WO2021107446A1 - Apparatus and method for providing knowledge graph-based marketing analysis chatbot service - Google Patents

Abstract

A service providing apparatus according to an embodiment of the present invention comprises: a collection unit for collecting user information of a user terminal that receives marketing information and knowledge base information that uses a marketing-specialized knowledge graph model and corresponds to the user information; a processing unit for processing the information collected by the collection unit into structured data and unstructured data for marketing analysis; an analysis unit for learning and analyzing the processed structured data or unstructured data; and a chatbot unit for transmitting a marketing-related analysis information request received from a user interface unit to the chatbot unit and obtaining response information obtained from the analysis unit according to the marketing-related analysis information request, so as to output the response information through the user interface unit.

Description

Knowledge graph-based marketing information analysis chatbot service provision method and device

The present invention relates to a service providing method and an apparatus therefor. More specifically, the present invention relates to a method and apparatus for providing a knowledge graph-based marketing information analysis chatbot service.

As Internet technology develops, online services are being provided in various fields such as education, shopping, entertainment, and marketing. Among them, online marketing first started as a form of unilaterally delivering information about a company or product to users through a homepage. Recently, as the use of SNS has rapidly increased, various marketing using SNS is also being attempted.

In particular, with the advent of high-performance terminal devices such as smart phones and the construction of inexpensive wireless networks such as Wi-Fi networks, the number of users in the online environment is increasing rapidly, and various information collected in such an online environment is As it is used as data, it is also used for marketing and effectiveness analysis.

However, due to the rapid increase in the amount of analysis data in recent years, the cost and time for analyzing it are also rapidly increasing. In particular, the marketing environment due to the increase in the amount of data requires production infrastructure that requires additional hiring of analysts and marketers to analyze marketing cost-effectiveness, etc., and as a result, small and medium-sized enterprises that are difficult to hire are fragmented and intuitive. It is very difficult to receive effective marketing analysis consulting at low cost as only some analysis is possible.

Accordingly, at present, decision-making marketing that relies on the intuition and sense of marketer employees is mainly used in small and medium-sized companies, and it is difficult to follow the flow despite the increase in the amount of data that can be analyzed. .

Therefore, there is a need for an efficient marketing analysis service that facilitates marketing decision making based on accurate grounds even at low cost.

The present invention has been devised to solve the above problems, and by effectively providing a marketing information analysis service based on a marketing specialized knowledge graph model based on analysis information for each marketing channel through artificial intelligence technology, marketing with low cost and high efficiency An object of the present invention is to provide a method and apparatus for providing an efficient marketing analysis service that can support decision-making.

In addition, the present invention provides a method and apparatus for providing a knowledge graph-based marketing information analysis chatbot service capable of providing a marketing information service using a chatbot function in order to effectively provide marketing decision making and analysis results as described above. There is a purpose.

A service providing apparatus according to an embodiment of the present invention for solving the above-described problems includes user information of a user terminal receiving marketing information and knowledge base information using a marketing specialized knowledge graph model corresponding to the user information. a collecting unit to collect; a processing unit for processing the information collected by the collecting unit into structured data and unstructured data for marketing analysis; an analysis unit for learning and analyzing the processed structured data or unstructured data; and a chatbot unit that transmits a request for marketing-related analysis information received from the user interface unit to the chatbot unit to the analysis unit, obtains response information obtained from the analysis unit according to the marketing-related analysis information request, and outputs it through the user interface unit .

In a service providing method according to an embodiment of the present invention for solving the above problems, user information of a user terminal receiving marketing information and knowledge base information using a marketing specialized knowledge graph model corresponding to the user information are provided. collecting; processing the information collected by the collecting unit into structured data and unstructured data for marketing analysis; learning and analyzing the processed structured data or unstructured data; and the chatbot unit transmits the marketing-related analysis information request received from the user interface unit to the chatbot unit to the analysis unit, obtains response information obtained from the analysis unit according to the marketing-related analysis information request, and outputs it through the user interface unit includes steps.

On the other hand, the method according to an embodiment of the present invention for solving the above problems may be implemented as a program for executing the method in a computer and a recording medium in which the program is recorded.

According to an embodiment of the present invention, by effectively providing a marketing information analysis service based on a marketing specialized knowledge graph model based on analysis information for each marketing channel through artificial intelligence technology, efficient marketing that can support marketing decision making at low cost and high efficiency It is possible to provide a method and an apparatus for providing an analysis service.

In addition, according to an embodiment of the present invention, in order to effectively provide marketing decision-making and analysis results, a method and apparatus for providing a knowledge graph-based marketing information analysis chatbot service capable of providing a marketing information service using a chatbot function are provided. can do.

1 is a block diagram schematically showing an entire system according to an embodiment of the present invention.

2 is a block diagram illustrating in more detail an apparatus for providing a marketing service according to an embodiment of the present invention.

3 is a flowchart illustrating an operation of an apparatus for providing a marketing service according to an embodiment of the present invention.

4 is a block diagram for explaining in more detail a knowledge graph construction module according to an embodiment of the present invention.

5 is a flowchart illustrating an operation of a knowledge graph building module according to an embodiment of the present invention.

6 is a relationship diagram for explaining a knowledge graph construction and semantic mapping process according to an embodiment of the present invention.

7 is a block diagram for explaining in more detail a chatbot service providing unit of a service providing unit according to an embodiment of the present invention, and FIG. 8 is a flowchart for explaining a service providing process according to an embodiment of the present invention.

The following is merely illustrative of the principles of the invention. Therefore, those skilled in the art will be able to devise various devices which, although not explicitly described or shown herein, embody the principles of the present invention and are included within the spirit and scope of the present invention. Moreover, it is to be understood that all conditional terms and examples listed herein are, in principle, expressly intended solely for the purpose of enabling the concept of the present invention to be understood, and not limited to such specifically enumerated embodiments and states. should be

Moreover, it is to be understood that all detailed description reciting the principles, aspects, and embodiments of the invention, as well as specific embodiments, are intended to cover structural and functional equivalents of such matters. It is also to be understood that such equivalents include not only currently known equivalents, but also equivalents developed in the future, i.e., all devices invented to perform the same function, regardless of structure.

Thus, for example, the block diagrams herein are to be understood as representing conceptual views of illustrative circuitry embodying the principles of the present invention. Similarly, all flowcharts, state transition diagrams, pseudo code, etc. may be tangibly embodied on a computer-readable medium and be understood to represent various processes performed by a computer or processor, whether or not a computer or processor is explicitly shown. should be

The functions of the various elements shown in the figures including a processor or functional blocks represented by similar concepts may be provided by the use of dedicated hardware as well as hardware having the ability to execute software in association with appropriate software. When provided by a processor, the functionality may be provided by a single dedicated processor, a single shared processor, or a plurality of separate processors, some of which may be shared.

In addition, clear use of terms presented as processor, control, or similar concepts should not be construed as exclusively referring to hardware having the ability to execute software, and without limitation, digital signal processor (DSP) hardware, ROM for storing software. It should be understood to implicitly include (ROM), RAM (RAM) and non-volatile memory. Other common hardware may also be included.

In the claims of the present specification, a component expressed as a means for performing the function described in the detailed description includes, for example, any form of software including a combination of circuit elements or firmware/microcode for performing the above function. It is intended to include all methods of performing the functions that are combined with suitable circuitry for executing the software to perform the functions. Since the present invention defined by these claims is combined with the functions provided by the various enumerated means and in a manner required by the claims, any means capable of providing the functions are equivalent to those contemplated from the present specification. should be understood as

The above-described objects, features, and advantages will become more apparent through the following detailed description in relation to the accompanying drawings, and accordingly, those of ordinary skill in the art to which the present invention pertains can easily implement the technical idea of the present invention. There will be. In addition, in the description of the present invention, when it is determined that a detailed description of a known technology related to the present invention may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted.

Hereinafter, a preferred embodiment according to the present invention will be described in detail with reference to the accompanying drawings.

1 is a conceptual diagram schematically illustrating an entire system according to an embodiment of the present invention.

The entire system according to an embodiment of the present invention includes a marketing information analysis service providing apparatus 100, a marketing platform 200 and one or more user terminals 300 connected through one or more mutually distinct channels, and a marketing information analysis service The providing apparatus 100 may be connected to the machine learning module 400 or include the machine learning module 400 .

The marketing information analysis service providing apparatus 100 may be connected to each platform 200 and the user terminal 300 through a wired/wireless network to provide a marketing information analysis service, and to analyze marketing information based on learning and artificial intelligence For this purpose, it may be connected to the machine learning module 400 or include the machine learning module 400, and devices or terminals connected to each network may perform mutual communication through a preset network channel.

Here, each network is a local area network (LAN), a wide area network (WAN), a value added network (VAN), a personal area network (PAN), a mobile communication network ( It can be implemented in all types of wired/wireless networks such as mobile radiocommunication network) or satellite communication networks.

The user terminal 300 may include various server devices, network devices, or terminal devices that access the marketing information analysis service providing apparatus 100 for the purpose of receiving a marketing analysis service for marketing decision making.

In particular, in general, the user terminals 300 may be connected to the marketing information analysis service providing apparatus 100 through an individual security network, and the marketing information analysis service providing apparatus 100 may be connected to each user terminal 300 through each security network. ) may collect channel-based marketing document data based on the company information provided from them, and provide analysis result information corresponding to the collected document data to the user terminal 300 through the security network. Here, the security network may be an encryption network, and the service-registered user terminal 300 stores in advance the decryption key information according to the company authentication, and stores the marketing analysis result information received from the marketing information analysis service providing apparatus 100, Decryption according to the decryption key information can be obtained and output.

According to this embodiment of the present invention, the user terminals 300 may have completed the basic information registration process corresponding to the marketing information analysis service providing apparatus 100 .

For example, the user terminal 300 may be a terminal that is provided with a marketing information analysis service as a member of each company. For example, it may be a terminal of a company that directly makes marketing decisions, a terminal of a company that provides marketing services in partnership with a plurality of companies, or a terminal of a network service company that mediates data between a plurality of networks.

In addition, the marketing information analysis service providing apparatus 100 receives company information from each user terminal 300 , collects marketing document data based on a marketing network channel classified in advance based on the received company information, and the document data By learning the unstructured data according to the processing of the machine learning module 400 through the machine learning module 400, and using the knowledge graph information and ontology information built in advance, the learning information and the structured data collected and analyzed in advance, a marketing-specialized knowledge graph model create

Accordingly, the marketing information analysis service providing apparatus 100 may process the marketing market trend and demand prediction analysis using the marketing specialized knowledge graph model, and transmit marketing analysis information according to the processed result information to the user terminal 300 . can provide

Here, the marketing-specialized knowledge graph may be constructed by semantic mapping processing of pre-established knowledge graph model information according to a natural language analysis result of company information and marketing document collection information, and the marketing information analysis service providing apparatus 100 ) can collect, store and manage dictionary (DICTIONARY) information required for natural language processing and text analysis for such semantic mapping processing and ontology information for constructing a classification system in advance.

More specifically, the marketing information analysis service providing apparatus 100 collects and sets a dictionary and a classification system specialized for marketing in advance, and natural language analysis-based learning of marketing document information collected for each marketing channel in response to corporate information According to the processing, semantic mapping may be performed on the pre-established knowledge graph. Accordingly, the meaning-mapped marketing-specialized knowledge graph is specialized for marketing and includes the latest information and rich synonym information, and can include rich context (CONTEXT) and relationship (ASSOCIATION) information.

Such a marketing-specialized knowledge graph can include relationship information between keywords, can be used for various solutions such as marketing trend analysis and future predictive analysis, and can be used to individually create a subdivided dictionary and classification system for each marketing field. can

The marketing-specialized knowledge graph according to an embodiment of the present invention is a graph-based data model including relationship information between knowledge keywords by setting keyword information, which is a marketing entity, as a node, and representing the relationship between each node as an edge. A relational data model may be exemplified, but the marketing information analysis service providing apparatus 100 according to an embodiment of the present invention is based on the recently proposed SEMANTIC WEB technology to overcome the complexity and performance limitations of the relational data model. Based on this, it is possible to create higher efficiencies, expand the knowledge expression method, and solve the problems of scalability of data models and interoperability between systems.

In particular, the dictionary and classification system for text analysis are manually created by experts in a specific field, and as described above, there is a problem of cost increase due to the increase in the amount of data. There is a problem that this falls, and the technology itself, such as a typical web ontology language (OWL, Ontology Web Language), has problems with low model complexity and reusability.

Accordingly, the marketing information analysis service providing apparatus 100 according to an embodiment of the present invention provides learning information learned through machine learning from unstructured data analysis information and knowledge extracted from structured data in order to construct a marketing-specialized knowledge graph. Based on the graph transformation rule information, the pre-established knowledge graph information obtained from the open knowledge graph, and the ontology rule information obtained from the pre-managed relational database, the diversified marketing knowledge data is efficiently semantically mapped and processed to enable automation while Its accuracy and performance can be improved.

In particular, the marketing information analysis service providing apparatus 100 according to an embodiment of the present invention may provide keyword classification and system information based on a marketing specialized knowledge graph through the semantic mapping processing of the diversified marketing knowledge data, It facilitates the reflection of recent issue keywords or new words for marketing purposes, and it is possible to quickly build and process information on compatibility between languages for marketing purposes (eg, foreign language data corresponding to Korean transliteration, etc.).

Meanwhile, the platform 200 may be a marketing target network platform, and may be connected to the marketing information analysis service providing apparatus 100 through each access channel. Each channel may be, for example, site address information corresponding to a specific platform, and the marketing information analysis service providing apparatus 100 collects marketing document data for each platform channel determined in response to site address information, and collects the results can be stored and analyzed.

In addition, the machine learning module 400 used in the analysis may process parallel analysis of structured and unstructured data, and hybrid-type document classification processing for this may be performed in advance. Hybrid document classification processing is marketing document data using a machine learning-based primary document classification process and secondary classification information using an ontology dictionary and a linguistic rule from classification information obtained from the primary document classification process. It may include a secondary classification process for classifying As such, the classification information according to the primary and secondary classification may be used as re-learning training information of the machine learning module 400 .

And, according to the classification and analysis processing, the marketing information analysis service providing apparatus 100 according to an embodiment of the present invention may provide an analysis information service for effective marketing to the user terminal 300 . For example, the marketing information analysis service providing apparatus 100 may provide a keyword dictionary construction service for market trend analysis, a digital influence quantification service for each keyword, a trend prediction information providing service according to a prediction model, etc. to the user terminal 300 . can

In addition, in order to provide such a service, the marketing information analysis service providing apparatus 100 analyzes text or voice-based request data received from the user terminal 300, and provides a marketing analysis information providing service using an artificial intelligence chatbot function. You may.

2 is a block diagram illustrating in more detail an apparatus for providing a marketing service according to an embodiment of the present invention.

Referring to FIG. 2 , referring to FIG. 2 according to an embodiment of the present invention, the apparatus 100 for providing a marketing information analysis service according to an embodiment of the present invention includes a control unit 110 , a communication unit 120 , and a user management unit 130 . ), a channel-based information collection unit 140 , an analysis data processing unit 150 , a dashboard configuration unit 160 , a service providing unit 170 , and a storage unit 190 .

The control unit 110 generally controls the execution of the operation and function of each component including the marketing document data information collection, analysis data processing, dashboard configuration, and marketing information analysis service provision of the marketing information analysis service providing device 100 . do. For example, the controller 110 may be implemented as a processor for controlling all or a part of a function of providing an analysis result of information collected from the platforms 200 to the user terminals 300 or a program for executing the same. can

In addition, the communication unit 120 is a network between the marketing information analysis service providing apparatus 100 and a wireless communication system including a mobile communication network or Internet network or between the service providing apparatus 100 and the platform 200 or the user terminal 300 is located. It may include one or more communication modules that enable wired/wireless communication between them. For example, the communication unit 120 may include a modem that encodes and modulates a transmitted signal and demodulates and decodes a received signal, or an RF front end that processes an RF signal.

Meanwhile, the user manager 130 performs user registration and account management for one or more user terminals 300 using the service providing apparatus 100 .

The user management unit 130 receives authentication information including at least one of account identification information and terminal identification information of a person in charge of a logged-in company or a marketing service provider from the user terminal 300, and uses the authentication information to store user information. Registration can be processed. Accordingly, the user management unit 130 may register and manage information on the platform 200 to provide or analyze a marketing service and information on the user terminal 300 corresponding thereto for each marketing channel.

In addition, the channel-based information collection unit 140 collects marketing document data through data channels connected from the platform 200 corresponding to the user terminals 300 managed by the user management unit 130, respectively, and for each channel. The collected marketing document data is output to the analysis data processing unit 150 .

Here, the marketing document data may form basic analysis information processed by the analysis data processing unit 150 according to an embodiment of the present invention. The marketing document data may include, for example, web page document data collected for each channel from the platform 200 , keyword data collected corresponding to a preset format, or site source code information.

For such channel-specific collection, the channel-based information collection unit 140 stores a keyword crawler that collects and stores keywords classified by industry/subject/brand in response to each platform 200, a user request collection process, and A collection process manager that allocates a collection process for each channel, a collector for each channel that accesses the platform 200, performs collection by channel and stores the collection result in the storage unit 190, and a problem that collection is stopped due to site source change It may include a collection site source management manager that prepares for and periodically compares and reports newly updated information.

Accordingly, the channel-based information collection unit 140 may access the platform 200 through a specific channel according to channel information requested from the user terminal 300 or preset in response to the user terminal 300 . In addition, the channel-based information collection unit 140 receives the marketing document data to be collected according to keyword information received from the user terminal 300 or preset corresponding to the user terminal 300 through a data channel connected to the platform 200 . It can be collected through the Star Collector.

In addition, the channel-specific collector of the channel-based information collection unit 140 may store the collected marketing document data in the collection result database of the storage unit 190 .

For example, the channel-based information collection unit 140 identifies the channel information of the platforms 200 for each industry/subject/brand corresponding to the classification information requested from the user terminal 300, and through the channel, the user terminal ( 300), a suitable collection site may be determined, and marketing document data corresponding to preset keyword information may be collected and stored from the determined site. In particular, the preset keyword information may be obtained from a marketing ontology-based knowledge graph processed by the analysis data processing unit 150 , which will be described in more detail later.

In addition, the channel-based information collection unit 140 may register and periodically monitor site information of the platform 200 on which marketing document data is collected, and when source code update information is generated, the information is sent to the user terminal 300 . It provides an alarm and can collect and store updated data.

In addition, the analysis data processing unit 150 may perform document classification processing of the marketing document data collected by the channel-based information collection unit 140 , and may generate or construct a marketing-specialized knowledge graph model using the classified document data.

According to an embodiment of the present invention, the marketing specialized knowledge graph model includes pre-built keyword-based knowledge graph information, pre-collected ontology information, machine learning learning information of the collected and classified document data, and structured data information. can be built using The marketing-specialized knowledge graph model may be modular ontology model data, and the ontology model data includes a core ontology built from key concepts, relationship information, daily keywords and emotional keyword information, and real-time machine learning-based document classification to reflect the latest keywords. It can be designed as a layered domain ontology built from the data obtained from the data, and interoperability can be secured by the semantic web standard technology. The semantic web standard technology may include, for example, a conversion processing technology into a standard protocol language corresponding to an ontology description query, and the converted ontology description query format is RDF (Resource Description Framework) format, OWL (Web Ontoyoly language) Format, Sparkle (SPARQL, Protocol and RDF Query Language) format, etc. may be exemplified.

For this processing, the analysis data processing unit 150 includes a knowledge graph construction module 151 that processes knowledge graph construction, a dictionary construction module 152 corresponding to the domain ontology, and filtering classification of structured and unstructured documents It may include a document classification module 153 for each. Accordingly, the analysis data processing unit 150 may provide various service information based on the marketing ontology by using the generated or constructed marketing specialized knowledge graph model. In particular, the knowledge graph construction module 151 may acquire machine learning-based marketing learning information, and the acquired marketing learning information may be used to build a marketing-specific knowledge graph model.

In order to provide the marketing analysis information service, the dashboard configuration unit 160 may configure a marketing analysis dashboard interface to be provided to the user terminal 300 , and the dashboard may be in the form of a GUI (GRAPHIC USER INTERFACE) such as a web interface. may be visually or aurally output through the user terminal 300 .

In addition, the dashboard configuration unit 160 may configure an artificial intelligence chatbot-based marketing interface dashboard for a user-friendly marketing information analysis service, and through this marketing interface dashboard, a request is made from the user terminal 300 . It can provide various services such as market trend analysis, demand prediction analysis, keyword influence analysis, new word keyword dictionary, and product competitiveness analysis.

Then, the service providing unit 170 receives the service request of the user terminal 300, and through the dashboard interface configured in the dashboard configuration unit 160, the marketing information analysis service result corresponding to the service request, the user terminal ( 300), and may include a service manager provided by .

On the other hand, the storage unit 190 includes one or more storage media for storing program information for the operation of the above-described control unit 110 and the operation of the above-described components, and may include one or more databases according to each purpose. have.

3 is a flowchart illustrating an operation of an apparatus for providing a marketing service according to an embodiment of the present invention.

Referring to FIG. 3 , the apparatus 100 for providing a marketing information analysis service according to an embodiment of the present invention first collects platform channel-based marketing document data according to a service request of the user terminal 300 ( S101 ).

Then, the marketing information analysis service providing apparatus 100 performs hybrid document classification processing according to primary filtering of marketing document data and secondary filtering based on machine learning (S105).

Then, the marketing information analysis service providing apparatus 100 extracts unstructured data from the marketing document data (S105), and obtains machine learning-based marketing learning information corresponding to the unstructured data (S107).

Thereafter, the marketing information analysis service providing apparatus 100 generates a specialized marketing knowledge graph model using pre-built knowledge graph information and pre-collected ontology information, and the marketing learning information and structured data (S109).

Then, the marketing information analysis service providing apparatus 100 performs marketing market trend and demand prediction analysis based on the marketing specialized knowledge graph model (S111).

Here, the marketing information analysis service providing apparatus 100 may perform an analysis corresponding to the service according to the request of the user terminal 300, and not only the market trend and demand prediction analysis, but also the construction of a neologism dictionary, keyword influence analysis, etc. This can be done further.

In addition, the marketing information analysis service providing apparatus 100 may provide marketing analysis information based on natural language processing according to the analysis result by using the dashboard interface (S113).

4 is a block diagram for explaining in more detail a knowledge graph construction module according to an embodiment of the present invention.

Referring to FIG. 4 , in order to generate marketing-specific knowledge graph model data according to an embodiment of the present invention, unstructured data classified from marketing document data collected by the channel-based information collection unit 140 and pre-collected structured data And, a large-capacity knowledge graph and ontology information obtained from the open knowledge graph may be required.

For example, the unstructured data for building a marketing-specialized knowledge graph model may be the original text of a marketing web page collected by the channel-based information collection unit 140, and the structured data may be a general-purpose file format or a structured data that can be collected through openAPI It may contain data. In addition, the open knowledge graph data may be domestic and foreign data published in RDF format, and may be obtained by receiving an RDF file or a query response targeting a SPARQL endpoint.

And, in order to build the marketing-specific knowledge graph model data from the collected or classified information, the knowledge graph construction module 151 processes step-by-step through a two-stage pipeline module as shown in FIG. 4 , thereby effectively marketing specialized knowledge Graph model building processing can be performed.

More specifically, referring to FIG. 4 , the knowledge graph building module 151, as a first pipeline module, includes an unstructured data processing unit 1511 , a structured data processing unit 1512 , an open knowledge graph management unit 1515 , and a relational database. 1517 , and may include a natural language analysis unit 1513 , a knowledge graph information conversion unit 1514 , a large-capacity knowledge graph processing unit 1516 , and an ontology information processing unit 1518 as the second pipeline module.

In addition, the data output from the second pipeline may be transmitted to the marketing specialized knowledge graph construction unit 1519 and used to generate marketing specialized knowledge graph model data or keyword analysis information.

First, the unstructured data processing unit 1511 may identify the unstructured data from the marketing document data collected in the first pipeline stage, and transmit it to the natural language analyzer 1513 . The unstructured data may include, for example, text data identified from marketing document data.

In addition, the natural language analyzer 153 may extract main keywords using natural language processing technology from the unstructured data. The natural language processing technology may be exemplified by techniques such as morpheme analysis and entity name recognition, and the natural language analysis unit 1513 may use classification information of the document classification module 153 for more accurate keyword extraction processing.

Then, the extracted keyword may be transmitted to the knowledge graph information conversion unit 1514 . The knowledge graph information conversion unit 1514 is a marketing knowledge graph information that is mapped and integrated into the knowledge graph information in a preset format by a mapping technology such as rule-based marketing keyword mapping or machine learning algorithm-based mapping. Format conversion can be processed.

In addition, the open knowledge graph management unit 1515 may collect and store pre-built open knowledge graph information using an openAPI or the like.

Then, the large-capacity knowledge graph processing unit 1516 pre-builds the large-capacity knowledge graph information prepared so that the collected open knowledge graph information can be mapped to the marketing knowledge graph information that has been format-converted from the natural language analysis information described above, The knowledge graph information may be transmitted to the marketing specialized knowledge graph model building unit 1519 .

On the other hand, the relational database 1517 may collect and store ontology information for semantic mapping between the knowledge graph information converted by the knowledge graph information conversion unit 1514 and the knowledge graph information processed by the large capacity knowledge graph processing unit 1516, Among the stored ontology information, mutually compatible ontology information may be transmitted to the marketing specialized knowledge graph construction unit 1519 .

For example, the marketing-specialized knowledge graph model building unit 1519 collects open knowledge graph information collected from an RDF file or SPARQL Endpoint as knowledge graph model information for processing a large-capacity knowledge graph, and the converted marketing knowledge graph information By building a mapping table between and the large-capacity knowledge graph information, a marketing-specific knowledge graph model can be built.

To this end, the marketing-specialized knowledge graph model building unit 1519 performs mapping processing based on the unique identifier assigned to each data item, but in the case of the same data whose identifiers do not match, the pre-collected ontology information-based relationship information and attributes After calculating the matching probability through the information, data mapping processing for preferentially mapping the high probability may be performed.

5 is a flowchart illustrating an operation of a knowledge graph construction module according to an embodiment of the present invention, and FIG. 6 is a relationship diagram illustrating a knowledge graph construction and semantic mapping process according to an embodiment of the present invention.

5 and 6 are for explaining in more detail the process of generating the marketing-specific knowledge graph model data, the knowledge graph building module 151, first as shown in FIG. 6, is a knowledge graph from OpenAPI or structured file data. Conversion rule information may be obtained (S201).

For example, the conversion rule information may be obtained from a conversion rule file described in R2RML (RDB to RDF Mapping Language), which is a W3C international standard. In addition, the transformation rule information may be converted into knowledge graph transformation rule data using transformation rules described in RML (RDF Mapping Language) from OpenAPI or formatted file data.

Thereafter, the knowledge graph construction module 151 obtains ontology transformation rule information from the relational database (S203), and transforms the natural language analysis information of the unstructured data according to the knowledge graph transformation rule information (S205).

Thereafter, the knowledge graph construction module 151 maps the transformed knowledge graph information to a pre-built large-capacity knowledge graph according to the ontology transformation rule information to build a marketing-specialized knowledge graph model (S207).

As shown in FIG. 6 , the knowledge graph construction module 151 may include a marketing-specific knowledge graph model construction unit 1519 for generating marketing-specific knowledge graph model data. The marketing specialized knowledge graph model building unit 1519 may include the semantic mapping processing unit to efficiently perform the above-described mapping processing with high accuracy. Here, the semantic mapping processing unit may further include a fuzzy algorithm processing unit and a URI identifier processing unit.

More specifically, the semantic mapping processing unit may process semantic mapping between items of data converted into a knowledge graph format (eg, RDF) and a pre-established large-capacity knowledge graph item.

To this end, the semantic mapping processing unit may include a URI identifier processing unit for processing primary mapping by comparing URI identifiers assigned to all data items.

In addition, the semantic mapping processing unit applies a semantic mapping tool between words implemented based on the Levenshtein fuzzy metric algorithm developed according to the linguistic characteristics of Korean from the primary mapping-processed data to obtain automated meaning. Mapping can be handled. The data for which the automatic mapping is completed may be subjected to sampling processing, and the processed sampling data may be used for subsequent mapping inspection and correction processing.

Meanwhile, the knowledge graph construction module 151 may acquire knowledge graph model data on which semantic mapping is completed as marketing-specific knowledge graph model data. For example, the knowledge graph construction module 151 may integrally generate a knowledge graph model by importing the mapped knowledge graph data into a triplestore type database in which the large-capacity knowledge graph data previously built is stored. have.

Accordingly, it is preferable that the final knowledge graph model be described as an RDF (Resource Description Framework) data model, which improves compatibility and analysis efficiency.

In addition, the classification system for each item of the established large-capacity knowledge graph may be a marketing-specialized system created by a domain expert in the marketing field. By collecting and combining the brand and product classification system information of major portal services, it is possible to construct and process a more efficient large-capacity knowledge graph. To this end, the open knowledge graph management unit 1515 manages the classification system for each field based on the public interest (can be calculated as a number of searches for each period of the main portal service) corresponding to each classification system keyword, and the classification system for each field may decide to keep or archive them.

According to this knowledge graph model construction, the apparatus 100 for providing marketing information analysis service according to an embodiment of the present invention, the problem of not reflecting the latest keywords pointed out as a disadvantage of the general knowledge graph, the Korean-based knowledge graph and It solves the difficulty of building dictionary data for analysis, facilitates marketing trend and keyword analysis through the establishment of a marketing-specialized knowledge graph model, and makes accurate marketing at a lower cost by facilitating the reflection of new words and Korean keyword analysis in particular. It has the advantage of being able to provide information analysis services.

7 is a block diagram for explaining in more detail a chatbot service providing unit of a service providing unit according to an embodiment of the present invention, and FIG. 8 is a flowchart for explaining a service providing process according to an embodiment of the present invention.

As described above, the apparatus 100 for providing a marketing information analysis service according to an embodiment of the present invention provides a service system capable of providing a marketing information service using a chatbot function in order to effectively provide a marketing decision and analysis result. Study 170 may be included.

More specifically, the service providing unit 170 may include a chatbot service unit 171, and the chatbot service unit 171 is based on marketing data and solution usage pattern analysis based on optimal data suitable for the level of the marketer. Marketing analytics can be presented to users in the form of automated chatbots.

In particular, the chatbot service unit 171 according to an embodiment of the present invention may be optimized for the purpose of automatically performing communication optimized for marketing analysis by analyzing various marketing document data, and using the user terminal 300 . As marketers improve their analysis capabilities, they can provide artificial intelligence chatbot services in the field of marketing that evolve together.

In addition, the chatbot service unit 171 provides an artificial intelligence marketing chatbot service that helps interpret dashboard data using the dashboard configuration unit 160 of a marketer using a marketing analysis solution and suggests future marketing activities. can do.

To implement this, the chatbot service unit 171 according to an embodiment of the present invention includes a user interface unit 1711 , a collection unit 1712 , a processing unit 1713 , an analysis unit 1715 , and a chatbot unit 1714 . include

The collection unit 1712 collects user information for driving the chatbot and transmits it to the processing unit 1713 . The collection unit 1712 processes the dashboard interface configured in the dashboard configuration unit 160 to be provided through the user interface unit 1711 in order to collect user information, and the user collected through the user interface unit 1711 . information can be collected.

To collect user information, the collection unit 1712 may include a user information registration unit, a user data collection unit, a user log collection unit, and a knowledge base unit. The user information registration unit may receive and register marketing target company information and product information from the user terminal 300 . The user data collection unit may collect user input information input through the interface unit 1711 , and the user log collection unit may collect solution utilization information of the user.

For example, the user information registration unit, in addition to the basic information of the user (eg, marketer) of the user terminal 300 input through the interface unit 1711, keywords for companies and products, SNS PR channels and online advertising media. It is possible to receive and store account information and product information sold in the e-commerce market, and furthermore, it is possible to set a target value for each online promotion and sales channel and store it as an index. To this end, the interface unit 1711 may provide a user setting information menu on the dashboard interface to facilitate the user's information registration.

The user data collection unit may collect and store user-related information identified based on the marketing data stored in the user information register online. In addition, the user data collection unit may collect text and search volume written in the platform 200 of a predetermined channel based on the keyword input by the user.

For example, the user data collection unit may collect comparative data for marketing analysis of the own channel platform 200 and the platform 200 of a competitor's channel through account information of an SNS promotion channel and an online advertising medium. Also, for example, the user data collection unit may define and collect marketing data of the company's own and competitor product groups sold in the e-commerce market according to the analysis.

In addition, the user log collection unit may store and manage detailed records in which the user uses each function of the dashboard interface provided through the interface unit 1711 from login to logout. For example, the user log collection unit may track, store and manage the user learning log according to the marketer's solution user setting, utilization log, and calculation formula utilizing the dashboard of the marketing solution.

In addition, the knowledge base unit may include pre-built marketing-specific knowledge graph model information, which may be obtained from the model information built in the aforementioned knowledge graph construction module 151 .

And, the processing unit 1713 includes a data processing unit for processing the marketing-specialized knowledge graph model information into a required form, and a user information extraction unit for grouping the user information, user data, and user log information into user information.

Here, the data processing unit may quantify the unstructured data of the collection unit and classify the structured data into a processable form.

Then, the analysis unit 1715 learns and analyzes the extracted user information and the processed data based on machine learning, and stores the analyzed learning information.

More specifically, the analysis unit 1715 receives the marketing-related information request information input from the user interface unit 1711 through the chatbot function unit, analyzes and processes it using an artificial intelligence-based model, and outputs the analysis result to function as a chatbot can be passed on to

For example, the analysis unit 1715 may analyze KPI data for each channel and channel information of a competitor stored by the user with data of its own channel, and may perform analysis processing such as marketing data analysis and strategy recommendation.

However, for this processing, cumulative learning of data is required, and as the amount of feedback information received through the user interface unit 1711 and the data amount of the processing unit increases, continuous advancement of the analysis model may be possible.

In addition, the analysis unit 1715 analyzes the data generated by the user information extraction unit, generates an alarm signal corresponding to the occurrence of a specific event pattern, and transmits it to the chatbot function unit, thereby enabling active communication with the user. .

On the other hand, the chatbot unit 1714 includes one or more chatbot function units and natural language processing units that output chatbot function result information through the dashboard interface based on user chatbot event information input through the interface unit 1711. .

More specifically, the chatbot function unit may receive and process natural language text information through the interface unit 1711 , and may output response information or alarm information corresponding thereto through the interface unit 1711 , and the natural language processing unit may include a user It can be used for analysis of text information and output conversion processing.

More specifically, the natural language processing unit generates request information in an appropriate form to be transmitted to the analysis unit 1715 by identifying the user's intention and object from the request text input to the chatbot function unit, and from the data received from the analysis unit 1715 . The answer can be inferred and delivered to the chatbot function.

For example, the natural language processing unit can pre-process and vectorize the input rawText for AI learning-based processing, and for vectorization, word2vec one-hot vetor, bag-of-word, TFIDF, Doc2vec, RNN encoder , CNN for text, and at least one algorithm of FastText may be used. In addition, the natural language processing unit may perform optimization training to understand the query intent, and an artificial intelligence algorithm such as Classifier: Logistic-Reg, SVM, RandomForest, and NN may be used.

In particular, the natural language processing unit according to an embodiment of the present invention may be designed to infer appropriate intentions and answers by giving weights to web-based marketing and a keyword dictionary input by a user.

In addition, the natural language processing unit according to an embodiment of the present invention may further enhance the learning model performance by using the communication log input/output through the chatbot function unit.

In addition, the natural language processing unit may further include a rule-based analysis model so that users can learn marketing terminology, solution usage, and data analysis through chatbot queries. can be created

Here, the response data may include marketing prediction information or marketing recommendation information based on a marketing analysis result corresponding to a user's chatbot query. Accordingly, the chatbot service unit 171 outputs a marketing prediction parameter corresponding to the user chatbot query to the user terminal 300 or marketing methods suitable for user information corresponding to the user terminal 300 that has transmitted the user chatbot query. A process of providing the recommended marketing method information obtained by indexing to the user terminal 300 may be performed.

When this process is described with reference to FIG. 8 , first, the chatbot service unit 171 collects user information from input information input through the interface unit 1711 ( S401 ).

Then, the chatbot service unit 171 performs data processing for the marketing chatbot service by using the user collected information (S403).

Thereafter, the chatbot service unit 171 performs analysis processing of the processed data using the processes described above (S405).

Thereafter, the chatbot service unit 171 may provide a chatbot service of marketing analysis information based on the analysis result and user information to the user terminal 300 through the interface unit 1711 ( S407 ).

As described above, the chatbot service may include marketing prediction information or marketing recommendation information based on a marketing analysis result corresponding to a user's chatbot query.

Accordingly, for example, the chatbot service unit 171 may output a marketing prediction value corresponding to the user's chatbot query to the user terminal 300 through the interface unit 1711 . In addition, the chatbot service unit 171 provides the recommended marketing method information obtained by calculating marketing methods suitable for user information corresponding to the user terminal 300 that has transmitted the user chatbot query from the analysis processing information of the processed data, the interface A process of providing to the user terminal 300 may be performed through the unit 1711 .

Meanwhile, the above-described method according to various embodiments of the present invention may be implemented as a program and provided to each server or device while being stored in various non-transitory computer readable media. Accordingly, the user terminal 100 may access the server or device and download the program.

The non-transitory readable medium refers to a medium that stores data semi-permanently, rather than a medium that stores data for a short moment, such as a register, cache, memory, etc., and can be read by a device. Specifically, the above-described various applications or programs may be provided by being stored in a non-transitory readable medium such as a CD, DVD, hard disk, Blu-ray disk, USB, memory card, ROM, and the like.

In addition, although preferred embodiments of the present invention have been illustrated and described above, the present invention is not limited to the specific embodiments described above, and the technical field to which the present invention belongs without departing from the gist of the present invention as claimed in the claims In addition, various modifications may be made by those of ordinary skill in the art, and these modifications should not be individually understood from the technical spirit or perspective of the present invention.

Claims (12)

1. In the service providing device,

   a collecting unit for collecting user information of a user terminal receiving marketing information and knowledge base information using a marketing specialized knowledge graph model corresponding to the user information;

   a processing unit for processing the information collected by the collecting unit into structured data and unstructured data for marketing analysis;

   an analysis unit for learning and analyzing the processed structured data or unstructured data; and

   A chatbot unit that transmits a request for marketing-related analysis information received from the user interface unit to the chatbot unit to the analysis unit, obtains response information obtained from the analysis unit in response to the marketing-related analysis information request, and outputs it through the user interface unit

   Service providing device.
2. According to claim 1,

   The chatbot unit,

   On the basis of the user chatbot event information input through the interface unit, comprising at least one chatbot function unit and a natural language processing unit for outputting chatbot function result information through the dashboard interface

   Service providing device.
3. 3. The method of claim 2,

   The natural language processing unit identifies the user's intention and entity from the marketing-related analysis information request text input to the chatbot function unit, generates request information in the form of transmission to the analysis unit, and infers an answer from the data received from the analysis unit. Delivered to the chatbot function

   Service providing device.
4. According to claim 1,

   The collection unit,

   Further comprising a user information registration unit for receiving marketing target company information and product information from the user terminal and registering as marketing data

   Service providing device.
5. 5. The method of claim 4,

   Further comprising a user data collection unit that collects and stores user-related information identified based on the marketing data stored in the user information registration unit online

   Service providing device.
6. According to claim 1,

   The knowledge base information includes marketing-specific knowledge graph model information obtained from the model information built in the knowledge graph construction module.

   Service providing device.
7. In the service providing method,

   collecting user information of a user terminal receiving marketing information and knowledge base information using a marketing specialized knowledge graph model corresponding to the user information;

   processing the information collected by the collecting unit into structured data and unstructured data for marketing analysis;

   learning and analyzing the processed structured data or unstructured data; and

   transmitting, by the chatbot unit, the marketing-related analysis information request received from the user interface unit to the chatbot unit, to the analysis unit, obtaining response information obtained from the analysis unit in response to the marketing-related analysis information request, and outputting it through the user interface unit containing

   HOW TO PROVIDE SERVICES.
8. 8. The method of claim 7,

   The chatbot unit,

   On the basis of the user chatbot event information input through the interface unit, comprising at least one chatbot function unit and a natural language processing unit for outputting chatbot function result information through the dashboard interface

   HOW TO PROVIDE SERVICES.
9. 9. The method of claim 8,

   The natural language processing unit identifies the user's intention and entity from the marketing-related analysis information request text input to the chatbot function unit, generates request information in the form of transmission to the analysis unit, and infers an answer from the data received from the analysis unit. Delivered to the chatbot function

   HOW TO PROVIDE SERVICES.
10. 8. The method of claim 7,

    The collecting step is

    Receiving marketing target company information and product information from the user terminal, further comprising the step of registering as marketing data

    HOW TO PROVIDE SERVICES.
11. 11. The method of claim 10,

    The method further comprising the step of collecting and storing user-related information identified on the basis of the marketing data stored in the user information registration unit online

    HOW TO PROVIDE SERVICES.
12. 8. The method of claim 7,

    The knowledge base information includes marketing-specific knowledge graph model information obtained from the model information built in the knowledge graph construction module.

    HOW TO PROVIDE SERVICES.

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