WO2019177182A1 - Multimedia content search apparatus and search method using attribute information analysis - Google Patents

Abstract

The present invention relates to a multimedia content search apparatus and search method using attribute information analysis and, more specifically, to a multimedia content search apparatus and search method using attribute information analysis, which acquire a multimedia content search word inputted through voice recognition or text so as to determine whether to perform a text keyword search or a similar attribute search, thereby outputting search result information of multimedia content when the text keyword search is performed and outputting search result information of multimedia content having similar attributes when the similar attribute search is performed, and thus are capable of providing pieces of multimedia content having high similarity to attribute information of multimedia content to be searched for.

Description

Multimedia content retrieval device and search method through attribute information analysis

The present invention relates to an apparatus and method for searching multimedia contents through attribute information analysis. More particularly, the present invention relates to a method of searching a text keyword by acquiring a search word of multimedia content input by speech recognition or text, or performing a similar attribute search. The multimedia information to be searched is output by outputting the search result information of the multimedia contents when performing the text keyword search by determining whether to perform the search. The present invention relates to a multimedia content retrieval apparatus and a retrieval method through attribute information analysis capable of providing multimedia contents having high similarity with the attribute information of the content.

In the case of Naver, a portal company such as the following, and a search engine such as Google, the user can search for the latest keyword information related to the keyword of the user's search query, or a specific operator grouping the keywords. Through this, efforts are made to provide information closer to the information desired by the user.

These companies that provide a search service do not respond to only the keywords entered by the user, but give associations between a plurality of keywords entered by a plurality of users or keywords entered sequentially. In addition, a service providing a related keyword has been developed.

The related search word providing service not only facilitates a user's search, but also serves as one piece of information.

There have been many technical attempts to extract related words by giving associations between keywords constituting a search word.

Related patent documents include "Keyword Visualization Apparatus and Method thereof (Publication No. 10-2011-0035001, hereinafter referred to as" prior art 1 ").

The prior art 1 relates to a keyword visualization apparatus and a method thereof, comprising: a keyword extracting unit extracting a keyword from data obtained through the Internet; A frequency analysis unit for raising a frequency of occurrence of the keyword each time a keyword is extracted; An association analysis unit for increasing association values between the extracted keywords when a plurality of keywords are extracted from a single data; An information storage unit for storing the extracted keywords and storing occurrence frequency values for each keyword and correlation values between the keywords; And a graph having a plurality of nodes and edges is displayed on the screen by using a plurality of keywords, occurrence frequency values of the keywords, and correlation values between the keywords, and each node of the graph is displayed with keywords. Nodes with high values are displayed in large sizes, and nodes with low keyword occurrence frequencies are displayed in small sizes.If the correlation values between keywords of two nodes connected by edges are high, the edges are displayed with thick edges. If it is low, characterized in that it comprises a visualization processing unit for processing so that the edge is displayed thin, suggests a change in the frequency of occurrence of the keyword and the degree of association between the keywords.

Other related patent documents include "a search method and system using the ranking of keywords (patent registration no. 10-1072113, hereinafter referred to as" prior art 2 ").

The prior art 2 is a search method and system using an association ranking of a keyword, comprising: an index module for generating an independent index by indexing a property of a keyword and an association index by indexing a correlation between a keyword and another keyword; An association score calculation module that quantifies an association degree between a keyword and another keyword based on an association index as an association score; A rank score calculation module that calculates a rank score according to the use purpose based on the association score and the independent index; And a search module for providing a related keyword for the search term based on the ranking score.

However, Prior Art 2 only discloses a technical idea of extracting a related search word for a keyword, and does not provide general information on the related search.

However, Prior Art 1 provides a graph of ranking among related search terms for a keyword to provide which related search terms for a search term is the most frequently used. However, the related art automatically searches for the highest frequency among related search terms. It is not much different from the known technology ranking at the top of the related search word list.

On the other hand, search systems equipped with artificial intelligence-based can be divided into crawler-based, directory-based, hybrid search, and meta-search method in terms of search method.

The crawler-based retrieval system downloads and stores documents on the web in its database using an automated agent program called spider, crawler, webbot, and the like.

The user's search request is handled by finding the search keyword in the index of the stored web document and providing a link to that document.

This is a good example of the Google search system.

In addition, in the directory-based search system, web sites are classified and stored in a predetermined directory by a person, and the stored websites are ranked by a predefined rule.

The user's search request is processed by grouping the web documents found by keyword matching by directory.

This is the case with Yahoo and Naver search system. Also,

In the hybrid search system, the crawler method and the directory method are used together and generally provide a better search result to the user.

Microsoft's MSN search is a good example of this.

In addition, the meta-search system utilizes search algorithms and evaluation criteria of other search systems.

That is, the search results of different search systems are merged and provided to the user.

Metacrawler system is a typical example.

On the other hand, there is a method of implementing a web-based Hangul information retrieval system, which is a function and an implementation method of the Hangul search engine, which is a key part in implementing a web-based Hangul information retrieval system, especially nouns, investigations, and stopwords Using various Hangul dictionaries etc., this paper suggests how to use morphological analysis suitable for the characteristics of Hangul.

However, as the web-based Korean information retrieval system and the crawler-based, directory-based, hybrid search, and meta-search methods search for information using only search keywords, the user cannot reflect the desired attributes. There is a fatal drawback that it does not provide the exact content it really wants.

Therefore, there is a need for a technique that can perform a similar attribute search that is closest to or matching the desired attribute of the user, and through this technique, analysis of attribute information that can output search result information of multimedia content having similar attributes is performed. Through multimedia content retrieval technology has become necessary.

<Preceding technical literature>

Prior Art 1 Republic of Korea Patent Publication No. 10-2011-0035001

Prior Art 2 Republic of Korea Patent No. 10-1072113

Therefore, the present invention has been proposed in view of the above-described problems of the prior art, and a first object of the present invention is to perform a text keyword search by acquiring a search word of multimedia content input by speech recognition or text, or similar property search. In this case, the search result information of the multimedia content is output when the text keyword search is performed and the search result information of the multimedia content having the similar property is output.

The second object of the present invention is to provide the similarity matching property analysis unit 530 to the attribute information and the multimedia content attribute assignment unit 520 for the search word stored in the search term attribute value information DB 517 when performing the similar attribute search. Similarity matching analysis is performed with the multimedia content attribute information assigned by the present invention, thereby providing search result information of the multimedia content having attributes similar to the intention of the search word (question).

The third object of the present invention is to provide a content crawling module 522, to collect a plurality of multimedia content information from the content server 560 to store in the content storage DB to extend the operation range of the attribute information, the content attribute allocation model By providing the module 524, the attribute information is allocated to each multimedia content stored in the content storage DB 523 and provided to the content information search module.

In order to achieve the problem to be solved by the present invention, the multimedia content retrieval apparatus through attribute information analysis,

A search start unit 100 for acquiring a search word of multimedia content input by voice recognition or text and providing search execution request information to the attribute search execution determining unit 200;

When obtaining the search execution request information from the search start unit 100, it is determined whether to perform a text keyword search or a similar attribute search, and as a result of the determination, the text keyword search is performed when the text keyword search is performed. The attribute search decision unit 200 which provides the text keyword search request information to the unit 300, and provides the similar property search request information to the attribute similarity search unit 500 when performing the similar attribute search as a result of the determination; ,

A text keyword search unit 300 which performs a text keyword search when obtaining the text keyword search request information provided from the attribute search performing determination unit, and provides the search result information to the text keyword result output unit;

A text keyword result output unit 400 for outputting search result information of the text keyword provided from the text keyword search unit;

An attribute similarity search means 500 which performs a similar attribute search when obtaining similar attribute search request information provided from the attribute search execution determination unit and provides the search result information to the attribute similarity search result output unit 500;

And an attribute similarity search result output unit 600 for outputting search result information of the similar attribute provided from the attribute similarity search unit 500.

In addition, the multimedia content retrieval method by analyzing the attribute information,

A search start step (S100) of providing a search execution request information to the attribute search performing determination unit 200 by obtaining a search word of the multimedia content inputted by voice recognition or text by the search start unit 100;

When the attribute search execution unit 200 obtains the search execution request information from the search start unit 100, it is determined whether to perform a text keyword search or a similar attribute search. When performing a search, the text keyword search request information is provided to the text keyword search unit 300, and as a result of the determination, when the similar property search is performed, the similar property search request information is provided to the attribute similarity search unit 500. Attribute search determination step (S200),

When the text keyword search unit 300 obtains the text keyword search request information provided from the attribution search execution determination unit 200, the text keyword search unit performs a text keyword search and provides the search result information to the text keyword result output unit. Step S300,

A text keyword result output step (S400) for the text keyword result output unit 400 to output search result information of the text keyword provided from the text keyword search unit 300;

When the property similarity search means 500 obtains the similar property search request information provided from the property search execution decision unit 200, the property similarity search is performed and the search result information is provided to the property similarity search result output unit. Search step (S500),

The attribute similarity search result output unit 600 includes an attribute similarity search result output step S600 for outputting search result information of similar attributes provided from the attribute similarity search unit 500.

Whether to perform a text keyword search by acquiring a search word of multimedia content input by speech recognition or text through a multimedia content search apparatus and a search method through attribute information analysis according to the present invention having the above-described configuration and operation, or similar property search Search result of multimedia content is output when performing a text keyword search by determining whether to perform a text keyword search, and output search result information of multimedia content having a similar property when performing a similar property search. The present invention provides an effect of providing a multimedia content search result using a keyword method and of providing a multimedia content search result most similar to a search word (question) that a user wants to search through a similar property search.

In addition, by providing a similarity matching analysis unit, when performing a similar attribute search, by performing a similarity matching analysis with the attribute information about the search term stored in the search term attribute information DB and the multimedia content attribute information assigned by the multimedia content attribute assignment unit , By providing search result information of multimedia contents having attributes similar to the intention of the search term (question), it provides multimedia contents that match the attributes (atmosphere, emotion, etc.) desired by the user, thereby increasing the reliability of the search. Will be effective.

In addition, the amount of information of the multimedia content changes over time, and accordingly, the attributes of a specific object change from time to time. By reflecting this variably by the multimedia content attribute assignment unit, various multimedia contents that change in real time may be reflected in a search. Will be effective.

1 is an overall configuration diagram schematically showing an apparatus for retrieving multimedia contents through attribute information analysis according to a first embodiment of the present invention.

2 is an exemplary view in which a movie of a conventional similar atmosphere is not searched.

3 is an overall block diagram of an apparatus for retrieving multimedia contents through attribute information analysis according to a first embodiment of the present invention.

4 is an exemplary view showing a search result output when a text keyword is searched.

5 is an exemplary view of a similar property search result output through a multimedia content search apparatus through analysis of property information according to a first embodiment of the present invention.

6 is a block diagram of attribute similarity retrieval means of a multimedia content retrieval apparatus by analyzing attribute information according to the first embodiment of the present invention;

7 is a block diagram of a keyword attribute analysis unit of a multimedia content retrieval apparatus through attribute information analysis according to the first embodiment of the present invention.

8 is a block diagram of a multimedia content attribute assignment unit of the multimedia content retrieval apparatus through attribute information analysis according to the first embodiment of the present invention.

9 is a flowchart illustrating a multimedia content retrieval method through attribute information analysis according to a first embodiment of the present invention.

10 is a flowchart illustrating an attribute similarity search step of a multimedia content search method through analysis of attribute information according to a first embodiment of the present invention;

<Description of the code>

100: start of search

200: Attribute search performance decision unit

300: text keyword search unit

400: text keyword result output unit

500: attribute similarity search means

560 content server

600: attribute similarity search result output unit

The following merely illustrates the principles of the invention. Therefore, those skilled in the art, although not explicitly described or illustrated herein, can embody the principles of the present invention and invent various devices that fall within the spirit and scope of the present invention.

In addition, all conditional terms and embodiments listed herein are in principle clearly intended to be understood only for the purpose of understanding the concept of the invention and are not to be limited to the specifically listed embodiments and states. do.

In describing the present invention, terms such as first and second may be used to describe various components, but the components may not be limited by the terms.

For example, without departing from the scope of the present invention, the first component may be referred to as the second component, and similarly, the second component may also be referred to as the first component.

When a component is referred to as being connected or connected to another component, it may be understood that the component may be directly connected to or connected to the other component, but there may be other components in between. .

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention, and singular forms may include plural forms unless the context clearly indicates otherwise.

In this specification, the terms including or including are intended to designate that there exists a feature, a number, a step, an operation, a component, a part, or a combination thereof described in the specification, and one or more other features or numbers, It can be understood that it does not exclude in advance the possibility of the presence or addition of steps, actions, components, parts or combinations thereof.

<First Embodiment>

In accordance with a first aspect of the present invention, there is provided an apparatus for retrieving multimedia contents through attribute information analysis.

A search start unit 100 for acquiring a search word of multimedia content input by voice recognition or text and providing search execution request information to the attribute search execution determining unit 200;

When obtaining the search execution request information from the search start unit 100, it is determined whether to perform a text keyword search or a similar attribute search, and as a result of the determination, the text keyword search is performed when the text keyword search is performed. The attribute search decision unit 200 which provides the text keyword search request information to the unit 300, and provides the similar property search request information to the attribute similarity search unit 500 when performing the similar attribute search as a result of the determination; ,

A text keyword search unit 300 which performs a text keyword search when obtaining the text keyword search request information provided from the attribute search performing determination unit, and provides the search result information to the text keyword result output unit;

A text keyword result output unit 400 for outputting search result information of the text keyword provided from the text keyword search unit;

An attribute similarity search means 500 which performs a similar attribute search when obtaining similar attribute search request information provided from the attribute search execution determination unit and provides the search result information to the attribute similarity search result output unit 500;

It is characterized in that it comprises a property similarity search result output unit 600 for outputting the search result information of the similar property provided from the attribute similarity search unit 500.

And, the attribute search determination unit 200,

Applying at least one of a first mode of pre-determining whether to perform a text keyword search or a similar property search according to a service domain, and a second mode of analyzing a sentence entered as a search word. It is characterized by.

And, the attribute similarity search means 500,

A search word attribute analyzer 510 for analyzing linguistic attribute information included in a search word of multimedia content input through speech recognition or text;

A multimedia content attribute allocator 520 for acquiring and storing multimedia contents from the content server 560 and allocating attribute information to the stored multimedia contents;

It provides multimedia content request information including attribute information similar to linguistic attribute information of a search word to the multimedia content attribute assigning unit 520, obtains multimedia content list information from the multimedia content attribute assigning unit 520, and obtains the multimedia content list information. A similarity matching analysis unit 530 for performing a similarity matching analysis of multimedia contents included in the multimedia contents list information;

A similarity candidate group extracting unit 540 for sequentially extracting multimedia contents according to candidate group numbers from multimedia contents having the highest similarity with reference to a preset candidate group number;

And a similarity reference multimedia content sorting unit 550 for sorting the multimedia contents extracted according to the number of candidate groups according to similarity and providing the sorted multimedia contents to the attribute similarity search result output unit 600. do.

And, the search word attribute analysis unit 510,

The machine learning model module 512 provides information on requesting interpretation of linguistic attributes included in a search word of multimedia content input through speech recognition or text, and provides linguistic attribute information included in a search word interpreted from the machine learning model module. A natural language processing module 511 for providing the query attribute assignment module 513;

Machine learning model module for providing linguistic attribute information interpreted as natural language processing module by interpreting linguistic attributes included in search term when obtaining information on interpretation of linguistic attributes included in search term from natural language processing module. 512);

A knowledge information DB 514 that stores attribute type information refined into attribute types that can be matched with attribute information of multimedia content;

Obtains the linguistic attribute information included in the search word provided by the natural language processing module, extracts the attribute type information from the knowledge information DB based on the obtained linguistic attribute information, allocates the attribute for the search term, and the attribute for the assigned search term. A search word attribute assignment module 513 for providing information to the search term attribute value conversion module 515;

When obtaining the attribute information for the search term provided from the search term attribute assignment module 513, the probability model calculation request information is provided to the attribute model module 516, and the probability value calculated from the attribute model module 516 is obtained to provide the search term. A keyword attribute value conversion module 515 for converting the attribute value into an attribute value and providing the result to the keyword attribute value information DB 517;

An attribute model module 516 for calculating a probability value through language modeling when obtaining the probability value calculation request information from the keyword attribute value conversion module 515 and providing the calculated probability value to the keyword attribute value conversion module 515;

And a search word attribute value information DB 517 that stores the attribute value for the search word provided by the search word attribute value conversion module 515.

And, the multimedia content attribute assignment unit 520,

A content interlocking module 521 for providing multimedia content information to the content crawling module 522 in association with the content server 560;

A content crawling module 522 for collecting a plurality of multimedia content information provided from the content interworking module 521 and storing the multimedia content information in a content storage DB to expand the operation range of the attribute information;

A content storage DB 523 for storing multimedia content information provided from the content crawling module 522 and attribute information allocated to each multimedia content;

A content attribute assignment model module 524 for allocating attribute information for each multimedia content stored in the content storage DB 523;

A content property information analysis module 525 for analyzing the property information of each multimedia content assigned by the content property assignment model module 524 and providing the same to the content information search module;

The attribute information of each multimedia content analyzed by the content attribute information analysis module 525 is provided to the similarity matching property analysis unit 530, and similar property information is similar to the linguistic property information of the search word from the similarity matching property analysis unit 530. A content information retrieval module 526 for providing multimedia content list information including similar attribute information from the content storage DB 523 to the similarity matching property analysis unit 530 when acquiring the included multimedia content request information; Characterized in that comprises a.

And, the similarity matching property analysis unit 530,

The similarity matching analysis may be performed using the attribute information of the search word stored in the search term attribute value information DB 517 and the multimedia content attribute information allocated by the multimedia content attribute assigning unit 520.

In addition, according to the first embodiment of the present invention, a method for retrieving multimedia contents by analyzing attribute information includes:

A search start step (S100) of providing a search execution request information to the attribute search performing determination unit 200 by obtaining a search word of the multimedia content inputted by voice recognition or text by the search start unit 100;

When the attribute search execution unit 200 obtains the search execution request information from the search start unit 100, it is determined whether to perform a text keyword search or a similar attribute search. When performing a search, the text keyword search request information is provided to the text keyword search unit 300, and as a result of the determination, when the similar property search is performed, the similar property search request information is provided to the attribute similarity search unit 500. Attribute search determination step (S200),

When the text keyword search unit 300 obtains the text keyword search request information provided from the attribution search execution determination unit 200, the text keyword search unit performs a text keyword search and provides the search result information to the text keyword result output unit. Step S300,

A text keyword result output step (S400) for the text keyword result output unit 400 to output search result information of the text keyword provided from the text keyword search unit 300;

When the property similarity search means 500 obtains the similar property search request information provided from the property search execution decision unit 200, the property similarity search is performed and the search result information is provided to the property similarity search result output unit. Search step (S500),

The attribute similarity search result output unit 600 includes an attribute similarity search result output step S600 for outputting search result information of similar attributes provided from the attribute similarity search unit 500.

At this time, the attribute similarity search step (S500),

A keyword attribute analysis step (S510) for the keyword attribute analyzer 510 to analyze linguistic attribute information included in a keyword of a multimedia content input through speech recognition or text;

A multimedia content attribute assignment step (S520) of the multimedia content attribute assignment unit 520 acquiring and storing multimedia content from the content server 560 and allocating attribute information to the stored multimedia content;

The similarity matching property analysis unit 530 provides the multimedia content property assignment unit 520 with multimedia content request information including property information similar to the linguistic property information of the search word, and the multimedia content from the multimedia content property assignment unit 520. A similarity matching analysis step (S530) of acquiring list information and performing similarity matching analysis of multimedia contents included in the obtained multimedia contents list information;

A similarity candidate group extracting step 540 for extracting, by the similarity candidate group extracting unit 540, multimedia contents having the highest similarity sequentially from the multimedia contents having the highest similarity, according to the candidate group number;

Similarity-based multimedia content sorting unit 550 sorts the multimedia contents extracted according to the number of candidate groups according to similarity, and provides similarity-based multimedia content sorting step to provide the sorted multimedia contents to the attribute similarity search result output unit 600. (S550); characterized in that it comprises a.

Hereinafter, an embodiment of a multimedia content search apparatus and a search method through attribute information analysis according to the present invention will be described in detail.

1 is an overall configuration diagram schematically showing an apparatus for retrieving multimedia contents through attribute information analysis according to a first embodiment of the present invention.

As shown in FIG. 1, the apparatus 1000 for retrieving multimedia contents through the analysis of attribute information of the present invention obtains and stores multimedia contents from the content server 560, and allocates and manages attribute information to the stored multimedia contents. to be.

In addition, the present invention, the multimedia content search apparatus 1000 through attribute information analysis acquires a search word of multimedia content input by voice recognition or text, and determines whether to perform a text keyword search or a similar property search. When the text keyword search is performed, the search result information of the multimedia content is output.

This uses a text keyword-based search algorithm that is generally used, but there is a limit in the search for multimedia content similar to the attribute information desired by the user.

Specifically, in the case of searching for a movie that is multimedia content, the conventional text keyword based search has a problem of being searched again with the same title, and a movie having a similar name and a completely different content is recommended. There was a serious problem that the movies were not searched at all.

In other words, the user cannot search for a movie that has a similar mood, emotion, or the like.

For example, as shown in FIG. 2, when a search term 'movie like a love reality' is inputted, a movie having a similar atmosphere is not searched for by the search term.

It was fun after the user saw a movie called Love Actual, and wanted to search for a movie with a similar mood, and if they searched with the keyword 'Love Actually', the result would be similar but not at all. In particular, some of the recommendation lists recommend a movie with a very low audience rating, so that the user does not feel the recommendation effect.

In summary, in the conventional search keyword method, a problem of failing to perform a similar attribute search and the amount of information change according to the passage of time, and accordingly, an attribute of a specific target also changes every time, and cannot be variably reflected.

Therefore, in the present invention, by providing the above-described text keyword-based search function, by providing a structural feature for performing a similar property search, the search results of multimedia content having similar properties when performing a similar property search By outputting the information, it is possible to provide multimedia contents having high similarity to the attribute information of the multimedia contents to be searched.

In addition, the present invention, through the configuration as described above, to determine whether to proceed to the existing keyword search or similar property search and the attributes of each multimedia content (warm, touching, fun, etc.) The similarity between the constructive feature that assigns the attribute value of the searched multimedia through the constructive feature and natural language processing (data crawling, statistical modeling, etc.) and the comparable feature and attribute information that are numerically calculated (language modeling) It provides a constructive feature for recommending high multimedia content (comparison value).

Detailed configuration means for performing a similar attribute search will be described in detail with reference to the following drawings.

3 is a block diagram of an apparatus for retrieving multimedia contents through attribute information analysis according to a first embodiment of the present invention.

As shown in FIG. 3, the present invention provides a multimedia content search apparatus 1000 through attribute information analysis. The search start unit 100, the attribute search execution determination unit 200, the text keyword search unit 300, and the text keyword result are shown. It comprises an output unit 400, the attribute similarity search means 500, the attribute similarity search result output unit 600.

Through the above configuration, the present invention provides a text keyword type search and an attribute similarity type search.

Specific structural features for providing the search method will be described as follows.

The search start unit 100 obtains a search word of multimedia content input through voice recognition or text and provides search execution request information to the attribute search execution determining unit 200.

The search start unit includes a natural language processing module for speech recognition, and extracts a user's command target value from the speech recognition result text processed by the natural language processing module.

Embedded Natural Language Understanding technology incorporates a natural language processing module using a rule-based algorithm or statistical model inside an electronic device, so that the user's final goal in speech recognition text is a command. It means the method of automatically extracting the intention (Intention, Goal) and the specific named object, it is to extract the command target value of the user from the speech recognition result text processed by the natural language processing module.

Since the technique of extracting the command target value of the user is a general technique, a detailed description thereof will be omitted.

In addition, the search start unit may configure a voice recognition engine, through which the function of extracting a recognition result value by recognizing a result close to a word or sentence previously input as a command based on the extracted command target value of the user. Done.

In other words, speech recognition is performed based on recognition grammars that can be understood by a recognizer, and a list of recognition targets is determined, and only the target list has a structure that can be output as a recognition result.

In this case, the search start unit 100 obtains a search word of the multimedia content input through voice recognition or text and provides the search execution request information to the attribute search execution determining unit 200.

For example, if a user inputs a movie such as a love act by voice or text, it can be referred to as a search word for requesting multimedia content by referring to a love act, a movie, and the like. It will be provided to the search performance determination unit 200.

The attribute search determining unit 200 determines whether to perform a text keyword search or a similar attribute search when obtaining the search execution request information from the search start unit 100.

In detail, the determination of whether to perform a text keyword search or a similar property search is performed in at least one of a first mode for determining according to a service domain and a second mode for analyzing and determining a sentence input by a search word. It is characterized by applying the mode.

The first mode or the second mode may be set in advance by an administrator.

That is, when the first mode is set to determine whether to perform a text keyword search or similar property search, whether to perform a text keyword search with reference to the service domain address or similar property search is performed. Is determined.

For example, a text keyword search is set for a domain address of 'www.naver.com', and a similar attribute search is set for a domain address of 'www.google.com'.

Alternatively, when the second mode is set by the administrator, a sentence input as a search word is analyzed to determine whether a keyword corresponding to a similar attribute search exists.

For example, if a user obtains a search word that is intended to search for similar attributes, such as 'same', 'similar', 'same', etc., it may be understood that this is to perform a similar attribute search.

As a result of the determination, the attribution search performing decision unit 200 provides the text keyword search request information to the text keyword search unit 300 when the text keyword search is performed.

In this case, when the text keyword search unit 300 obtains the text keyword search request information provided from the attribute search performing determination unit, the text keyword search unit 300 performs a text keyword search by referring to the text keyword 'love actual', The search result information including 'Love Actually' is provided to the text keyword result output unit.

In this case, the text keyword result output unit 400 outputs search result information of the text keyword provided from the text keyword search unit.

That is, as shown in FIG. 4, since all search result information including the keyword 'love reality' is output, the probability of providing a desired search result is reduced.

This is a problem caused by not reflecting the attribute information desired by the user. The present invention is characterized by providing a similar attribute search method while providing a general text keyword search method.

To this end, the attribute search performing decision unit 200 provides similar attribute search request information to the attribute similarity search unit 500 when performing the similar attribute search.

That is, when the result analyzed by any one of the first mode preset according to the service domain and the second mode analyzing the sentence inputted by the search word corresponds to the similar property search, the similarity property search is performed by the property similarity search unit 500. It is to provide the request information.

Thereafter, the attribute similarity search means 500 performs a similar attribute search when obtaining the similar attribute search request information provided from the attribute search determining unit 200, and provides the search result information to the attribute similarity search result output unit. The attribute similarity search result output unit 600 outputs the search result information of the similar attribute provided from the attribute similarity search unit 500.

For example, if a keyword such as “love actual” is entered as a search word, a similar property search is performed through the property similarity search means 500, and the search result is provided to the property similarity search result output unit 600 and displayed on the screen. Will print.

As shown in FIG. 5, similar property search results such as 'if only, romantic holiday, notting hill, walk to member', etc. will be provided.

Next, the components and operation process of the attribute similarity search means 500 that provide the core structural features of the present invention according to the first embodiment will be described in detail with reference to FIGS. 6 to 8.

As shown in FIG. 6, the attribute similarity search means 500 includes a keyword attribute analysis unit 510, a multimedia content attribute assignment unit 520, a similarity matching property analysis unit 530, a similarity candidate group extraction unit 540, and similarity degree. The reference multimedia content alignment unit 550 is included.

By providing the search result information of the multimedia content having the similar property by the configuration of the attribute similarity search means 500 as described above, by providing the multimedia content with high similarity to the property information of the multimedia content to search for accuracy and Provide reliability.

In the present invention, the property refers to an inherent characteristic of the object, and the property itself is not meaningful. However, when an object is composed of related properties, one important expression can be expressed, and the property is generally meaningful data. It is recognized as the smallest logical unit of and used for database processing.

Accordingly, in the present invention, the similar property is used to search for multimedia content information having the highest similarity with a search word (question or query word).

The keyword attribute analyzer 510 analyzes linguistic attribute information included in a keyword of a multimedia content input through speech recognition or text.

For example, a linguistic meaning included in a search word such as a movie such as a love reality is analyzed, which means to analyze linguistic attribute information.

In other words, it is possible to search for similar multimedia contents only by analyzing attribute information for searching for multimedia contents having attributes similar to those of a movie called 'Love Actually'.

That is, if attribute information such as 'warmness, inspiration, and fun' is assigned to the love reality, it is possible to search for a movie having the above-mentioned attribute information 'warmness, inspiration and fun'.

The multimedia content attribute assignment unit 520 acquires and stores multimedia content from the content server 560 and allocates attribute information to the stored multimedia content.

That is, the content information is gathered to determine what attribute information the multimedia contents have. The content information is crawled by a connected content server using an external network or communication, and the attribute information is assigned through linguistic refinement.

Specifically, it acquires and stores multimedia content called love actuation, and assigns attribute information to the stored love actuary movie, for example, assigns attribute information such as 'warmness, emotion, fun', etc. It is done.

As described above, when attribute information is allocated and managed for every content to be gathered, contents similar to the attribute information of a search word may be extracted.

The similarity matchability analysis unit 530 provides the multimedia content attribute assignment unit 520 with multimedia content request information including attribute information similar to linguistic attribute information of a search word.

For example, with linguistic attribute information such as 'movie', 'love truth', and 'like', which are the linguistic attribute information of the search word, and multimedia content attribute information such as 'warm, touching, fun', Multimedia content request information including attribute information similar to “im, fun,” and the like, is generated and provided to the multimedia content attribute assignment unit 520, and the multimedia content list information is obtained from the multimedia content attribute assignment unit. Similarity matching analysis of multimedia contents included in the content list information is performed.

The similarity matching analysis described above is content to be provided to the user by using various similarity calculation formulas such as Euclidean distance formula and vector space model, which are frequently used to search for similarity in information retrieval theory. Can be selected.

In addition, based on a search keyword provided by the user, the most similar content with the keyword of the content may be searched and the contents may be sorted in the order of high similarity.

Here, the number of contents derived as a result of the similarity search may be determined by sorting an upper predetermined number, and the predetermined number may be arbitrarily set by the user according to a situation.

Here, a is a keyword inputted by a user to search for content, and there are n keywords in total up to a ₁ , a ₂ , a ₃ ... a _n , and the total n keywords are a (a ₁ , a ₂ , a ₃ ... a _n) , b is the content, and there are n keywords up to b ₁ , b ₂ , b ₃ ... b _n , and the total n keywords are b (b ₁ , When b ₂ , b ₃ ... b _n) , the Euclidean distance formula can be expressed as follows.

(Formula 1)

Here, the closer the Euclidean distance between each keyword, that is, the smaller the value derived through Equation 1, the higher the similarity may be determined.

In addition, the vector space model can be expressed as follows.

(Formula 2)

Here, the closer the value derived through Equation 2 is to 1, the higher the similarity, and the closer to 0, the lower the similarity may be determined.

Therefore, the similarity between the search keyword and the keyword generated for each content may be inspected by Equation 1 and Equation 2 to sort the contents in the order of high similarity.

In this case, the similarity candidate group extracting unit 540 extracts the multimedia contents according to the number of candidate groups sequentially from the multimedia contents having the highest similarity with reference to a preset candidate group number.

For example, if the number of four candidates is set, the multimedia content is sequentially extracted according to the number of candidates, and four candidate groups of 'if only, romantic holiday, notting hill, and work-to-member' are extracted.

In this case, the similarity-based multimedia content sorting unit 550 sorts the multimedia contents extracted according to the number of candidate groups according to the similarity, and provides the sorted multimedia contents to the attribute similarity search result output unit 600.

For example, when the similarity with IF ONLY is 1.215, the similarity with the romantic holiday is 0.75, the similarity with the Notting Hill is 1.787, and the similarity with the walk-to-remember is 0.454, respectively, the Euclidean distance formula is The smaller the similarity value is, the higher the similarity is. Therefore, when the content is rearranged in the order of high similarity, the information is sorted in order of work-to-member, romantic holiday, if only, and notting hill, and the corresponding information is returned to the attribute similarity search result output unit 600. Will be provided to the screen.

Next, the configuration means and operation process of the keyword attribute analyzer 510 will be described in detail with reference to FIG. 7.

The search term attribute analysis unit 510 includes a natural language processing module 511, a machine learning model module 512, a search term attribute assignment module 513, a knowledge information DB 514, a search term attribute value conversion module 515, and an attribute model. Module 516, search word attribute value information DB (517).

The natural language processing module 511 provides the machine learning model module 512 to provide information on requesting interpretation of linguistic attributes included in a search word of multimedia content input through speech recognition or text, and a search word interpreted from the machine learning model module. The linguistic attribute information included in the search word attribute assignment module 513 is provided.

In this case, when the machine learning model module 512 obtains request information for interpretation of linguistic attributes included in the search word from the natural language processing module, the linguistic language interpreted by the natural language processing module is interpreted. Function to provide attribute information.

For example, if you request an interpretation of the linguistic attributes included in the search term 'tell me a movie like love actual', you will interpret the linguistic attributes of the search term, using a general machine learning model technique. It analyzes the semantic information of, and extracts the intention of the question and various semantic information based on the model generated through machine learning. For example, the linguistic attribute information such as 'Love Actually, Movie,' It is provided to the allocation module (513).

The knowledge information DB 514 stores attribute type information refined into a type of attribute that can be matched with attribute information of multimedia content.

For example, it is set as attribute information such as 'warm, touching, fun, romance' as attribute information of a movie called love act, and is set as 'movie' as an attribute type that can be matched and stored.

The type of attribute may be used to find information, a website, a news / region / shopping, a specific field of content, or a multimedia content.

In this case, the search word attribute assignment module 513 obtains the linguistic attribute information included in the search word provided by the natural language processing module, extracts the attribute type information from the knowledge information DB based on the obtained linguistic attribute information, and then searches the attribute for the search term. And assign the attribute information on the assigned keyword to the keyword attribute value conversion module 515.

For example, based on the linguistic attribute information of 'Love Actually, Like, Movie', the attribute type information 'movie' is extracted from the knowledge information DB, and the attribute information of the search term 'warmness, emotion, fun, romance' 'And the like are provided to the keyword attribute value conversion module 515.

In this case, the search word attribute value conversion module 515 provides the probability model calculation request information to the attribute model module 516 when obtaining the attribute information for the search word provided from the search word attribute assignment module 513.

Subsequently, the attribute model module 516 calculates a probability value through language modeling when obtaining the probability value calculation request information from the keyword attribute value conversion module 515, and converts the calculated probability value to the keyword attribute value conversion module 515. Will be provided.

The language modeling refers to an algorithm for finding regularity about a grammar, phrase, word, etc. in a natural language and increasing the accuracy of an object to be searched using the regularity.

In this case, a commonly used method is a statistical modeling method for calculating a probability value, which is a method of expressing a language rule as a probability in a large corpus and restricting the search area through the probability value.

In addition, there is an advantage that the search space is sharply reduced as well as accuracy in speech recognition, and since the probability model of the sentence is based on the probability distribution of all possible sentences, it is necessary to learn the probability model from the training data.

In addition, N-Gram, which is a statistical language model in most language modeling applications, is known as the most successful language model, and the present invention preferably uses N-Gram.

In addition, the keyword attribute value conversion module 515 obtains the probability value calculated from the attribute model module 516, converts the probability value into an attribute value for the keyword, and provides the result to the keyword attribute value information DB 517.

For example, such as 'warm-8, touching-9, fun-7, romance-10', the attribute information is converted into attribute values for each attribute information and stored in the query attribute value information DB 517.

In addition, the attribute information for the search word is also stored.

As described above, when an attribute value for a search term is assigned, the similarity matching property analysis unit 530 has similarity with the attribute values for various search terms and contents provided by the multimedia content attribute assignment unit 520 described below. Will be analyzed.

That is, the similarity matching analysis unit 530 performs similarity matching analysis using the attribute information on the search word stored in the search word attribute value information DB 517 and the multimedia content property information allocated by the multimedia content attribute assigning unit 520. will be.

For example, if the attribute information for a search term is assigned to 'inspiring romance movie', 'if only, romantic holiday, notting hill, walk-to-remember' etc., which has multimedia content attribute information assigned to 'inspiring romance movie' The similarity matching analysis unit 530 obtains the multimedia content list information and performs the similarity matching analysis.

Next, the configuration means and operation process of the multimedia content attribute assignment unit 520 will be described in detail with reference to FIG. 8.

The multimedia content attribute assignment unit 520 includes a content linkage module 521, a content crawling module 522, a content storage DB 523, a content attribute assignment model module 524, a content attribute information interpretation module 525, and a content. And an information retrieval module 526.

The amount of information of multimedia contents changes with the passage of time, and accordingly, the attributes of a specific object change from time to time, and various multimedia contents that are changed in real time are searched by reflecting multimedia contents variably through the multimedia content attribute assignment unit as described above. The effect can be reflected in.

Specifically, the content interlocking module 521 interoperates with the content server 560 to provide the multimedia content information to the content crawling module 522, and the content crawling module 522 is provided from the content interlocking module 521. Collecting a plurality of multimedia content information provided and stored in the content storage DB to extend the operation range of the attribute information.

That is, the information delivered from the content server becomes a resource of the content property model through the content interworking module.

At this time, the multimedia content is collected through the content crawling module 522 to expand the operation range of the attribute information.

For example, it is possible to collect various multimedia contents including 'warmness, emotion, fun, romance', etc., which are attribute information of a movie.

In this case, the content property assignment model module 524 obtains each multimedia content stored in the content storage DB 523 and allocates property information to each multimedia content.

Accordingly, the content storage DB 523 stores multimedia content information provided from the content crawling module 522 and attribute information allocated to each multimedia content.

For example, it plays a role of assigning attribute information to each multimedia content, for example, assigning attribute information of 'calm and touching' to A music.

The content attribute information analysis module 525 interprets the attribute information of each multimedia content assigned by the content attribute assignment model module 524 and provides the same to the content information search module.

For example, when a content information search module requests a 'movie' that provides 'warmness, inspiration, fun, romance' corresponding to a search word, the corresponding content is interpreted, and each of the analyzed multimedia contents is analyzed. The attribute information is provided to the content information search module 526.

Accordingly, the content information retrieval module 526 is to provide the similarity matching property analysis unit 530 with attribute information of each multimedia content analyzed by the content property information analysis module 525.

Subsequently, when the multimedia content request information including attribute information similar to the linguistic attribute information of the search word is obtained from the similarity matching property analysis unit 530, the multimedia content list including the similar attribute information from the content storage DB 523. The information is requested to the content attribute information analysis module 525, the multimedia content list information including similar attribute information is obtained from the content storage DB 523, and provided to the similarity matching property analysis unit 530.

For example, referring to the linguistic attribute information of 'Love Actually, Same, Movie', the multimedia contents list information such as 'If Only, Romantic Holiday, Notting Hill, Work to Remember' including similar attribute information is stored in the content storage DB. It is extracted from 523.

Next, a method for retrieving multimedia contents through the present inventors attribute information analysis will be described in detail with reference to FIGS. 9 to 10.

9 is a flowchart illustrating a multimedia content searching method through attribute information analysis according to a first embodiment of the present invention.

As shown in FIG. 9, the multimedia content search method through attribute information analysis includes: a search start step (S100), an attribute search execution determination step (S200), a text keyword search step (S300), and a text keyword result output step (S400). ), Attribute similarity search step (S500), and attribute similarity search result output step (S600).

Specifically, the search start step (S100) is to obtain the search request request information of the multimedia content input by voice recognition or text through the search start unit 100 to provide the search execution request information to the attribute search determination unit 200. Done.

When inputting text, the search information is provided by extracting text information and providing search request information. However, when inputted by voice recognition, the search start unit includes a natural language processing module for voice recognition, and processes the voice processed by the natural language processing module. The command target value of the user is extracted from the recognition result text.

Subsequently, in the attribute search determination step (S200), when the attribute search execution determination unit 200 obtains the search execution request information from the search start unit 100, whether to perform a text keyword search or perform a similar attribute search If it is determined whether or not to perform, and as a result of the determination, the text keyword search unit 300 provides the text keyword search request information when performing the text keyword search, and when the similar attribute search is performed, the attribute similarity search unit ( 500), similar property search request information is provided.

That is, when the text keyword search is performed, the text keyword search request information is provided to the text keyword search unit 300.

Therefore, the text keyword search step (S300) performs a text keyword search when the text keyword search unit 300 obtains the text keyword search request information provided from the attribution search execution determination unit 200, and retrieves the search result information. It is provided to the text keyword result output unit.

Subsequently, in the text keyword result output step S400, the text keyword result output unit 400 outputs search result information of the text keyword provided from the text keyword search unit 300.

For example, a text keyword search is performed by referring to a text keyword called 'love actual', and search result information including 'love actual' is provided to the text keyword result output unit.

On the other hand, when the attribute search decision unit 200 performs a similar attribute search as a result of the determination, it provides the similarity attribute search request information to the attribute similarity search unit 500, in which the attribute similarity search step (S500) When the similarity similarity search means 500 obtains the similar property search request information provided from the attribution search execution decision unit 200, the similar property search is performed and the search result information is provided to the property similarity search result output unit. .

Thereafter, in the attribute similarity search result output step (S600), the attribute similarity search result output unit 600 outputs search result information of similar attributes provided from the attribute similarity search unit 500.

For example, if a keyword such as “love actual” is entered as a search word, a similar property search is performed through the property similarity search means 500, and the search result is provided to the property similarity search result output unit 600 and displayed on the screen. Will print.

10 is a flowchart illustrating an attribute similarity retrieval step of a multimedia content retrieval method through attribute information analysis according to a first embodiment of the present invention.

As shown in Figure 10, the attribute similarity search step (S500), the keyword attribute analysis step (S510), multimedia content attribute assignment step (S520), similarity matching property analysis step (S530), similarity candidate group extraction step (540), Similarity-based multimedia content sorting step (S550) is included.

In detail, in the search word attribute analysis step S510, the search word attribute analyzer 510 analyzes linguistic attribute information included in a search word of multimedia content input by voice recognition or text.

For example, it is possible to search for similar multimedia contents only by analyzing attribute information for searching for multimedia contents having attributes similar to those of a movie called 'Love Actually'.

That is, if attribute information such as 'warmness, inspiration, and fun' is assigned to the love reality, it is possible to search for a movie having the above-mentioned attribute information 'warmness, inspiration and fun'.

Subsequently, in the multimedia content attribute assignment step S520, the multimedia content attribute assignment unit 520 acquires and stores the multimedia content from the content server 560, and assigns attribute information to the stored multimedia content.

That is, the content information is gathered to determine what attribute information the multimedia contents have. The content information is crawled by a connected content server using an external network or communication, and the attribute information is assigned through linguistic refinement.

Specifically, it acquires and stores multimedia content called love actuation, and assigns attribute information to the stored love actuary movie, for example, assigns attribute information such as 'warmness, emotion, fun', etc. It is done.

As described above, when attribute information is allocated and managed for every content to be gathered, contents similar to the attribute information of a search word may be extracted.

Then, in the similarity matching property analysis step (S530), the similarity matching property analysis unit 530 provides the multimedia content property assignment unit 520 with the multimedia content request information including the similar property information in the linguistic property information of the search word. The multimedia content list information is obtained from the content property allocator 520, and similarity matching analysis of multimedia contents included in the obtained multimedia content list information is performed.

For example, with linguistic attribute information such as 'movie', 'love truth', and 'like', which are the linguistic attribute information of the search word, and multimedia content attribute information such as 'warm, touching, fun', Multimedia content request information including attribute information similar to “im, fun,” and the like, is generated and provided to the multimedia content attribute assignment unit 520, and the multimedia content list information is obtained from the multimedia content attribute assignment unit. Similarity matching analysis of multimedia contents included in the content list information is performed.

Thereafter, in the similarity candidate group extracting step 540, the similarity candidate group extracting unit 540 extracts the multimedia contents according to the candidate group numbers sequentially from the multimedia contents having the highest similarity with reference to a preset candidate group number.

For example, if the number of four candidates is set, the multimedia content is sequentially extracted according to the number of candidates, and four candidate groups of 'if only, romantic holiday, notting hill, and work-to-member' are extracted.

Thereafter, in the similarity-based multimedia content sorting step (S550), the similarity-based multimedia content sorter 550 sorts the multimedia contents extracted according to the candidate group number according to the similarity, and arranges the sorted multimedia contents in the attribute similarity search result output unit ( 600).

For example, when the similarity with IF ONLY is 1.215, the similarity with the romantic holiday is 0.75, the similarity with the Notting Hill is 1.787, and the similarity with the walk-to-remember is 0.454, respectively, the Euclidean distance formula is The smaller the similarity value is, the higher the similarity is. Therefore, when the content is rearranged in the order of high similarity, the information is sorted in order of work-to-member, romantic holiday, if only, and notting hill, and the corresponding information is returned to the attribute similarity search result output unit 600. Will be provided to the screen.

According to the present invention described above, the multimedia content is obtained when a text keyword search is performed by determining whether to perform a text keyword search or a similar property search by acquiring a search word of the multimedia content input through speech recognition or text. Outputs the search result information of and outputs the search result information of the multimedia contents having the similar property when performing the similar property search. The multimedia content search result most similar to the search word (question) that the user wants to search through is effective.

In particular, by providing a similarity matching analysis unit, when performing a similar attribute search, similarity matching analysis is performed using the attribute information of the search term stored in the search term attribute information DB and the multimedia content attribute information assigned by the multimedia content attribute assignment unit. , By providing search result information of multimedia contents having attributes similar to the intention of the search term (question), it provides multimedia contents that match the attributes (atmosphere, emotion, etc.) desired by the user, thereby increasing the reliability of the search. Will be effective.

In addition, although the preferred embodiment of the present invention has been shown and described above, the present invention is not limited to the above-described specific embodiment, the technical field to which the invention belongs without departing from the spirit of the invention claimed in the claims. Of course, various modifications can be made by those skilled in the art, and these modifications should not be individually understood from the technical spirit or the prospect of the present invention.

Determining whether to perform a text keyword search or a similar property search by acquiring a search word of multimedia content input through speech recognition or text through an apparatus and method for searching multimedia contents through analyzing attribute information according to the present invention. Outputting the search result information of the multimedia content when performing a text keyword search, and outputting the search result information of the multimedia content having a similar property when performing a similar property search, thereby generating multimedia content using a general search keyword method. It is also highly applicable to the industry through providing a multimedia content search result that is most similar to a search word (question) that a user wants to search through the effect of providing a search result and similar property search.

Claims (7)

1. In the multimedia content retrieval apparatus through attribute information analysis,

   A search start unit 100 for acquiring a search word of multimedia content input by voice recognition or text and providing search execution request information to the attribute search execution determining unit 200;

   When obtaining the search execution request information from the search start unit 100, it is determined whether to perform a text keyword search or a similar attribute search, and as a result of the determination, the text keyword search is performed when the text keyword search is performed. The attribute search decision unit 200 which provides the text keyword search request information to the unit 300, and provides the similar property search request information to the attribute similarity search unit 500 when performing the similar attribute search as a result of the determination; ,

   A text keyword search unit 300 which performs a text keyword search when obtaining the text keyword search request information provided from the attribute search performing determination unit, and provides the search result information to the text keyword result output unit;

   A text keyword result output unit 400 for outputting search result information of the text keyword provided from the text keyword search unit;

   An attribute similarity search means 500 which performs a similar attribute search when obtaining similar attribute search request information provided from the attribute search execution determination unit and provides the search result information to the attribute similarity search result output unit 500;

   And an attribute similarity search result output unit (600) for outputting search result information of similar attributes provided from the attribute similarity search unit (500).
2. The method of claim 1,

   The attribute similarity search means 500,

   A search word attribute analyzer 510 for analyzing linguistic attribute information included in a search word of multimedia content input through speech recognition or text;

   A multimedia content attribute allocator 520 for acquiring and storing multimedia contents from the content server 560 and allocating attribute information to the stored multimedia contents;

   It provides multimedia content request information including attribute information similar to linguistic attribute information of a search word to the multimedia content attribute assigning unit 520, obtains multimedia content list information from the multimedia content attribute assigning unit 520, and obtains the multimedia content list information. A similarity matching analysis unit 530 for performing a similarity matching analysis of multimedia contents included in the multimedia contents list information;

   A similarity candidate group extracting unit 540 for sequentially extracting multimedia contents according to candidate group numbers from multimedia contents having the highest similarity with reference to a preset candidate group number;

   A similarity reference multimedia content sorting unit 550 for sorting the multimedia contents extracted according to the number of candidate groups according to similarity and providing the sorted multimedia contents to the property similarity search result output unit 600. Multimedia content retrieval apparatus through information analysis.
3. The method of claim 2,

   The search word attribute analysis unit 510,

   The machine learning model module 512 provides information on requesting interpretation of linguistic attributes included in a search word of multimedia content input through speech recognition or text, and provides linguistic attribute information included in a search word interpreted from the machine learning model module. A natural language processing module 511 for providing the query attribute assignment module 513;

   Machine learning model module for providing linguistic attribute information interpreted as natural language processing module by interpreting linguistic attributes included in search term when obtaining information on interpretation of linguistic attributes included in search term from natural language processing module. 512);

   A knowledge information DB 514 that stores attribute type information refined into attribute types that can be matched with attribute information of multimedia content;

   Obtains the linguistic attribute information included in the search word provided by the natural language processing module, extracts the attribute type information from the knowledge information DB based on the obtained linguistic attribute information, allocates the attribute for the search term, and the attribute for the assigned search term. A search word attribute assignment module 513 for providing information to the search term attribute value conversion module 515;

   When obtaining the attribute information for the search term provided from the search term attribute assignment module 513, the probability model calculation request information is provided to the attribute model module 516, and the probability value calculated from the attribute model module 516 is obtained to provide the search term. A search word attribute value conversion module 515 for converting the value into an attribute value to provide the search term attribute value information DB 517;

   An attribute model module 516 for calculating a probability value through language modeling when obtaining the probability value calculation request information from the keyword attribute value conversion module 515 and providing the calculated probability value to the keyword attribute value conversion module 515;

   And a search term attribute value information database (517) including the attribute value for the search term provided by the search term attribute value conversion module (515).
4. The method of claim 2,

   The multimedia content attribute assignment unit 520,

   A content interlocking module 521 for providing multimedia content information to the content crawling module 522 in association with the content server 560;

   A content crawling module 522 for collecting a plurality of multimedia content information provided from the content interworking module 521 and storing the multimedia content information in a content storage DB to expand the operation range of the attribute information;

   A content storage DB 523 for storing multimedia content information provided from the content crawling module 522 and attribute information allocated to each multimedia content;

   A content attribute assignment model module 524 for allocating attribute information for each multimedia content stored in the content storage DB 523;

   A content property information analysis module 525 for analyzing the property information of each multimedia content assigned by the content property assignment model module 524 and providing the same to the content information search module;

   The attribute information of each multimedia content analyzed by the content attribute information analysis module 525 is provided to the similarity matching property analysis unit 530, and similar property information is similar to the linguistic property information of the search word from the similarity matching property analysis unit 530. A content information retrieval module 526 for providing multimedia content list information including similar attribute information from the content storage DB 523 to the similarity matching property analysis unit 530 when acquiring the included multimedia content request information; Multimedia content retrieval apparatus through the analysis of the attribute information configured to include.
5. The method of claim 2,

   The similarity matchability analysis unit 530,

   Multimedia through attribute information analysis, characterized in that similarity matching analysis is performed with attribute information on the search term stored in the keyword attribute value information database 517 and multimedia content attribute information allocated by the multimedia content attribute assignment unit 520. Content search device.
6. In the multimedia content retrieval method through attribute information analysis,

   A search start step (S100) of providing a search execution request information to the attribute search performing determination unit 200 by obtaining a search word of the multimedia content inputted by voice recognition or text by the search start unit 100;

   When the attribute search execution unit 200 obtains the search execution request information from the search start unit 100, it is determined whether to perform a text keyword search or a similar attribute search. When performing a search, the text keyword search request information is provided to the text keyword search unit 300, and as a result of the determination, when the similar property search is performed, the similar property search request information is provided to the attribute similarity search unit 500. Attribute search determination step (S200),

   When the text keyword search unit 300 obtains the text keyword search request information provided from the attribution search execution determination unit 200, the text keyword search unit performs a text keyword search and provides the search result information to the text keyword result output unit. Step S300,

   A text keyword result output step (S400) for the text keyword result output unit 400 to output search result information of the text keyword provided from the text keyword search unit 300;

   When the property similarity search means 500 obtains the similar property search request information provided from the property search execution decision unit 200, the property similarity search is performed and the search result information is provided to the property similarity search result output unit. Search step (S500),

   Searching for multimedia content through attribute information analysis including attribute similarity search result output step (S600) for the attribute similarity search result output unit 600 to output search result information of similar attributes provided from the attribute similarity search unit 500. Way.
7. The method of claim 6,

   The attribute similarity search step (S500),

   A keyword attribute analysis step (S510) for the keyword attribute analyzer 510 to analyze linguistic attribute information included in a keyword of a multimedia content input through speech recognition or text;

   A multimedia content attribute assignment step (S520) of the multimedia content attribute assignment unit 520 acquiring and storing multimedia content from the content server 560 and allocating attribute information to the stored multimedia content;

   The similarity matching property analysis unit 530 provides the multimedia content property assignment unit 520 with multimedia content request information including property information similar to the linguistic property information of the search word, and the multimedia content from the multimedia content property assignment unit 520. A similarity matching analysis step (S530) of acquiring list information and performing similarity matching analysis of multimedia contents included in the obtained multimedia contents list information;

   A similarity candidate group extracting step 540 for extracting, by the similarity candidate group extracting unit 540, multimedia contents having the highest similarity sequentially from the multimedia contents having the highest similarity, according to the candidate group number;

   Similarity-based multimedia content sorting unit 550 sorts the multimedia contents extracted according to the number of candidate groups according to similarity, and provides similarity-based multimedia content sorting step to provide the sorted multimedia contents to the attribute similarity search result output unit 600. (S550); multimedia content search method through the analysis of the attribute information comprising a.

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