to calculate the appearance frequency of i for the unprocessed curriculum, and the next curriculum is processed.

  When all the curriculums in the memory are processed in step 805, the metadata inference processing unit 111 divides the number of all curriculums in the memory by the number of curriculums including one or more i. Is stored in the variable curriculum_frequency (step 806).

  Further, the metadata estimation processing unit 111 calculates the logarithm of curriculum_frequency (hereinafter referred to as IDF (i)) with the value x specified in advance as a base (step 807). Here, IDF (i) is a value (first evaluation value) indicating how commonly i is a word used in the document of the entire curriculum. Indicates that the word is used only in the curriculum.

  Next, the metadata estimation processing unit 111 acquires one curriculum from the curriculum 113 stored on the memory (step 808).

  Then, the metadata estimation processing unit 111 divides the appearance frequency of i in the curriculum by total_frequency (hereinafter referred to as TF (i)) (step 809). The processing in step 809 compares the frequency of i occurrences in each curriculum by the number of occurrences of i in all the curriculums in the memory, thereby comparing the relative frequencies among curriculums including i (second evaluation value). ). In addition, there is an effect of measuring normalization with the number of appearances of words other than i in the content keyword. For example, it is assumed that there are curriculum A and curriculum B in the science of junior high school and the content keywords “water”, “sun”, and “plant”. Curriculum A has a description of instruction content regarding refraction of light, and Curriculum B has a description of instruction content regarding the structure of a plant body. The description of the content of the curriculum A explains the phenomenon of refraction when light such as the sun enters the water from the air, and the curriculum B explains the structure of the body that takes in water from plant photosynthesis and roots. doing. In addition, the number of appearances of “water” in curriculum A is 19, the number of appearances of “sun” is 1, the number of appearances of “plants” is 0, the number of occurrences of “water” in curriculum B is 3, "Appears twice, and" plant "appears five times. Although the curriculum to which the content to be registered corresponds to B, when compared only with the simple total number of appearances, curriculum A is 20 times and curriculum B is 10 times, and curriculum A is the content to be registered. There may be a case where it is erroneously guessed that this is the case. Then, at the time when the content keyword includes “water”, it becomes easy to erroneously guess the curriculum A even for content with different contents. To avoid this case, divide the number of occurrences of each word by the number of occurrences of each word, and keep the comparison of the frequency between curriculums while keeping the score of words that are too frequent compared to other words. be able to. In this case, since the frequency of occurrence of “water” in curriculum A and curriculum B is 22 times, “sun” is 3 times, and “plant” is 5 times, the total appearance of content keywords in normalized curriculum A The number of times is obtained by adding the appearance frequencies for each of “water”, “sun”, and “plant”, and is 19/22 + 1/3 + 0/5 = 1.2 times (rounded to the first decimal place). Similarly, the total number of appearances of the content keyword in the curriculum B is 3/22 + 2/3 + 5/5 = 1.8 times (rounded to the first decimal place).

  Subsequently, the metadata estimation processing unit 111 multiplies TF (i) by IDF (i) and adds it to the score of the curriculum (step 810). By this processing, a higher score is obtained as more keywords are used more frequently in the curriculum and more words are used in a limited amount in the curriculum.

  Then, the metadata estimation processing unit 111 checks whether or not the scores based on the appearance frequency of i have been calculated for all the curriculums on the memory (step 811).

  If it is determined in step 811 that all the curriculums in the memory have not been processed, the process returns to step 808 to calculate scores for the remaining curriculums.

  If it is determined in step 811 that all the curriculums stored in the memory have been processed, the metadata estimation processing unit 111 further checks whether all the words in the content keyword have been processed (step 812).

  If it is determined in step 812 that all words have not been processed, processing returns to step 800 to process the remaining words.

  On the other hand, if it is determined in step 812 that all the words have been processed, the scoring process for the curriculum ends.

<Details of Processing of Metadata Input Screen Display Unit (S603)>
FIG. 9 is a flowchart for explaining details of processing by the metadata input screen display unit 112 in step 603 of FIG. This flowchart shows a screen display process for suggesting category information and keywords (feature words) as metadata candidates to be added to the content to be registered from the scored curriculum 113 on the memory. In addition, in the metadata to be displayed as candidates, for example, a keyword that is more likely to be related to content is determined, and a highlight display is performed on the metadata input screen display.

  In FIG. 9, the metadata input screen display unit 112 first sorts the curriculum 113 on the memory in descending order of scores (step 900).

  Next, the metadata input screen display unit 112 acquires the first curriculum (step 901), and stores the values of the grade 202 and the subject 203 in the curriculum 113 in the grade 304 and the subject 305 in the content information 115 (step 902). ).

  Further, the metadata input screen display unit 112 acquires the feature word 114 having the ID 200 value of the curriculum 113 as the related ID 208 from the feature word DB 102 (step 903).

  Further, the metadata input screen display unit 112 is stored in the memory in order to determine whether there is a word more strongly related to the registered content among the acquired feature words 114 in comparison with the content keyword. One (referred to as A) is acquired from the keyword 209 in the feature word 114 (step 905), and one (referred to as B) is acquired from the content keyword 307 in the content information 115 (step 906).

  Then, the metadata input screen display unit 112 confirms whether one of the character strings A and B is included in a part of the other character string, so that the character string B is included in the character string A. Or, it is confirmed whether the character string of A partially matches the character string of B (step 907).

  When it is determined in step 907 that one character string partially matches the other character string, the metadata input screen display unit 112 determines that A is more likely to be related to the content to be registered, and determines A. True is stored in the important flag 210 of the feature word 114 held in the keyword 209 (step 908). If it is determined in step 907 that there is no partial match, step 908 is skipped.

  Then, the metadata input screen display unit 112 checks whether all content keywords have been compared with A (step 909).

  If it is determined in step 909 that all content keywords have not been compared, the process returns to step 906 to compare the remaining content keywords with A.

  On the other hand, if it is determined in step 909 that all content keywords have been confirmed for A, the metadata input screen display unit 112 may be related to the content to be registered for the keywords 209 in all category keywords 209. Is confirmed to be high (step 910).

  If it is determined in step 910 that the keywords 209 in all the feature words 114 have not been processed, the process returns to step 905 to process the keywords 209 in the remaining feature words 114.

  On the other hand, when it is determined in step 910 that the keyword 209 in all the feature words 114 has been processed, the metadata input screen display unit 112 displays the metadata input screen (FIG. 10) (step 911). Note that after displaying the metadata input screen, the program waits for a processing command from the metadata input screen by the user. Details of the metadata input screen will be described later with reference to FIG.

  Subsequently, the metadata input screen display unit 112 determines whether the user has changed the value of the display category column 1005 from the pull-down on the metadata input screen (FIG. 10) (step 912).

  If the value in the display category column 1005 is not changed, the process ends. On the other hand, when the value of the display category column 1005 is changed, the process proceeds to step 913.

  The metadata input screen display unit 112 acquires the ID 200 of the curriculum 113 having the subject character string selected by the change instruction of the user as the value of the subject 204 (step 913).

  Next, the metadata input screen display unit 112 acquires the feature word 114 having the ID as the related ID 208 from the feature word DB 102 and updates the memory (step 914).

  Then, the metadata input screen display unit 112 performs the processing from step 905 to step 911 to determine whether the category keyword associated with the curriculum designated by the user is likely to be associated with the content to be registered. Then, the related keyword column 1006 on the metadata input screen is updated and redrawn on the screen. For example, when the user selects “2. State of formation” from the pull-down list of the display category 1200 in FIG. 12 described later, the metadata input screen display unit 112 acquires a curriculum having the character string as a subject, A feature word associated with the curriculum is acquired, and the related keyword column 1201 is updated. When the related keyword column 1201 is updated, words whose checkboxes are checked by the user remain stored in the memory and are displayed in the related keyword column 1201 even after the update.

<Metadata input screen>
The metadata input screen will be described in detail with reference to FIG. FIG. 10 is a diagram illustrating an example of a metadata input screen.

  On the metadata input screen, the grade and subject are displayed as pre-selected by the user as a guess result of the content metadata, and keyword candidates (feature words) related to the content are displayed.

  In the GUI window, the file path of the file to be registered in the file server 108 is displayed in the file column 1000, and the thumbnail image of the file to be registered is displayed in the thumbnail column 1001.

  In the title column 1002, the title 302 in the content information 115 stored in the memory is displayed. The grade column 304 is displayed in the grade column 1003 and the subject 305 is displayed in the subject column 1004. Note that the title column 1002 is a text area, and the user can freely input the contents. The grade column 1003 and the subject column 1004 are pull-down lists, and values corresponding to the category specified in the school division 303 in the content information 115 are provided as a list in advance. For example, when the school division 303 is “junior high school”, the grade column 1003 is “first grade”, “second grade”, “third grade”, and the subject column 1004 is “national language”, “math”, “English”, “ “Science”, “Society”, “Music”, “Art”, “Health and Physical Education”, “Technology / Home”, and “Morality” are provided as a list in advance. In the grade column 1003 and the subject column 1004, the same character strings as the grade 304 and the subject 305 in the content information 115 are displayed as selected.

  As described above, the data memory 105 stores all the curriculum information. For example, the subjects 204 of the curriculum 113 are stored in the order of the list. The display category column 1005 displays the theme of the selected curriculum. The display category column 1005 is a pull-down list, and the user can select a desired curriculum theme from the pull-down list. When the user changes the value selected from the pull-down list, a command to start processing is passed to the waiting program (see step 913 in FIG. 9). The initial selection value when the metadata input screen is displayed is the first subject 204 in the curriculum 113 list.

  In the related keyword column 1006, the keyword 209 of the feature word 114 associated with the curriculum having the subject displayed in the display category column 1005, which is the feature word 114 on the data memory 105, is displayed with a check box. . Further, when the important flag 210 in the feature word 114 is true, the keyword is highlighted and displayed, for example. In addition to bold display, highlighting may be performed by changing the color or changing the size of the character. This makes it easier for the user to search for keywords that are highly relevant to the content to be registered from among the displayed keywords.

  The additional keyword column 1007 is a text area, and when there is a keyword to be arbitrarily added other than the keyword candidates displayed in the related keyword column, the user can directly input with the keyboard. If there are multiple keywords you want to add, enter them separated by spaces.

  While confirming the content of the content to be registered with the thumbnail 1001, the user inputs the title of the content and confirms and selects the estimated grade, subject, and related keywords.

  In FIG. 10, the user presses a registration button 1008 when input of each item field is completed, and the value input / selected on the metadata input screen is stored in the content information 115. The file column 1000 is the file 301 in the content information 115, the title column 1002 is the title 302, the grade column 1003 is the grade 304, the subject column 1004 is the subject 305, and the word whose associated keyword column 1006 is checked is the assigned keyword 308. The keyword column 1007 divides the character string into space delimiters and stores them in the assigned keywords 308. Then, the content information 115 in which content metadata is stored is stored in the content information DB 102. In this way, the user can register metadata about the content to be registered from the metadata input screen.

  When the user inputs or changes any of the title column 1002, the grade column 1003, and the subject column 1004, the content registration program accepts a processing command from the screen and starts processing. Then, the values in the title column 1002, the grade column 1003, and the subject column 1004 on the metadata input screen are stored in the title 302, the grade 304, and the subject 305 in the content information 115. In this case, since the value of the display category column has not been changed in step 912, the processing of the flowchart of FIG. 9 ends. Further, in step 604 in the flowchart of FIG. 6, it is determined that the user has entered or changed any of the title, grade, and subject values, and the process returns to step 601 to change the grade and subject conditions. Only the curriculum at is acquired and the content metadata is recalculated.

  Also in the process of estimating the metadata of the content in step 602, keywords are extracted from the title character string input by the user and added to the content keywords. The added content keyword is used to estimate the corresponding curriculum.

  For example, in FIG. 10, when the grade is changed from “first grade” to “third grade”, the title 302 in the content information 115 in the memory is “volcanic eruption”, the school division 303 is “junior high school”, and the grade 304 is “3rd grade” and subject 305 are updated to “science”. From the curriculum DB 100, only the curriculum 113 classified into the category of “junior high school”, school grade “third grade”, and subject “science” is acquired from the curriculum DB 100, and the curriculum acquired by the content to be registered is acquired. The score is recalculated.

  FIG. 11 is a diagram illustrating a metadata input screen after score recalculation. The display category 1101 and the related keyword column 1102 are updated when the user changes the grade column 1100 from “1st grade” to “3rd grade”. When the user presses the registration button, the keyword displayed in the related keyword (related feature word) column 1102 and the check mark given by the user is registered as metadata of the content to be registered.

<Summary>
(1) The content management apparatus (content management system) for giving metadata to the content to be registered has been described above. The content management device classifies the content into each document (content description) of the curriculum (content management information) that is classified into categories that have been hierarchized in advance, such as grades and subjects, based on text information that can be acquired from the registered content Then, the keyword (characteristic word) associated with the category or curriculum is suggested. In particular, educational content created using text and images in line with the curriculum should contain limited or frequent words in each subject to explain what was supported by the curriculum. There are many. By using such a feature, it becomes easy to classify content for each subject of the curriculum. Furthermore, a characteristic word (characteristic word) is defined in advance for each subject and associated with each other to perform a suggestion as a keyword related to the content. By providing the keyword as a dictionary in advance, it is possible to deal with words that are not included in the text on the content but are important for explaining the subject, general words that indicate the entire subject, and fluctuations in notation. The user only needs to select a check box for the keyword suggested in this way. By doing so, it is possible to simplify the content registration work by inferring effective metadata for searching and understanding the contents of the user from text information in the content and reflecting the estimation result on the registration screen. .

  In addition, in the metadata registration screen in FIG. 10, by inputting and confirming metadata items in the order of title, grade, and subject from the top, the curriculum targets are narrowed down. The accuracy of guessing the curriculum is improved, and it is possible to suggest more appropriate keywords for content when selecting related keywords. In this way, by using the feature extraction device, it is possible to simplify the registration process for adding metadata that is effective at the time of search or when the user understands the content by referring to the content information.

  In the present embodiment, processing for registering a content file is described. In addition to a content file, a web page published on the web may be used. In this case, the URL of the Web page to be registered is input, and the text in the Web page is acquired and stored in the content text 306 in the process of extracting the content information in Step 600 of FIG. In this way, the same processing as when text is acquired from a file can be performed.

  In the present embodiment, processing for registering content files including text information such as Office files and Flash files on the file server 108 is described. Further, not only a file including text information but also a file not including text information may be registered in a file such as an image. In this case, on the metadata input screen, a text area for inputting a utilization example of the image file to be registered is added and the user inputs it. A morphological analysis is performed on the utilization case document input by the user, and keywords are extracted by the processing in steps 703 and 704 in FIG. Alternatively, character strings may be extracted from the image by OCR (Optical Character Recognition), and these character strings may be used as content keywords. In this way, scoring of each curriculum can be processed in the same way as a content file having text information. Each time the user inputs a character in the text area, the corresponding curriculum 113 is recalculated and the related keyword column 1006 is dynamically updated.

(2) In the present embodiment, the processing of classifying content in the curriculum for educational fields classified into categories such as grades and subjects in advance in the processing of step 601 in FIG. 6 is described. In addition, the curriculum for the education field is not limited to a call center or a manual for industrial products. For example, when using a call center manual, it is possible to easily identify the part of the manual corresponding to inquiries and complaints by providing a document that is classified in advance according to the work to be handled, and for statistical processing for each category. Can be used. Furthermore, it is possible to suggest to call center users by associating past correspondence cases and the like in advance for each correspondence business. Similarly, when a product manual is used, the complaint information from the customer is classified for each defect content, so that it can be used for identification of a manual portion corresponding to the defect and statistical processing for each defect.

  Furthermore, for example, when an individual wants to sell his / her own car as a used car, the present invention can also be applied to processing when registering in a database. In this case, instead of the curriculum information shown in the present embodiment, content management information for managing the vehicle type, model year, color, state, etc. of the car and feature words associated therewith are prepared in advance. Become. The present invention can also be applied when an individual wants to register content related to a specific hobby in a database and share information with other people. In this case, instead of the curriculum information, content management information composed of various hobby categories and characteristic words associated therewith are prepared in advance.

  As described above, the present invention is not only applied to the metadata presentation process when registering educational content, but also provided to the metadata presentation process when registering various types of content. Understood.

(3) In the present embodiment, with respect to the feature words of each subject in the curriculum in the processing of Step 903 and Step 914 in FIG. 9, processing for acquiring a characteristic word for each subject prepared in advance from the feature word DB Explains. Instead of the process of acquiring the feature word from the feature word DB of the process, the feature word may be automatically generated from the curriculum document. In this case, a morphological analysis is performed on curriculum documents classified by subject, and a method based on the frequency of TF / IDF or a method based on the co-occurrence frequency of words such as mutual information and chi-square test. This can be realized by using

(4) The present invention can also be realized by software program codes that implement the functions of the embodiments. In this case, a storage medium in which the program code is recorded is provided to the system or apparatus, and the computer (or CPU or MPU) of the system or apparatus reads the program code stored in the storage medium. In this case, the program code itself read from the storage medium realizes the functions of the above-described embodiments, and the program code itself and the storage medium storing the program code constitute the present invention. As a storage medium for supplying such program code, for example, a flexible disk, CD-ROM, DVD-ROM, hard disk, optical disk, magneto-optical disk, CD-R, magnetic tape, nonvolatile memory card, ROM Etc. are used.

  Also, based on the instruction of the program code, an OS (operating system) running on the computer performs part or all of the actual processing, and the functions of the above-described embodiments are realized by the processing. May be. Further, after the program code read from the storage medium is written in the memory on the computer, the computer CPU or the like performs part or all of the actual processing based on the instruction of the program code. Thus, the functions of the above-described embodiments may be realized.

  Further, by distributing the program code of the software that realizes the functions of the embodiment via a network, it is stored in a storage means such as a hard disk or memory of a system or apparatus, or a storage medium such as a CD-RW or CD-R And the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage means or the storage medium when used.

  Finally, it should be understood that the processes and techniques described herein are not inherently related to any particular apparatus, and can be implemented by any suitable combination of components. In addition, various types of devices for general purpose can be used in accordance with the teachings described herein. It may prove useful to build a dedicated device to perform the method steps described herein. Various inventions can be formed by appropriately combining a plurality of constituent elements disclosed in the embodiments. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, constituent elements over different embodiments may be appropriately combined. Although the present invention has been described with reference to specific examples, these are in all respects illustrative rather than restrictive. Those skilled in the art will appreciate that there are numerous combinations of hardware, software, and firmware that are suitable for implementing the present invention. For example, the described software can be implemented in a wide range of programs or script languages such as assembler, C / C ++, perl, shell, PHP, Java (registered trademark).

  Furthermore, in the above-described embodiment, control lines and information lines are those that are considered necessary for explanation, and not all control lines and information lines on the product are necessarily shown. All the components may be connected to each other.

  In addition, other implementations of the invention will be apparent to those skilled in the art from consideration of the specification and embodiments of the invention disclosed herein. Various aspects and / or components of the described embodiments can be used singly or in any combination in a computerized storage system capable of managing data. The specification and specific examples are merely exemplary, and the scope and spirit of the invention are indicated in the following claims.

100 ... Curriculum DB
101 ... feature word DB
102 ... Content information DB
103 ... Central processing unit 104 ... Program memory 105 ... Data memory 106 ... Display device 107 ... Input device 108 ... File server 109 ... Registered content selection screen display unit 110 ... Content information extraction processing unit 111: Metadata estimation processing unit 112 ... Metadata input screen display unit 113 ... Curriculum 114 ... Feature word 115 ... Content information

Claims (15)

A content management device that provides metadata of content as support information when a user registers content,
A plurality of types of content management information prepared in advance and used for classifying the content when registering the content, and feature word information prepared in advance associated with the content management information. A storage device for storing;
A processor that acquires and outputs metadata of content to be registered as the support information based on the content management information and the feature word information stored in the storage device;
The content management information includes a description regarding a content category,
The processor is
A process of extracting a keyword included in the content to be registered;
Calculating the appearance frequency of the extracted keyword in the description of the content management information, and scoring the plurality of types of content management information based on the appearance frequency;
Processing for obtaining feature word information associated with content management information having the highest score value obtained by the scoring process, and outputting the feature word information as the support information;
The content management apparatus characterized by performing. In claim 1,
In the process of scoring when the extracted keyword appears across a plurality of pieces of content management information,
Calculating a first evaluation value indicating whether or not the extracted keyword is characteristic and relatively important;
Calculating a second evaluation value obtained by normalizing the appearance frequency of the extracted keyword in each content management information;
The content management apparatus, wherein the score value of each content management information is calculated by multiplying the first evaluation value and the second evaluation value. In claim 2,
The processor is
The first evaluation value is calculated by dividing the total number of content management information by the number of content management information in which the extracted keyword appears,
The content management characterized in that the second evaluation value is calculated by dividing the appearance frequency of the extracted keyword in each content management information by the appearance frequency of the extracted keyword in the entire content management information apparatus. In claim 1,
The processor generates and outputs a GUI for enabling a user to select feature word information associated with the content management information having the highest score value in the output process. Management device. In claim 4,
The processor further includes:
For a plurality of feature words included in the feature word information associated with the content management information having the highest score value, it is determined whether or not there is an inclusion relationship with the extracted keyword. Execute processing to add flag information indicating that the feature word information is included in the inclusion relationship,
In the output process, the processor outputs the GUI so that the feature words of the feature word information to which the flag information is assigned are output separately from the feature words of the feature word information not having the flag information. A content management apparatus that generates the content management apparatus. In claim 1,
The content management information includes a plurality of types of hierarchical category information used when classifying content,
The plurality of types of hierarchized category information includes large item category information for classifying content in a first category, and content classified in the first category, further subdivided into a second category. Medium item category information, and sub-category category information for subdividing and classifying the content classified in the second category into a third category,
The content management apparatus, wherein the processor narrows down content management information that is a target for counting the appearance frequency of the extracted keyword based on category information designated by the user. A content management device according to claim 1;
At least one client terminal device having a display device and an input device;
The client terminal device transmits content to be registered designated by the input device to the content management device,
The content management device transmits the support information to the client terminal device;
The content management system, wherein the display device of the client terminal device displays support information received from the content management device. A content management method for providing metadata of content as support information when a user registers content,
When a processor of a content management apparatus registers the content, it is used to classify the content, and a plurality of types of content management information prepared in advance and associated with each content management information are prepared in advance. Providing a storage device for storing the feature word information;
The processor acquires the metadata of the content to be registered as the support information based on the content management information and the feature word information stored in the storage device, and outputs the support metadata;
The content management information includes a description regarding a content category,
In the outputting step, the processor includes:
A process of extracting a keyword included in the content to be registered;
Calculating the appearance frequency of the extracted keyword in the description of the content management information, and scoring the plurality of types of content management information based on the appearance frequency;
Processing for obtaining feature word information associated with content management information having the highest score value obtained by the scoring process, and outputting the feature word information as the support information;
The content management method characterized by performing. In claim 8,
In the process of scoring when the extracted keyword appears across a plurality of pieces of content management information,
Calculating a first evaluation value indicating whether or not the extracted keyword is characteristic and relatively important;
Calculating a second evaluation value obtained by normalizing the appearance frequency of the extracted keyword in each content management information;
The content management method, wherein the score value of each content management information is calculated by multiplying the first evaluation value and the second evaluation value. In claim 9,
The processor is
The first evaluation value is calculated by dividing the total number of content management information by the number of content management information in which the extracted keyword appears,
The content management characterized in that the second evaluation value is calculated by dividing the appearance frequency of the extracted keyword in each content management information by the appearance frequency of the extracted keyword in the entire content management information Method. In claim 8,
The processor generates and outputs a GUI for enabling a user to select feature word information associated with the content management information having the highest score value in the output process. Management method. In claim 11,
The processor further includes:
For a plurality of feature words included in the feature word information associated with the content management information having the highest score value, it is determined whether or not there is an inclusion relationship with the extracted keyword. Execute processing to add flag information indicating that the feature word information is included in the inclusion relationship,
In the output process, the processor outputs the GUI so that the feature words of the feature word information to which the flag information is assigned are output separately from the feature words of the feature word information not having the flag information. A content management method comprising generating the content management method. In claim 8,
The content management information includes a plurality of types of hierarchical category information used when classifying content,
The plurality of types of hierarchized category information includes large item category information for classifying content in a first category, and content classified in the first category, further subdivided into a second category. Medium item category information, and sub-category category information for subdividing and classifying the content classified in the second category into a third category,
The method further comprises:
The content management method comprising: a step of narrowing down the content management information to be counted by the processor based on the category information designated by the user.   A program for causing a computer to execute the content management method according to claim 8.   A computer-readable storage medium storing a program for causing a computer to execute the content management method according to claim 8.

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