JP4413796B2 - List information generation system and method, program - Google Patents

Description

  The present invention relates to a list information generation system, method, and program for generating list information based on metadata including a subject, a predicate, and an object.

  In the prior art, metadata consisting of subject information, predicate information, and object information is stored in advance in a storage device of a computer, and information processing by the computer based on the stored contents is performed. By using such information processing technology using metadata, various applications according to the purpose are possible.

  In addition, by using the metadata constraint definition for a large amount of metadata stored and accumulated, it is possible to limit the range that the target concept can take, so a series of processing when processing metadata is performed. Can be easily.

  For example, as an application for providing information to a user, when a user wants to know a product (clothing) that can be “cleaned” at “A store”, the subject information is “A store”. Thus, metadata that matches two types of information whose predicate information is “cleaning” is retrieved from a storage device and acquired.

  Similarly, when it is desired to know a store where “Y-shirt” can be “cleaned”, two types of metadata are stored in which the predicate information is “cleaning” and the object information is “Y-shirt”. Searching and acquiring from a device is performed.

  In this way, if a technique for searching for target information using metadata composed of subject information, predicate information, and object information is used, necessary information is searched and acquired easily and quickly. It becomes easy.

  A technique disclosed in Japanese Patent Application Laid-Open No. 2002-251311 is known as a technical reference related to information retrieval using metadata according to a conventional technique (see Patent Document 1).

Note that it is possible to use an RDF (Resource Description Framework) sentence as the metadata format, and technical references relating to the RDF sentence are disclosed in Non-Patent Document 1 and Non-Patent Document 2. (See Non-Patent Document 1 and Non-Patent Document 2).
JP 2002-251311 A "Resource Description Framework (RDF) Model and Syntax Specification", Ora Lassia, Ralph R. Swick, [online], Internet <URL: http://www.w3.org/TR/1999/REC-rdf-syntax- 19990222 /> "RDF Vocabulary Description Language 1.0: RDF Schema", Dan Brickley, RVGuha, [online], Internet <URL: http://www.w3.org/TR/rdf-schema/>

  However, in the above-described prior art, it is necessary to repeatedly search and compare the stored and stored metadata to find the target information. Therefore, the search takes a lot of time. It was.

  In addition, there is no means for utilizing various information obtained in the retrieval process for information retrieval, and it is difficult to utilize various information as a comparative viewpoint.

  The present invention has been made in view of the above-described problems, and the object of the present invention is to use viewpoints for utilizing such information based on text information related to a search object using metadata, and to cope with it. To provide a list information generation system, method, and program capable of extracting values to be provided and providing them as list information.

  In order to solve the above problem, the present invention of claim 1 is a list information generation system for generating list information using metadata included in text information, wherein the subject information included in the text data Metadata storage means for storing metadata composed of predicate information, object information, word information, word concept information representing the concept of the word information, and subject concept information representing the concept of the subject information; Metadata constraint definition storage means for storing metadata constraint definition information composed of predicate information corresponding to the subject information, object concept information representing the concept of the object information, and subject concept information The subject concept information is extracted from the text data including the subject information and the metadata constraint definition including the subject concept information is retrieved from the metadata constraint definition storage means. Metadata constraint definition retrieval means for extracting the object concept information from the metadata constraint definition retrieved by the metadata constraint definition retrieval means, and the metadata including the word concept information equal to the object concept information Word concept metadata search means for searching the metadata storage means, word information is extracted from the metadata searched by the word concept metadata search means, and the word information is searched from the text data Extracted word search means, predicate information extracted from the text data when the word information searched by the extracted word search means is found in the text data, and the word extracted from the metadata Object information equal to the information, metadata generating means for generating generated metadata, and the generated metadata And a list information generation means for generating a list information based on the subject information and the predicate information and object information included in the.

  In addition, the present invention described in claim 2 provides the predescription word information constituting the metadata constraint definition information according to claim 1 having weighting information for each subject concept information, and based on the weighting information. There is provided a posting order determining means for selecting pre-description word information used for generating the list information and determining a presentation order to the list information.

  According to a third aspect of the present invention, the predicate information corresponding to the subject concept information for which the weighting information is to be updated with respect to the content stored in the metadata storage means in the first or second aspect. The metadata described with respect to the subject concept information and the previous description word information is used for each of the subject concept information and the previous description word information, and the result of calculating the usage frequency of the predicate information for each of the subject concept information and the previous description word information is weighted In order to change the weighting information of the metadata constraint definition that is stored in the metadata constraint definition storage means and has predicate information and subject concept information corresponding to the previous description word information corresponding to the subject concept information. The first weight changing means is provided.

  According to a fourth aspect of the present invention, in any one of the first to third aspects, the metadata storage means is added by adding metadata other than the already obtained metadata to the stored content. The subject concept information included in the metadata and the metadata constraint definition information described with respect to the predicate information are acquired from the metadata constraint definition means, and the weighting information based on the acquired metadata constraint definition information There is provided second weight changing means for changing.

  According to a fifth aspect of the present invention, in any one of the first to fourth aspects, the metadata constraint definition information stored in the metadata constraint definition storage unit according to a use history of the list information. There is provided third weight changing means for changing the weighting information.

  The invention according to claim 6 is the subject of the list information according to any one of claims 1 to 5, based on the keyword information input to the list information generation means for narrowing down the contents of the list information. Predicate information corresponding to concept information is specified, metadata constraint definition information equivalent to the specified subject concept information and previous description word information is extracted from the metadata constraint definition storage means, and extracted metadata constraint definition information 4th weighting change means for changing the weighting information is provided.

  The present invention according to claim 7 is a list information generation method for generating list information using metadata included in text information, wherein the subject included in the text data is stored by the metadata storage means. Storing metadata composed of information, predicate information, object information, word information, word concept information representing the concept of the word information, and expressing the concept of the subject information by metadata constraint definition storage means Storing metadata constraint definition information composed of subject concept information, predicate information corresponding to the subject information, object concept information representing the concept of the object information, and metadata constraint definition search means To extract subject concept information from text data including subject concept information and subject information, and define metadata constraint definitions that include subject concept information. The step of retrieving from the metadata constraint definition storage means, and the word concept metadata search means extract object concept information from the metadata constraint definition retrieved by the metadata constraint definition retrieval means, and this object concept concept Retrieving the metadata including the word concept information equal to the information from the metadata storage means, and extracting the word information from the metadata searched by the word concept metadata search means by the extracted word search means; The predicate extracted from the text data when the word information searched by the extracted word search means is found in the text data by the step of searching the word information from the text data, and the metadata generation means. Information and object information equal to the word information extracted from the metadata Generating a formed metadata, the list information generation means, and a step of generating a list information based on the subject information and the predicate information and object information included in the generated metadata.

  The present invention according to claim 8 is a list information generation program for generating list information using metadata included in text information, and the subject included in the text data by a metadata storage means. Storing metadata composed of information, predicate information, object information, word information, word concept information representing the concept of the word information, and expressing the concept of the subject information by metadata constraint definition storage means Storing metadata constraint definition information composed of subject concept information, predicate information corresponding to the subject information, object concept information representing the concept of the object information, and metadata constraint definition search means To extract subject concept information from text data containing subject concept information and subject information, and metadata constraints that include this subject concept information Searching the metadata constraint definition storage means for meaning, and extracting word concept concept information from the metadata constraint definition searched by the metadata constraint definition search means by the word concept metadata search means, Searching the metadata including the word concept information equal to the word concept information from the metadata storage means, and extracting the word information from the metadata searched by the word concept metadata search means by the extracted word search means The word information is retrieved from the text data when the word information retrieved by the extracted word retrieval means is found in the text data by the step of retrieving the word information from the text data, and the metadata generation means. Predicate information, object information equal to the word information extracted from the metadata, or Generating a generated metadata comprising, by list information generation means, and a step of generating a list information based on the subject information and the predicate information and object information included in the generated metadata.

  The present invention according to claim 9 can be installed and operated in a server arranged on the network according to claim 8.

  According to the present invention, based on text information relating to a search target using metadata, a viewpoint for utilizing the information and a list information generation system capable of extracting values corresponding thereto and providing them as list information, and Methods and programs can be provided.

<Generation of metadata>
Below, the basic composition about the production | generation of the metadata which concerns on the list information production | generation system of this invention is demonstrated.

  First, FIG. 1 is a block diagram showing a configuration of metadata generation means according to an embodiment of the present invention.

  The metadata generation unit 1 includes a subject, a document storage unit 11 that stores a document including a subject concept that is a concept of the subject and document content, a document analysis unit 12 that analyzes the document, a subject, a predicate, and an object. Metadata, metadata storage unit 13 for storing metadata consisting of a word and a word concept that is the concept of the word, a subject concept that is a subject concept, a predicate corresponding to the subject, and corresponding to the subject and predicate Metadata constraint definition storage unit 14 for storing a metadata constraint definition including an object concept that is a concept of an object to be performed, and an object candidate storage for storing a subject and a set of object candidates that are object candidates The computer system includes a unit 15 and a metadata generation unit 16 that generates metadata using these units, and is communicably connected to the user device 2.

  Next, FIG. 2 is an explanatory diagram for explaining the configuration of the document 111 in the documents stored in the document storage unit 11.

  The document 111 includes a subject concept “cleaning store”, a subject “A store”, and document contents (title and body). The contents of the document include “menu”, “general clothing”, “suit”, “shirt”,..., “Special goods”, “Japanese clothes”,. The subject concept “cleaning store” and the subject “A store” constitute one RDF sentence. One RDF sentence is composed of the subject “A store” and the title “price list”. One RDF sentence is composed of the subject "A store" and the body.

  Next, FIG. 3 is an explanatory diagram for explaining the metadata constraint definition stored in the metadata constraint definition storage unit 14.

  The metadata constraint definition storage unit 14 stores a metadata constraint definition including a subject concept “cleaning shop”, a predicate “cleaning”, and an object concept “clothing”. One RDF sentence is composed of the subject concept “cleaning shop” and the predicate “cleaning”. One RDF sentence is composed of the predicate “cleaning” and the object concept “clothing”.

  Next, FIG. 4 is an explanatory diagram for explaining the metadata stored in the metadata storage unit 13.

  In the metadata storage unit 13, metadata including the word “A store” and the concept of the word concept “cleaning store”, metadata including the word “B store” and the word concept “cleaning store”, and the word “ Metadata including “shirt” and the word concept “clothing” is stored. In these metadata, one metadata constitutes one RDF sentence.

  Next, the operation of the metadata generation unit 1 will be described.

  First, FIG. 5 shows a first sequence diagram of the metadata generation means 1.

  First, the metadata generation unit 16 inputs the document 111 from the document storage unit 11 (step 1: S1), extracts the subject concept “cleaning shop” from the document 111 (step 3: S3), and the subject concept “cleaning shop”. Is retrieved from the metadata constraint definition storage unit 14 (step 5: S5), so that the metadata including the subject concept “cleaning shop”, the predicate “cleaning”, and the object concept “clothing”. The constraint definition is read (step 7: S7).

  The metadata generation unit 16 extracts the object concept “clothes” from the read metadata constraint definition (step 11: S11), and stores metadata including a word concept equal to the extracted object concept “clothes” in the metadata storage unit. By searching from 13 (step 13: S13), metadata consisting of the word “shirt” and the word concept “clothing” is read (step 15: S15).

  Then, the word “shirt” is extracted from the read metadata (step 17: S17), and the extracted word “shirt” and the document 111 are given to the document analysis unit 12 (step 19: S19).

  The document analysis unit 12 searches the document 111 for the given word “shirt” (step 21: S21), and if the word is found in the document 111, notifies the metadata storage unit 13 of the fact (step 21). 23: S23).

  When there is a notification that the word “shirt” is in the document 111, the metadata generation unit 16 extracts the subject “A shop” from the document 111 input in S1 (step 25: S25), and in S7. The predicate “cleaning” is extracted from the read metadata constraint definition (step 27: S27).

  Next, as shown in FIG. 6, metadata including the subject “A store” extracted in S25, the predicate “cleaning” extracted in S27, and the object “shirt” equivalent to the word extracted in S17 is generated. (Step 28: S28). The metadata consisting of the subject, predicate and object can be composed of one RDF sentence (metadata shown in the upper part of FIG. 6) as well as the metadata consisting of the word and the word concept described above, and a blank node. (A circle connecting “cleaning” and “article” in the metadata shown in the lower part of FIG. 6) can be provided, and can be composed of two RDF sentences. The metadata is stored in the metadata storage unit 13 (step 29: S29).

  FIG. 7 is a second sequence diagram of the metadata generation unit 1.

  In addition, the document analysis unit 12 examines how the word “shirt” given in S19 is arranged in the document 111 by, for example, extracting characters before and after the word “shirt” (referred to as “front and back characters”). (Step 31: S31), a character string (for example, suit, tie, blouse, etc.) having front and rear characters as words arranged in the same manner as the object is extracted from the document 111 (Step 33: S33). This word is returned to the metadata generation unit 16 (step 35: S35).

  As shown in FIG. 8, for example, the metadata generation unit 16 performs the subject “suit” that is equal to the subject “A store” extracted in S25, the predicate “cleaning” extracted in S27, and the word returned in S35. Is generated (step 37: S37) and stored in the metadata storage unit 13 (step 39: S39).

  The metadata generation unit 16 also stores in the metadata constraint definition storage unit 14 a metadata constraint definition consisting of the subject concept extracted in S3, the predicate extracted in S27, and the object concept equal to the word returned in S35. If not, a metadata constraint definition including, for example, the subject concept “cleaning shop”, the predicate “cleaning”, and the object concept “blouse” is generated (step 41: S41). It is stored in the constraint definition storage unit 14 (step 43: S43).

  In addition, the document analysis unit 12 obtains words (for example, menus, general clothing, ski wear, Japanese clothes, etc.) that do not have front and back characters from the document 111 as words that are not arranged in the same manner as the word “shirt”. Extraction (step 51: S51), the word is returned to the metadata generation unit 16 (step 53: S53).

  The metadata generation unit 16 sets the returned word as a target word candidate, and generates a set of the subject “A store” extracted in S25 and each target word candidate as shown in FIG. 10 (step 55: S55). Then, it is stored in the object candidate storage unit 15 (step 57: S57).

  FIG. 11 is a third sequence diagram of the metadata generation unit 1.

  The metadata generation unit 16 inputs metadata including the subject “B store”, the predicate “cleaning”, and the object “ski wear” as shown in FIG. 12 from the user device 2 (step 61: S61). Then, an object is extracted from the metadata (step 63: S63), and a metadata constraint definition including an object concept equal to the extracted object “skiware” is searched (step 64: S64).

  If there is no metadata constraint definition including the object concept “ski wear”, the subject “B store” is extracted from the input metadata (step 65: S65).

  Then, metadata including a word equal to the subject “B store” is searched from the metadata storage unit 13 (step 67: S67), whereby metadata including the word “B store” and the word concept “cleaning store” is obtained. Reading (step 69: S69), extracting the word concept “cleaning shop” from the metadata (step 70: S70), and searching for metadata including the extracted word concept (step 71: S71), the word “ Metadata including the “C store” and the word concept “cleaning store” is read (step 73: S73).

  The metadata generation unit 16 extracts the word “C store” from the read metadata (step 75: S75), and from the subject equal to this word and the target word candidate equal to the object “skiware” extracted in S63. Is searched from the object candidate storage unit 15 (step 77: S77).

  If the set is found in the target word candidate storage unit 15, the set is deleted from the target word candidate storage unit 15 (step 79: S79).

  The predicate “cleaning” is extracted from the metadata input in S63 (step 80: S80). Then, as shown in FIG. 12, the metadata including the subject “B store” extracted in S65, the predicate “cleaning” extracted in S80, and the object concept “skiware” equal to the object extracted in S63 is generated. Then (step 81: S81), the data is stored in the metadata storage unit 13 (step 83: S83).

  Further, the metadata generation unit 16 extracts the subject concept “cleaning shop” from the metadata constraint definition read out in S69 (step 85: S85), and converts the subject concept, the predicate extracted in S63 and the object extracted in S63. If there is no metadata constraint definition comprising the same object concept in the metadata constraint definition storage unit 14, as shown in FIG. 13, the subject concept “cleaning store” retrieved in S65, the predicate “cleaning” retrieved in S80, A metadata constraint definition composed of the object concept “skiware” equal to the object extracted in S63 is generated (step 87: S87), and is stored in the metadata constraint definition storage unit 14 (step 89: S89).

<Generation of list information using metadata>
FIG. 14 shows one of the list information using the metadata constraint definition (viewpoint) stored in the metadata constraint definition storage unit 14 and the metadata stored in the metadata storage unit 13. It is explanatory drawing for producing | generating the comparison table 17 and comparison table 17 'as an example of an expression format.

  FIG. 14 shows a metadata storage unit 13 that stores and stores metadata, a metadata constraint definition storage unit 14 that stores and stores metadata constraint definitions, and a metadata generation unit (processing device). 16 and the user device 2 are shown.

  The metadata constraint definition stored in the metadata constraint definition storage unit 14 has weights as weight information for each concept of subject information and for each predicate of each metadata constraint definition. For example, in the case of predicate information “handling” in the metadata constraint definition, the concept “cleaning” of the subject information that the predicate information has as a domain is specified. In FIG. 14, the class “cleaning” is specified in the domain for the predicate information “handling”.

  Furthermore, for the class designation of “handling”, the range that the value can take is designated by range. In FIG. 14, “Y-shirt” is specified as a possible value for “handling” which is a class for subject information “cleaning”.

  Note that these ranges are not limited to “Y-shirts”, but for example, as shown in FIG. 14, information such as “suits”, “coats”, “early morning discounts”, “storage”, and “days” are defined as ranges. Has been. Similarly, with regard to predicates, information such as “service” and “finish” is defined as a class in addition to “handling”.

  Further, when the predicate is “handling” and the domain is “cleaning”, the weighting information is set as “10”. Of course, this weighting information is not limited to “10”, and may be arbitrarily set according to the purpose. Similarly, when the predicate is “service” and the domain is “cleaning”, the weighting information is “5”, and when the predicate is “finished” and the domain is “cleaning”, the weighting information is “2”.

  Next, metadata having an instance (data) of “cleaning” is stored in the metadata storage unit 13. The metadata here is information such as “Store A” and “Store B” including “cleaning”. In addition, “handling”, “service”, “finished”, “Y-shirt”, “suit”, “coat”, “early morning discount”, “same day”, “storage” as corresponding information (metadata) for each. Information such as “2 days” is also stored.

  As shown in FIG. 1, the processing device 16 includes a document storage unit 11, a document analysis unit 12, an object candidate storage unit 15, a metadata storage unit 13, and a metadata constraint definition storage unit (not shown). 14, information is communicated with each other, and metadata is generated based on document information (text data) as described above with reference to FIGS. 1 to 13.

  Furthermore, the user device 2 includes an input unit (not shown) for displaying metadata generated by the processing device 16 or for inputting a keyword 10 by a user (not shown).

  The keyword 10 is used to create list information of “subject information” and “concept information” that matches the “predicate” and “object” of the metadata constraint definition.

  The comparison table 17 and the comparison table 17 ′ match the metadata constraint definition in the metadata constraint definition storage unit 14 based on the keyword 10 specified by the user for the metadata stored in the metadata storage unit 13. In this example, list information of “subject information” and “concept information” is generated, and the generated list information is displayed, for example, in the form of a comparison table.

  First, information (text data) such as “cleaning” is given as a keyword 10 when the comparison table 17 (comparison table 17 ′) is generated from the user. In response to this, a metadata constraint definition having “cleaning” as subject information and concept information is acquired from the metadata constraint definition storage unit 14.

  Next, metadata that is an instance of the acquired metadata constraint definition is extracted from the metadata storage unit 13.

  Finally, the predicate information of the extracted metadata constraint definition is used as a comparison item, and the order of the comparison items and the number of presentations are determined using the weight of the predicate, and the comparison target is the subject information of the metadata. A comparison table using the comparison value as the object of the metadata is generated.

  In the comparison table 17 generated in this way, items such as “handling”, “service”, and “finish” are arranged as predicate information on the X axis in the horizontal direction. Subject information such as “A store”, “B store”, and “C store” is arranged on the Y axis in the vertical direction. With such an arrangement, for example, in the “handling” item for “A store”, information such as “Y-shirt”, “suit”, and “coat” is displayed as a comparison value.

  When there are a large number of comparison values, “Y-shirt” for each “handling” item of “A store”, “B store”, and “C store” as referred to in the comparison table 17 ′. , “Suit”, “coat”, “futon” may be provided, and whether or not each item is included may be indicated by a display such as “○” (included) or “×” (not included). .

  Next, FIG. 15 is an explanatory diagram for describing weighting in the metadata constraint definition stored in the metadata constraint definition storage unit 14 referred to in FIG.

  The comparison items when the comparison table 17 and the comparison table 17 ′ shown in FIG. 14 are displayed are changed according to the “weighting information” given for each subject information and predicate information of the metadata constraint definition.

This weighting information
(1) When the number of predicate information used for stored metadata increases, the value of weighting information is added.

  (2) The weighting value of the predicate information used for the metadata newly added to the metadata storage unit 13 is added.

  (3) The weight value of the predicate information corresponding to the comparison item that the user of the comparison table 17 (comparison table 17 ′) is interested in and emphasizes is added.

  (4) The weighting value of the related predicate information is added from the keyword 10 used when generating the comparison table 17 (comparison table 17 ′).

  By executing the addition processing for the respective weighting information values (1) to (4) above, the weighting information value changes, and the display form of the comparison table 17 (comparison table 17 ′) also changes accordingly. .

  Referring to FIG. 15, first, metadata is generated by a method such as metadata extraction from text data or collection of word-of-mouth information from a user. It is stored in the metadata storage unit 13 generated in this way. Here, an example is shown in which metadata of an instance of “cleaning” is stored.

  The metadata stored and stored in the metadata storage unit 13 is stored in the metadata constraint definition storage unit 14 in accordance with the usage frequency of the predicate (“handling” in FIG. 15) in the stored metadata. The value of the weighting information attached to the metadata constraint definition is changed (increased from 10 to 14 in FIG. 15).

  Alternatively, if there is a metadata constraint definition that the user 18 has determined to be particularly important for the comparison table 17, the metadata constraint definition stored in the metadata constraint definition storage unit 14 can be specified by designating the metadata constraint definition. The value of the weighting information is changed. For example, when the user 18 attaches importance to the metadata constraint definition “finish”, this “important” information is transmitted to the metadata constraint definition storage unit 14 and has a metadata constraint definition “finish”. The value of the weighting information of the metadata is increased (increased from 5 to 6 in FIG. 15) or decreased according to the purpose.

  As a method of collecting history information such as “important” information of the user 18, the user 18 can arbitrarily select an item to be displayed as the comparison table 17, or can be used when the comparison table 17 is generated. It can be done by collecting information such as the contents of the keyword 10 and the input of word-of-mouth information. Among these, there is a method for collecting word-of-mouth information, such as input in a metadata format or converting text data input by free description into a metadata format on the system side.

  Next, FIG. 16 is a sequence diagram for explaining a series of processes for generating list information by the list information generating system of the present invention.

  First, a user (not shown) performs keyword input in the user device 2 (step 100: S100). The processing device 16 issues a query (processing request) in response to an input of a keyword (step 101: S101). By issuing this query, a concept (concept information) search query is transmitted to the metadata constraint definition storage unit 14.

  The metadata constraint definition storage unit 14 extracts a metadata constraint definition having the keyword concept as a subject constraint (step 102: S102). The extracted “metadata constraint definition having a concept as a subject constraint” extracted here is sent to the processing device 16, where the metadata constraint definition to be used is determined from the predicate weighting information (step 103: S103). Further, a query is issued (step 104: S104), and a metadata extraction query is transmitted to the metadata storage unit 13.

  The metadata storage unit 13 extracts metadata corresponding to the metadata constraint definition (step 105: S105). The metadata extracted here is sent to the processing device 16, and data for displaying a comparison table, which is one display form of the metadata list information, is generated using the metadata (step 106: S106). The data generated here is transmitted to the user device 2 and a comparison table is displayed (step 107: S107).

  Thus, the comparison table can be easily created by the metadata constraint definition and the metadata. Further, the predicate information emphasized in each concept information can be known from the metadata and the trend of the user 18 in the comparison table 17, and the comparison table 17 can be generated more effectively.

  According to the embodiments of the present invention described above, using metadata, based on text information related to a search target, a viewpoint for utilizing the information and values corresponding thereto are extracted and provided as list information Possible list information generation system and method, and program can be provided.

  A computer program that causes the system to execute the method performed by the list information generation system described above (list information generation method) is a computer-readable recording medium such as a semiconductor memory, a magnetic disk, an optical disk, a magneto-optical disk, or a magnetic tape. The data may be stored in a network or transmitted via a communication network such as the Internet for wide distribution.

The block diagram for demonstrating the whole structure based on the list information generation system of embodiment of this invention is shown. It is a figure which shows the structure of the document in the document memorize | stored in the document memory | storage part based on the list information generation system of embodiment of this invention. It is a figure which shows the metadata constraint definition memorize | stored in the metadata constraint definition memory | storage part based on the list information generation system of embodiment of this invention. It is a figure which shows the metadata memorize | stored in the metadata memory | storage part based on the list information generation system of embodiment of this invention. It is a 1st sequence diagram of the metadata production | generation means based on the list information production | generation system of embodiment of this invention. It is a figure which shows the metadata produced | generated by S28 based on the list information production | generation system of embodiment of this invention. It is a 2nd sequence diagram of the metadata production | generation means based on the list information production | generation system of embodiment of this invention. It is a figure which shows the metadata produced | generated by S37 based on the list information production | generation system of embodiment of this invention. It is a figure which shows the metadata constraint definition produced | generated by S41 based on the list information production | generation system of embodiment of this invention. It is a figure which shows the group of the subject and object candidate produced | generated by S55 based on the list information production | generation system of embodiment of this invention. It is a 3rd sequence diagram of the metadata production | generation means based on the list information production | generation system of embodiment of this invention. It is a figure which shows the metadata produced | generated by S61 based on the list information production | generation system of embodiment of this invention. It is a figure which shows the metadata constraint definition produced | generated by S87 based on the list information production | generation system of embodiment of this invention. An explanatory view for explaining processing which generates metadata list information concerning a list information generation system of an embodiment of the invention is shown. An explanatory view for explaining processing which generates metadata list information concerning a list information generation system of an embodiment of the invention is shown. The sequence diagram for demonstrating the process which produces | generates the list information of metadata based on the list information generation system of embodiment of this invention is shown.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 ... Metadata production | generation means 2 ... User apparatus 10 ... Keyword 11 ... Document memory | storage part 12 ... Document analysis part 13 ... Metadata memory | storage part 14 ... Metadata constraint definition memory | storage part 15 ... Target word candidate memory | storage part 16 ... Metadata Generator

Claims (9)

A list information generation system for generating list information using metadata included in text information,
Metadata storage means for storing metadata composed of subject information, predicate information, object information, word information, and word concept information representing a concept of the word information included in the text data;
For storing metadata constraint definition information composed of subject concept information representing the concept of the subject information, predicate information corresponding to the subject information, and object concept information representing the concept of the subject information. Metadata constraint definition storage means;
Metadata constraint definition retrieval means for extracting subject concept information from text data including subject concept information and subject information, and retrieving metadata constraint definitions including the subject concept information from the metadata constraint definition storage means;
To extract object concept information from the metadata constraint definition retrieved by the metadata constraint definition retrieval means, and retrieve the metadata including the word concept information equal to the object concept concept information from the metadata storage means. Word concept metadata search means,
Extracted word information from the metadata searched by the word concept metadata search means, extracted word search means for searching the word information from the text data,
When the word information searched by the extracted word search means is found in the text data, predicate information extracted from the text data, object information equal to the word information extracted from the metadata, Metadata generating means for generating generated metadata comprising:
List information generating means for generating list information based on subject information, predicate information and object information included in the generated metadata;
A list information generation system comprising: Predescription word information constituting the metadata constraint definition information is:
Weight information is included for each subject concept information,
The publication order determination means for selecting predescription word information used for generation of the list information based on the weighting information and determining the order of presentation to the list information. List information generation system. About the contents stored in the metadata storage means,
Take out the metadata described for the predicate information corresponding to the subject concept information to be updated weighting information,
Using the metadata for each of the subject concept information and the previous description word information, the result of calculating the use frequency of the predicate information for each of the subject concept information and the previous description word information is weighted information,
A first for changing the weighting information for the metadata constraint definition stored in the metadata constraint definition storage means and having predicate information and subject concept information corresponding to the previous description word information corresponding to the subject concept information. The list information generation system according to claim 1, further comprising: The metadata storage means includes
By adding metadata other than the already obtained metadata to the storage content, subject concept information and metadata constraint definition information described with respect to the predicate information included in the added metadata are added to the metadata. Obtained from the data constraint definition means,
The list information generation system according to any one of claims 1 to 3, further comprising second weight change means for changing weight information based on the acquired metadata constraint definition information. A third weight change unit for changing the weight information of the metadata constraint definition information stored in the metadata constraint definition storage unit according to the usage history of the list information. Item 5. The list information generation system according to any one of Items 1 to 4. Based on the keyword information input to the list information generating means to narrow down the contents of the list information, specify predicate information corresponding to subject concept information of the list information,
Extracting metadata constraint definition information equivalent to the identified subject concept information and previous description word information from the metadata constraint definition storage means;
The list information generation system according to any one of claims 1 to 5, further comprising a fourth weight change unit for changing the weight information of the extracted metadata constraint definition information. A list information generation method for generating list information using metadata included in text information,
Storing metadata constituted by subject information, predicate information, object information, word information, word concept information representing a concept of the word information, included in the text data by the metadata storage means;
Metadata composed of subject concept information representing the concept of the subject information, predicate information corresponding to the subject information, and object concept information representing the concept of the subject information by the metadata constraint definition storage means. Storing constraint definition information;
Extracting the subject concept information from the text data including the subject concept information and the subject information by the metadata constraint definition retrieval means, and retrieving the metadata constraint definition including the subject concept information from the metadata constraint definition storage means; ,
The word concept metadata search means extracts the target word concept information from the metadata constraint definition searched by the metadata constraint definition search means, and the metadata including the word concept information equal to the target word concept information is extracted from the metadata. Retrieving from the metadata storage means;
Extracting word information from the metadata searched by the word concept metadata search means by an extracted word search means, and searching the word information from the text data;
When the word information searched by the extracted word search means is found in the text data by the metadata generation means, the predicate information extracted from the text data and the word information extracted from the metadata Generating generated metadata consisting of equal object information and
Generating list information based on subject information, predicate information and object information included in the generated metadata by list information generating means;
A list information generation method characterized by comprising: A list information generation program for generating list information using metadata included in text information,
Storing metadata constituted by subject information, predicate information, object information, word information, word concept information representing a concept of the word information, included in the text data by the metadata storage means;
Metadata composed of subject concept information representing the concept of the subject information, predicate information corresponding to the subject information, and object concept information representing the concept of the subject information by the metadata constraint definition storage means. Storing constraint definition information;
Extracting the subject concept information from the text data including the subject concept information and the subject information by the metadata constraint definition retrieval means, and retrieving the metadata constraint definition including the subject concept information from the metadata constraint definition storage means; ,
The word concept metadata search means extracts the target word concept information from the metadata constraint definition searched by the metadata constraint definition search means, and the metadata including the word concept information equal to the target word concept information is extracted from the metadata. Retrieving from the metadata storage means;
Extracting word information from the metadata searched by the word concept metadata search means by an extracted word search means, and searching the word information from the text data;
When the word information searched by the extracted word search means is found in the text data by the metadata generation means, the predicate information extracted from the text data and the word information extracted from the metadata Generating generated metadata consisting of equal object information and
Generating list information based on subject information, predicate information and object information included in the generated metadata by list information generating means;
A list information generation program characterized by comprising: 9. The list information generation program according to claim 8, wherein the list information generation program can be installed and operated on a server arranged on a network.

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