KR101318954B1 - A system and method for providing information including user's subjective comments - Google Patents

Abstract

The present invention proposes a method for providing adjective tagging information. According to the proposed method, storing the tagging related information received from at least one user in a database and constructing the tagging data and receiving a request for providing the tagging result from at least one user, and in response thereto, the tagging Analyzing data and providing the data to the at least one user is performed. As a result, the user may be provided with information on people's subjective feelings / opinions about things that could not be provided because of technical limitations.

Description

System for tagging information and its method {A SYSTEM AND METHOD FOR PROVIDING INFORMATION INCLUDING USER'S SUBJECTIVE COMMENTS}

The present invention relates to providing certain information to a user over the Internet.

Many people in modern times get information through the Internet. Most of the information through the Internet is obtained through a general method such as a string search, for example, a keyword search, and the information is also used for obtaining an objective knowledge about the string. However, users often want to know other people's subjective feelings or opinions about certain things in addition to information such as simple objective knowledge. In order to satisfy the needs of users, beyond natural keyword processing, natural language processing (NLP) and a statistical approach for improving the accuracy of such natural language processing are used. However, the natural language processing method as described above also has limitations as a task of receiving input or feelings of other users. For example, the natural language processing has difficulty in analyzing the meaning of words such as homonyms and new words. However, there is a problem in that the context to be grasped is not known exactly according to the subject covered by the article, and furthermore, it is difficult to analyze exactly what the sentence evaluates when the nested sentences and directives are repeatedly used. That is, such natural language processing methods always involve a problem that the accuracy of search itself is low, and although a statistical approach is involved to improve the search accuracy of the natural language processing method even a little, the first natural language processing method itself Because of limitations, the practical effect of the statistical approach is not high.

The present invention has been devised to overcome the difficulties with such an analysis system. According to the present invention and method for providing tagging information according to the present invention, a subjective feeling / opinion for a predetermined object is received from a user, stored in a database, and a request for providing the tagging information for the predetermined object is received from another user. In one case, it may provide information about other people's feelings / opinions about the object in response. According to the present invention, users can be provided with information on people's subjective feelings, opinions, tendencies, and the like, which cannot be provided because of technical limitations.

According to an embodiment of the present invention, a method for providing tagging information includes: storing the tagging tagging information received from at least one user in a database to construct the tagging data; Receiving a request for providing a tagging result from at least one user, and in response thereto, performing an analysis operation on the tagging data and providing the same to the at least one user.

In addition, the system for providing the tagging information according to the present invention, the storage unit for storing the tagging target information and the form tagging information received from the at least one user as a form tagging data; A metadata transformation unit configured to transform the tagging data stored in the storage unit into metadata to structure an association relationship between the tagging target information and the tagging information; A similarity measurer configured to measure similarity between the tagging data stored in the storage unit and to correct the tagging data; And a search result providing unit configured to receive a search word from at least one user and to provide an adjective tagging result with reference to the metadata for the search word.

1 is a block diagram of a system for providing tagging information according to an embodiment of the present invention.
2 illustrates an example of tagging data stored in a storage unit according to an embodiment of the present invention.
FIG. 3 illustrates an example of a result in which the adjective tagging data of FIG. 2 is structured by an object-adjective tagging association according to an embodiment of the present invention.
4 illustrates an example of a result of structured adjective tagging data of FIG.
FIG. 5 is an example of a result of structured adjective tagging data of FIG. 2 structured by an additional information-adjective tagging association according to an embodiment of the present invention.
6 is an example of a screen for providing a tagging result according to an embodiment of the present invention.
7 is a flowchart for constructing tagging data according to an embodiment of the present invention.
8A and 8B illustrate an example of a web page screen provided to a user for receiving adjective tagging target information, additional information, and adjective tagging information from a user according to an embodiment of the present invention.
9 is a flowchart for providing an adjective tagging result according to an embodiment of the present invention.

Hereinafter, a method and a system for providing the tagging information according to the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram of a system for providing tagging information according to an embodiment of the present invention. Referring to FIG. 1, an adjective tagging information providing system 100 according to the present invention includes a storage unit 120, a metadata transformation unit 130, a similarity measuring unit 140, and a search result providing unit 150. . The tagging information providing system 100 is connected to one or more external devices 300 through the network 200, receives a predetermined request from the external device 300, or provides predetermined information to the external device 300. do.

When the tagging information providing system 100 receives the tagging target information and the tagging information from the external device 300, the system 100 stores the tagging information in the storage 120. The tagging target information may have various data formats that can be represented by a computer, such as a text, an audio file, an image, a video, and the like. The adjective tagging information is information representing a subjective feeling / opinion of the user with respect to the adjective tagging target information. According to a preferred embodiment of the present invention, one or more additional information other than the adjective tagging target information may be additionally received in order to describe the adjective tagging target in more detail. The additional information is information for more specifically defining the tagging target information. In one embodiment, when a subjective feeling / comment is received from a given user about "iPhone" (text file) of "Apple", the system according to the present invention is "iPhone", "Apple", " Expensive "is stored in the storage unit 120 as the tagging target information, the additional information, and the tagging information. In another embodiment, when a subjective feeling / comment is received from a given user that "Big Bang" "Big Bang Music Video (movie file)" is "Good", the system according to the present invention is a "Big Bang", "Big Bang Music Video". "," "Good" is stored in the storage unit 120 as the tagging target information, the additional information, and the tagging information. In FIG. 1, the storage unit 120 is illustrated as one database, but the storage unit 120 The tagging target information may be stored in different databases according to a data format (text, image, video, etc.) of the tagging target information.

The metadata transformation unit 130 includes an object-adjective tagging association 132, an object-additional information association 134, and an additional information-adaptation tagging association 136, and store the adjectives stored in the storage 120. Transform tagging data into metadata. The transformation into metadata is performed to provide a quick response (providing the adjective tagging result) to the case when a request for providing the adjective tagging result from a predetermined user is received later. The object-adjective tagging association unit 132 structures an association relationship between the adjective tagging target information and the adjective tagging information with respect to the adjective tagging data stored in the storage 120. The object-addition information association unit 134 structures the association relationship between the tagging target information and the additional information with respect to the tagging data stored in the storage unit 120. The side information-tagging tagging unit 136 structures an association relationship between the side information and the tagging information with respect to the tagging data stored in the storage unit 120.

Hereinafter, the operation of the metadata transformation unit 130 will be described in detail with reference to FIGS. 2-5. As an example, FIG. 2 illustrates an example of the tagging data stored in the storage unit 120, and FIGS. 3, 4, and 5 each represent an object-tagging tagging unit 132 and an object-added information association unit. 134 shows the results of the structured tagging data shown in FIG. 2 structured by each of the side information-stamping associations 136. Referring to FIG. 2, the storage unit 120 stores "best love", "pasta", "hard romance", "Lee Sun-gyun" and "Cheong-hee" as "adjective tagging target data", and "additional information 1". "Cha Seung-won", "MBC", "Drama", "Lee Sun-kyun", "Movie", "Chief Kang-hee", "Actress" are stored as "Additional information 2" as "Gong Hyo-jin", "Drama", "Cha Seung-won" , "MBC", "Lee Sun Kyun", "Choi Kang-Hee", "Pasta", and "Romantic Romance" are stored, and as "Additional Information 3", "MBC", "Gong Hyo Jin", "Cha Seung-won", "Seon Kyun Lee", "Strong "," Drama "," Gong Hyo-jin "are stored as" Additional information 4 "," funny "," fun "," fun "," impressive ", as" type tagging information ", "Good" is stored.

FIG. 3 is a diagram for describing data structured by the object-specific tagging association unit 132. Referring to FIG. 3, each of the adjective tagging target data ("best love", "pasta", "hard romance", "Lee Sun-gyun", and "Choi Kang-hee") has each of the adjective tagging results ("fun", "no fun", " Association "with at least one of" fun "," inspiring "," good "). Specifically, the description of the best tagging information "best love" is described as "best love", "fun", "fun", "fun", "fun", "fun" tagging Stored in association with the object information (see first through fifth data in FIG. 2), and by the object-to-tagging association unit 132, the association is defined as "category 1" and " Can be structured as category 2 ". In one embodiment, the side information of the structured data group may be aggregated by the object-adjective tagging association 132, eg, "category 1" refers to the adjective tagging object "best love" as "fun". As a data group associated with the adjective tagging information (refer to the first to fourth data in FIG. 2), the data included in the “category 1” includes additional information of “Cha Seung-won”, “Gong Hyo-jin”, “MBC”, and “drama”. 3, 3, 2, and 4, respectively, " category 2 " is a data group that associates an adjective tagging object of "best love" with adjective tagging information of "no fun" (fifth data in FIG. 2). Reference), "Cha Seung-won", "Gong Hyo-jin", "MBC", "drama" each of the additional information including each one can be counted.

4 is a diagram for describing data structured by the object-additional information association unit 134. Referring to FIG. 4, each of the adjective tagging target data ("best love", "pasta", "hard romance", "Seon Kyun Lee", "Cheong-hee Choi") has additional information ("Cha Seung-won", "Gong Hyo-jin", "MBC", An association is formed with at least one of "drama", "choi kang hee", "strongest", "movie", "sun sun kyun", "actor", "pasta", and "hard romance". Specifically, referring to "best love" of the adjective tagging information as an example, "best love" is stored in association with additional information such as "Cha Seung-won", "Gong Hyo-jin", "MBC", and "drama" (FIG. 2). In the first to fifth data), by the object-additional information association unit 134, the association is " category 1 " 2 "(having" Gong Hyojin "as additional information)," category 4 "(having" MBC "as additional information), and" category 6 "(having" drama "as additional information). In one embodiment, the adjective tagging information for the data group structured by the object-side information association unit 134 may be aggregated, for example, "category 1" refers to the adjective tagging object "best love" as "chaseungwon". (See the first, second, fourth, and sixth data in FIG. 2) associated with the additional information, the data contained in "category 1" is an adjective tagging information of "fun" and "fun". 3 and 1, respectively, and "category 2" is a data group which associates the adjective tagging object "best love" with the additional information "Gong Hyojin" (first, second, fourth, first in FIG. 2). 6 data), the data included in the "category 2" may be counted three times and once, each containing the tagging information of "fun" and "not funny".

5 is a diagram for describing data structured by the side information-tagging tag association unit 136. Referring to FIG. 5, additional information ("Cha Seung-won", "Gong Hyo-jin", "MBC", "Drama", "Cheong-hee Choi", "Strong", "Movie", "Lee Sun-gyun", "Actress", "Pasta", Each has a relationship with at least one of the adjective tagging results ("fun", "fun", "fun", "inspiring", "good"). Specifically, referring to "chaseungwon" of the additional information as an example, "chaseungwon" is stored in association with the adjective tagging information of "fun", "fun" (Fig. 2, the first, second, fourth , See the sixth data), by the side information-tagging tag association unit 136, the association relationship is " category 1 " (adjective tagging information " funny "), " category 2, " Can be structured as "(adjective tagging information" funny "). In one embodiment, the adjective tagging target information for the data group structured by the side information-adjective tagging association 136 may be aggregated, such as "category 1" for the adjective tagging target 3 times. And "category 2" includes the adjective tagging object "best love" once.

The structured data in the object-adjective tagging unit 132, the object-additional information association unit 134, and the additional information-adjective tagging unit 136 facilitates ranking by the search ranking unit 150 described later. In addition, the inclusion relationship used for the similarity calculation of the similarity measurer 136 may be easily identified.

1 again, the similarity measurer 140, in cooperation with the metadata modifying unit 130, measures the similarity measure for the search terms included in the received request and based on the metadata modifying unit 130, To support the transformation of metadata. The similarity measurer 140 finds a typo or other notation of a foreign language included in the tagging data stored in the storage 120 and corrects the corresponding data. In one embodiment, the similarity measurer 140 corrects a typo using a set including relation algorithm, frequency calculation, letter-to-letter distance calculation, and the like. For example, when the data of FIG. 2 is stored as the tagging data in the storage 120, the similarity measurement unit 140 refers to the 16th data of FIG. It is defined as having a relation A of 1}. In addition, the similarity measurement unit 140 refers to the twelfth data of FIG. 2, and defines that "CHOI Kang-Hee" has a relationship B that is "inspiring" and {hard romance: 1}. Based on this, the similarity measurer 140 defines sets {A, B} for the strongest. The similarity measurer 140 defines, with reference to the tenth data of FIG. 2, that "strongest" has a relationship A of "fun" and {hard romance: 1}. That is, the similarity measuring unit 140 sets the set {A, B} for the strongest and the set {A} for the strongest. Since the set {A} is a subset of the set {A, B}, the similarity measurer 140 considers the frequency calculation / letter-to-letter distance calculation value when such a set relationship (set-subset) is set, " It is determined that there is a similarity between "Kang Kang-Hee" and "Best Gang", and the "Best Gang" is recognized as a typo of "Kang Kang-Hee".

When there is a request for providing a tagging result, the search result providing unit 150 cooperates with the storage unit 120 and the metadata transformation unit 130 to rank and provide the stored tagging data. As an example, the search result providing unit 150 may rank the tagging data based on whether the information included in the request for providing the tagging result matches the form tagging data stored in the system 100. 6 illustrates an example of an adjective tagging result providing screen. In FIG. 6, the request for providing an adjective tagging result includes "best love" as a search word and "Gong Hyo-jin" and "MBC" as additional search information. For example, when the request of FIG. 6 is received and the tagging data of FIG. 2 is stored in the storage unit 120, the search result providing unit 150 stores the tagging information corresponding to the received search word. By giving a weight w1 and giving a weight w1 to data having additional information that matches the received additional search information (when receiving a plurality of additional search information, a different weight or the same weight is assigned to each additional search information. Scores are assigned to each type of tagging data. The equation for obtaining the score can be generalized as follows.

Score = w1 * (matching adjective tagging target and search term) + w2 * (matching additional search information 1 and additional information + matching of additional search information 2 and additional information +… + additional search information n and additional information Match)

Hereinafter, a description method for providing tagging information according to the present invention will be described. In accordance with another aspect of the present invention, a method of providing adjective tagging information includes constructing adjective tagging data by receiving adjective tagging target information and adjective tagging information from predetermined users, and receiving a predetermined search request from other users and adjective tagging thereof. It can be broken down into steps that provide results.

7 is a flowchart for constructing tagging data according to an embodiment of the present invention. The steps described in FIG. 7 are performed to collect the subjective feelings / comments of the user for the predetermined adjective tagging target information. In operation 700, the tagging target information and the tagging information are received from the user. The user may input the tagging target information and the tagging information by using a predetermined input device. 8A and 8B illustrate an example of a web page screen provided to a user for receiving adjective tagging target information, additional information, and adjective tagging information from a user according to an embodiment of the present invention. The method and system for providing tagging information according to the present invention can receive necessary information by presenting the web page to the user and allowing the user to input corresponding information. The web page 800 includes a target field 810, an additional information field 820, and a tagging information field 830 for receiving type tagging target information from a user. In the embodiment shown in Fig. 8A, the user inputs "iPhone 4" in "text" format as the tagging target information, inputs "Apple", "KT", "mobile phone" as additional information, and as tagging information. You entered a subjective feeling / opinion that “expensive”. The target field 810 may be configured to further provide a selection window 840 that allows a user to select a format of the type tagging target information to be input, and in the embodiment shown in FIG. 8A, the selection window 840. Text format was selected. In the embodiment shown in Fig. 8B, the user inputs "Big Bang Music Video" in "Video" format as the adjective tagging target information, inputs "Big Bang", "Music Video" as additional information, and "Good" as tagging information. Entered a subjective feeling / opinion. In the embodiment illustrated in FIG. 8B, it can be seen that the big bang music video input as the tagging target information is uploaded in the form of a video file stored in the hard computer.

Returning to FIG. 7, in step 710, the tagging target information and the tagging information (including additional information according to an embodiment) received from the user are stored in the database. The tagging target information may be classified into text, image, video, etc. according to the format of the information (eg, text, image, video, etc.) and stored in each database or in one integrated database regardless of the format. In step 720, data stored in the database is transformed into appropriate metadata. The modification to the meta data is performed to promptly provide the adjective tagging result in response to receiving a request for a predetermined search target from other users in the future, which has been described above.

9 is a flowchart for providing a tagging result according to an embodiment of the present invention. The steps described in FIG. 9 are performed to receive a given search word from a user and to provide adjective tagging results for it. In operation 900, a search term is received from a user. In some embodiments, the search additional information other than the search word may be additionally received from the user. For example, when a user wants to search for an adjective tagging result related to "secret garden", the user inputs only "secret garden" in the adjective tagging information field and displays the adjective tagging result for "secret garden". Can be provided. In another embodiment, the user may be provided with an adjective tagging result for a drama or a secret garden by inputting “secret garden” and “drama” into each of the adjective tagging target information field and the additional information field.

9, in step 910, the stored tagging data is ranked and provided based on the search word received from the user. The detailed ranking operation is as mentioned above.

As described above, embodiments of the present invention have been described in order to help understanding of the present invention, but as will be appreciated by those skilled in the art, the present invention is not limited to the specific embodiments described herein. Accordingly, various modifications, reconfigurations, and substitutions may be made without departing from the spirit and scope of the invention as indicated by the claims.

Claims (3)

delete A plurality of users may store adjective tagging target information representing a data format of any one of text, video, and image files, adjective tagging information representing a subjective opinion about the tagging target information, and additional information defining the tagging target information. Receiving and storing from the;
Converting, by the meta data transformation unit, the meta tagging target information, the meta tagging information, and the additional information stored in the storage unit into metadata to structure an association relationship therebetween;
A similarity measurer sets a set relationship from the structured association relationship using a set including relation algorithm, and the similarity between the adjective tagging information information or the side information through the frequency calculation or the letter-to-letter distance calculation from the set relationship. Determining;
The metadata transformation unit restructuring the association according to the similarity determined by the similarity measurer; And
The search result providing unit receives a search word corresponding to the adjective tagging target information and search additional information corresponding to the additional information from any one of the plurality of users, and according to the restructured relationship from the metadata transformation unit. And ranking the adjective tagging information corresponding to the search word and the search additional information and providing the adjective tagging information to the arbitrary user. The tagging target information representing the data format of any one of text, video and image files, the tagging information representing the subjective opinion about the tagging target information and the additional information defining the tagging target information from a plurality of users A storage unit for receiving and storing;
Converting the tagging target information, the tagging tagging information, and the additional information stored in the storage unit to meta data to structure an association relationship, and converting the tagging target information converted to the metadata to each other or to each other A metadata transformation unit for restructuring the association according to the similarity between the metadata;
The set relationship is set using a set inclusion relation algorithm from the association structure structured by the metadata transformation unit, and the adjective tagging target information is mutually or additional information by calculating a frequency or a letter-to-letter distance from the set relation. A similarity measuring unit for determining similarity between each other;
Receiving a search term corresponding to the adjective tagging target information and search additional information corresponding to the additional information from any one of the plurality of users, and refer to the structured association to correspond to the search term and search additional information Search tagging information providing system comprising a; search result providing unit for ranking the adjective tagging information provided to the arbitrary user.

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