Patent Publication Number: US-2018039890-A1

Title: Adaptive knowledge base construction method and system

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority under 35 U.S.C. §119 to Korean Patent Application No. 10-2016-0098926, filed on Aug. 3, 2016 and Korean Patent Application No. 10-2017-0019873, filed on Feb. 14, 2017, the disclosure of which is incorporated herein by reference in its entirety. 
     TECHNICAL FIELD 
     The present invention relates to an adaptive knowledge base construction method and system, and more particularly, to an adaptive knowledge base construction method and system which convert a learned result, generated based on machine learning, into a rule and construct the rule in a knowledge base by using semantic technology. 
     BACKGROUND 
     Recently, research on machine learning and semantic technology is being actively done. The machine learning is technology that performs data-based learning according to an assigned purpose and predicts information necessary for a new environment, based on a learning result. 
     Generally, a learning method may be categorized into a tree-based analysis method and an association-based analysis method. The tree-based analysis method generates a rule by using node information about a tree constructed based on a learning result, and the association-based analysis method analyzes a pattern of learning data to generate an association rule. 
     The semantic technology denotes technology where a designer having domain knowledge generates a rule and extends, infers, and reuses knowledge by using the generated rule to construct a knowledge base. 
     In a case of generating a rule by using the machine learning and generating a result corresponding to a request by using the rule, since there is a generation period, it is unable to reuse a learning result. In the semantic technology, unless a person having domain knowledge changes a rule, a knowledge base is constructed as an extension and inference result, based on rule-based knowledge which is previously generated. In this case, since an actual environment can be dynamically changed by an ambient environment, it is required to adaptively change a rule depending on the ambient environment and construct a knowledge base in which the changed rule is reflected. 
     SUMMARY 
     Accordingly, the present invention provides an adaptive knowledge base construction method and system. In detail, the present invention provides a method and a system, which construct an adaptive knowledge base based on a dynamically changed environment by using machine learning and semantic technology without intervention of a person. 
     The object of the present invention is not limited to the aforesaid, but other objects not described herein will be clearly understood by those skilled in the art from descriptions below. 
     In one general aspect, an adaptive knowledge base construction system includes: a machine learning engine analyzing a correlation between pieces of data included in a first data set in a process of learning the first data set input thereto, based on machine learning; a rule generator generating a rule based on the machine learning by using an analysis result obtained by analyzing the correlation; and a semantic rule generator generating a semantic rule from the rule based on the machine learning by using a language expressing ontology, and reflecting the generated semantic rule in a knowledge base to extend the knowledge base. 
     In another general aspect, an adaptive knowledge base construction system includes: a memory storing a program for providing an adaptive knowledge base construction model; and a processor executing the program, wherein by executing the program, the processor generates a learning model corresponding to a first data set input thereto, outputs a correlation analysis result obtained by analyzing the first data set according to the generated learning model, generates a machine learning rule based on a correlation analysis result obtained by analyzing a correlation between a learned model and a learned algorithm, and generates a semantic rule by using the generated machine learning rule. 
     In another general aspect, an adaptive knowledge base construction method includes: performing machine learning on an input first data set; generating a machine learning-based rule, based on a learned result; converting the machine learning-based rule into a semantic rule by using a language expressing ontology; and storing the semantic rule to construct a knowledge base. 
     Other features and aspects will be apparent from the following detailed description, the drawings, and the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is an exemplary diagram for describing a configuration of a computer system where an adaptive knowledge base construction method according to an embodiment of the present invention is implemented. 
         FIG. 2  is a block diagram of an adaptive knowledge base construction system according to an embodiment of the present invention. 
         FIG. 3  is a block diagram of an adaptive knowledge base construction system according to another embodiment of the present invention. 
         FIG. 4  is an exemplary diagram for describing an adaptive knowledge base construction method based on a tree learning model according to some embodiments of the present invention. 
         FIGS. 5A and 5B  are an exemplary diagram for describing an example of generating a rule based on an Apriori algorithm 
         FIG. 6  is a block diagram of an adaptive knowledge base construction system according to another embodiment of the present invention. 
         FIG. 7  is a block diagram of an adaptive knowledge base construction system according to another embodiment of the present invention. 
         FIG. 8  is a flowchart of an adaptive knowledge base construction method according to another embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF EMBODIMENTS 
     The advantages, features and aspects of the present invention will become apparent from the following description of the embodiments with reference to the accompanying drawings, which is set forth hereinafter. The present invention may, however, be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the present invention to those skilled in the art. The terms used herein are for the purpose of describing particular embodiments only and are not intended to be limiting of example embodiments. As used herein, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. 
       FIG. 1  is an exemplary diagram for describing a configuration of a computer system  100  where an adaptive knowledge base construction method according to an embodiment of the present invention is implemented. 
     Referring to  FIG. 1 , an adaptive knowledge base construction method according to an embodiment of the present invention may be implemented in the computer system  100 , or may be stored in a recording medium. 
     The computer system  100  may include at least one processor  110 , a memory  120 , a data communication bus  130 , a storage  140 , a user input device  150 , and a user output device  160 . In addition, the computer system  100  may further include a network interface  170  connected to a network  180 . The elements  110 ,  120 ,  140 ,  150 ,  160 , and  170  may perform data communication therebetween through the data communication bus  130 . 
     The processor  110  may be a central processing unit (CPU), or may be a semiconductor device which executes a command stored in the memory  120  and/or the storage  140 . The processor  110  may perform a series of processing operations associated with an extension of a knowledge base according to an embodiment of the present invention. 
     The memory  120  and the storage  140  may each include a volatile or non-volatile storage medium. For example, the memory  120  may include a read-only memory (ROM)  123  and a random access memory (RAM)  126 . 
     When the adaptive knowledge base construction method according to an embodiment of the present invention is performed in a computer device, computer-readable commands may perform an operating method according to an embodiment of the present invention. 
     The adaptive knowledge base construction method according to an embodiment of the present invention may be implemented with a computer-readable code in a computer-readable recording medium. 
     Example of the computer-readable recording medium may include all kinds of recording mediums which store data capable of being decoded by a computer system. For example, there may be ROM, RAM, magnetic tape, magnetic disk, flash memory, optical data storage device, etc. 
     The computer-readable recording medium may be distributed to a computer system connected thereto through a computer communication network and may be stored and executed as a code which is readable through a distributed method. Hereinafter, the computer system may be referred to as an adaptive knowledge base construction system. 
       FIG. 2  is a block diagram of an adaptive knowledge base construction system  100  according to an embodiment of the present invention. 
     Referring to  FIG. 2 , the adaptive knowledge base construction system  100  according to an embodiment of the present invention may include a machine learning engine  210 , a rule generator  220 , a semantic rule engine  230 , and a knowledge base  280 . Also, the adaptive knowledge base construction system  100  may further include a rule modeler  240  of a domain expert. The elements  210 ,  220 ,  230 , and  280  may each be implemented as an internal logic or an external logic of the processor  110  illustrated in  FIG. 1 . When each of the elements  210 ,  220 ,  230 , and  280  is implemented as the external logic of the processor  110 , operations of the elements  210 ,  220 ,  230 , and  280  may be performed by the processor  110 . 
     The rule modeler  240  is for semantic filtering. In a case where the semantic filtering is not used, the rule modeler  240  may be excluded from a design of the adaptive knowledge base construction system  100 . 
     The adaptive knowledge base construction system  100  according to an embodiment of the present invention may operate both a knowledge base construction environment based on semantic modeling and a knowledge base construction environment based on a rule which is learned according to machine learning, or may individually operate each of the knowledge base construction environments. The semantic modeling and the machine learning may be functionally separated from each other, and in consideration of a whole system, may be constructed as a distributed processing system. 
     The machine learning engine  210  may learn a first data set  260  through the machine learning to generate an optimal learning model. In detail, the machine learning engine  210  may analyze a correlation between pieces of data included in the first data set  260 , based on the machine learning and may generate the optimal learning model based on a result of the analysis. 
     The rule generator  220  may generate a rule corresponding to the analysis result, namely, the correlation between the pieces of data included in the first data set  260 . The rule may be generated from an analysis result obtained by analyzing a pattern or the correlation between the pieces of data included in the first data set  260 , based on a method such as a learning algorithm based on tree included in the machine learning, an Apriori algorithm, a covariance matrix algorithm, a casual analysis, clustering affinity grouping, dimension reduction, a network analysis (or a link analysis, and/or the like. The rule generator  220  may provide the generated rule to the semantic rule engine  230 , or may provide the generated rule to the semantic rule engine  230  in the form of unstructured data. 
     In  FIG. 2 , an example where the rule generator  220  is designed outside the machine learning engine  210 , but the present embodiment is not limited thereto. In other embodiments, the rule generator  220  may be designed inside the machine learning engine  210 . 
     As described above, the adaptive knowledge base construction system  100  according to an embodiment of the present invention may be constructed as a distributed process system. In this case, the rule generator  220  may be included in a separate server depending on a designing method of the distributed processing system. 
     The semantic rule engine  230  may convert the rule and the unstructured data, transferred from the rule generator  220 , into a semantic rule by using resource description framework (RDF) or ontology Web language (OWL) expressing ontology and may store the semantic rule in the knowledge base  280 . Here, the RDF and the OWL may be standard for the semantic Web provided by World Wide Web Consortium (W3C) and may be an ontology (or a knowledge base) technology language. Unlike the RDF, the OWL may be a language which is designed in consideration of knowledge extension in the ontology (or the knowledge base) based on inference. 
     In this manner, the semantic rule engine  230  may convert the rule, generated from a learning result generated through the machine learning, into the semantic rule and by reflecting the semantic rule in the knowledge base, may construct an adaptive knowledge base based on machine learning technology and semantic technology. 
     Moreover, the semantic rule engine  230  may determine whether to store the generated semantic rule in the knowledge base  280  or not. 
     The semantic rule engine  230  may be referred to as an inference engine or an extension engine and may extend the semantic rule. For example, the semantic rule engine  230  may extend the semantic rule previously stored in the knowledge base  280 , based on a second data set  270  including a new semantic rule. 
       FIG. 3  is a block diagram of an adaptive knowledge base construction system according to another embodiment of the present invention. 
     Referring to  FIG. 3 , first, in step S 310 , the machine learning engine  210  may learn the first data set  260  to generate an optimal learning model. 
     In step S 320 , the machine learning engine  210  may analyze a correlation or a pattern between pieces of data included in the first data set  260  in a process of learning the first data set  260 . 
     In step S 330 , the rule generator  220  may generate a rule by using a result of the analysis. 
     In step S 340 , the semantic rule engine  230  may convert the generated rule into a semantic rule. The rule may be converted into the semantic rule by using the RDF or the OWL. 
     In step S 350 , a knowledge base may extend by merging the semantic rule and a pre-stored semantic rule. 
     Hereinafter, a machine learning method according to an embodiment of the present invention will be described in detail. 
       FIG. 4  is an exemplary diagram for describing an adaptive knowledge base construction method based on a tree learning model according to some embodiments of the present invention. 
     Referring to  FIG. 4 , A denotes a node representing input data, and each of B, C, C 1 , and C 2  denotes a node representing a learning result obtained through learning based on the machine learning. A tree type data structure corresponding to the input data A and C may be obtained. 
     In  FIG. 4 , the tree type data structure is illustrated as representing a simplest tree type data structure, but is not limited thereto. In other embodiments, various trees which are more complicated may be generated, and a control sentence representing a rule for each of the nodes may be generated. For example, when the A is equal to or more than a threshold value, the B representing “1” may be output. When the A is less than the threshold value and an instance value of the C is “yes”, the C 1  representing “2” may be output, and when the A is less than the threshold value and the instance value of the C is “no”, the C 2  representing “3” may be output. 
       FIG. 5A  is an exemplary diagram for describing an example of generating a rule based on an Apriori algorithm which is a type of machine learning method according to another embodiment of the present invention. 
     In order to held understand description, it is assumed that numbers illustrated in  FIG. 5A  respectively denote items listed in the following Table 1 and each of A, B, C, D, and E listed in the following Table 2 denotes one transaction, the following Table 2 show items purchased through respective transactions. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                 Item Name 
                 Item ID 
               
               
                   
                   
               
             
            
               
                   
                 soy milk 
                 1 
               
               
                   
                 lettuce 
                 2 
               
               
                   
                 diapers 
                 3 
               
               
                   
                 wine 
                 4 
               
               
                   
                 chard 
                 5 
               
               
                   
                 orange juice 
                 6 
               
               
                   
                   
               
            
           
         
       
     
     
       
         
           
               
               
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 Transaction 
                   
                   
               
               
                 ID 
                 Item List 
                 Item ID List 
               
               
                   
               
             
            
               
                 A 
                 Soy milk, lettuce 
                 1, 2 
               
               
                 B 
                 Lettuce, diapers 
                 2, 3 
               
               
                 C 
                 Soy milk, wine 
                 1, 4 
               
               
                 D 
                 Lettuce, soy milk, diapers, wine 
                 2, 1, 3, 4 
               
               
                 E 
                 Lettuce, soy milk, diapers 
                 2, 1, 3 
               
               
                   
               
            
           
         
       
     
     A semantic Web of  FIG. 5A  may be generated with reference to Table 2. The generated semantic Web may be regularized based on a semantic rule. 
     For example, through an analysis of a lowermost box, it can be seen that a total of four transaction IDs include 1, a total of four transaction IDs include 2, a total of four transaction IDs include 3, and a total of three transaction IDs include 4. These are arranged in the following Table 3. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 3 
               
               
                   
                   
               
               
                   
                 Item Set 
                 Approval Rating 
               
               
                   
                   
               
             
            
               
                   
                 {1} 
                 4 
               
               
                   
                 {2} 
                 4 
               
               
                   
                 {3} 
                 3 
               
               
                   
                 {4} 
                 2 
               
               
                   
                   
               
            
           
         
       
     
     In this case, when a minimum approval rating is set to 4, {1} and {2} may be classified into a frequent item set, and {3} and {4} may be classified into an infrequent item set. In  FIG. 5A , the frequent item set is inverted and illustrated. 
     Likewise, an approval rating may be calculated from an item set where the number of elements is two. This is shown in the following Table 4. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 4 
               
               
                   
                   
               
               
                   
                 Item Set 
                 Approval Rating 
               
               
                   
                   
               
             
            
               
                   
                 {1, 2} 
                 3 
               
               
                   
                 {1, 3} 
                 2 
               
               
                   
                 {1, 4} 
                 2 
               
               
                   
                 {2, 3} 
                 3 
               
               
                   
                 {2, 4} 
                 1 
               
               
                   
                 {3, 4} 
                 1 
               
               
                   
                   
               
            
           
         
       
     
     In this case, when a minimum approval rating is set to 3, {1,2} and {2,3} may be classified into a frequent item set, and {1,3}, {1,4}, {2,4}, and {3,4} may be classified into an infrequent item set. In  FIG. 5A , the frequent item set is inverted and illustrated. 
     Likewise, an approval rating may be calculated from an item set where the number of elements is three. This is shown in the following Table 5. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 5 
               
               
                   
                   
               
               
                   
                 Item Set 
                 Approval Rating 
               
               
                   
                   
               
             
            
               
                   
                 {1, 2, 3} 
                 2 
               
               
                   
                 {1, 2, 4} 
                 1 
               
               
                   
                 {1, 3, 4} 
                 1 
               
               
                   
                 {2, 3, 4} 
                 1 
               
               
                   
                   
               
            
           
         
       
     
     In this case, when a minimum approval rating is set to 2, {1,2,3} may be classified into a frequent item set, and {1,3}, {1,2,4}, {1,3,4}, and {2,3,4} may be classified into an infrequent item set. In  FIG. 5A , the frequent item set is inverted and illustrated. 
     Likewise, in a case where an approval rating is calculated from an item set where the number of elements is four, {1,2,3,4} has an approval rating of 1, and thus, when a minimum approval rating is 1, this corresponds to a frequency item set. On the other hand, when the minimum approval rating is 2, this corresponds to an infrequency item set. In  FIG. 5A , a case where a minimum approval rating is 1 is provided. 
     The Apriori algorithm may be a machine learning method that prunes an infrequent item and increases a calculation speed. 
       FIG. 5B  is an exemplary diagram for describing an example of generating a rule based on an Apriori algorithm according to another embodiment of the present invention. 
       FIG. 5B  is a diagram illustrating a result obtained by pruning a node less than a minimum approval rating in  FIG. 5A . 
     In all trees, the number of operations exponentially increases based on the number of items. In this case, a computer cannot satisfy a calculation speed. In a case where a node equal to or less than a minimum approval rating is pruned and an arithmetic operation is continuously performed on only a node equal to or more than the minimum approval rating, a semantic rule between nodes may be generated through a small number of operations. In  FIG. 5B , a dotted-line arrow indicates a pruned node. 
       FIG. 6  is a block diagram of an adaptive knowledge base construction system according to another embodiment of the present invention. 
     Referring to  FIG. 6 , the adaptive knowledge base construction system according to another embodiment of the present invention may include a rule generation server  610 , a semantic rule generation server  620 , and a rule modeler server  630 . 
     The rule generation server  610  may include a machine learning engine  210  and a rule generator  220 . In order to avoid repetitive descriptions, the descriptions of  FIG. 2  may be applied to the machine learning engine  210  and the rule generator  220 . 
     The rule generation server  610  may repetitively learn a first data set  260  by using the machine learning to generate a rule corresponding to a correlation between pieces of data which are dynamically changed. 
     The semantic rule generation server  620  may include a semantic rule engine  230  and a storage  280  which stores a knowledge base  280 . In order to avoid repetitive descriptions, the descriptions of  FIG. 2  may be applied to the semantic rule engine  230  and the storage  280 . 
     The semantic rule generation server  620  may generate a semantic rule from a machine learning-based rule provided from the rule generation server  610  and may store the semantic rule in the knowledge base  280  to extend the knowledge base  280 . 
     Moreover, the semantic rule generation server  620  may perform semantic inference on a second data set  270  to extend the knowledge base  280 . 
     Moreover, the semantic rule generation server  620  may perform semantic inference on the second data set  270  by using the semantic rule generated from the machine learning-based rule provided from the rule generation server  610  to extend the knowledge base  280 . This will be described below with reference to  FIGS. 7 and 8 . 
     The semantic rule engine of the semantic rule generation server  620  may correct a semantic rule by using a rule model input from a rule modeler  240  and may change a method of converting the machine learning-based rule into the semantic rule. 
     The rule modeler server  630  may include the rule modeler  240  that transmits a rule model, input by a domain expert, to the semantic rule engine  230 . 
     Although not shown, the rule modeler  240  may provide a user interface (UI) to the semantic rule engine  230  in order to enable the domain expert to input the rule model. 
     Moreover, the rule modeler  240  may provide the semantic rule engine  230  with a UI that connects a machine learning rule and a semantic rule. 
     Moreover, the rule modeler  240  may view a connection relationship between the semantic rule and the machine learning-based rule generated by the machine learning engine  210  before the connection relation is stored in the knowledge base  280 , and may provide a correctable UI to the semantic rule engine  230 . 
       FIG. 7  is a block diagram of an adaptive knowledge base construction system according to another embodiment of the present invention. 
     Referring to  FIG. 7 , the adaptive knowledge base construction system according to another embodiment of the present invention has a difference with the system described above with reference to  FIG. 6  in that a second data set  270  is input to a machine learning engine  210  instead of a semantic rule engine  230 . 
     It is possible for a semantic rule engine to be designed in a machine learning engine. However, since a machine learning process needs a long learning time, the new second data set  270  and a first data set  260  may all be input to the machine learning engine  210  in order to shorten the long learning time, and by processing the first data set  260  and the new second data set  270  through a one-time machine learning process, computer resources are efficiently used. 
     The adaptive knowledge base construction system according to an embodiment of the present invention may be constructed as a distributed processing system, and when a machine learning engine is constructed as a parallel type system in a plurality of servers and a semantic rule engine is installed in a small number of servers, a method of inputting a second data set to the machine learning engine may efficiently distribute resources. 
     The adaptive knowledge base construction system according to another embodiment of the present invention may include: a machine learning engine that generates a learning model corresponding to a first data set, analyzes a pattern or a correlation between pieces of data included in the first data set by using the generated learning model, generates a semantic inference model corresponding to a second data set, and performs inference by using the generated semantic inference model and the second data set to generate a prediction result; a rule generator that generates a machine learning rule from a learned model result obtained through analysis by the machine learning engine and generates a machine learning rule from the prediction result; and a semantic rule engine that converts the machine learning rule, transferred from the rule generator, into a semantic rule and stores the semantic rule to construct a knowledge base. 
     The adaptive knowledge base construction system may further include a rule modeler that changes, by a domain expert, a semantic rule generation method. 
     Depending on the case, a machine learning rule as well as a semantic rule may extend. However, since a machine learning-based rule has a significant characteristic, it is required to adaptively change the machine learning-based rule in an environment where an actual environment is dynamically changed, but changing of the machine learning engine is not efficient. 
     The rule modeler  240  may change a method of converting a machine learning rule into a semantic rule, instead of extending a machine learning-based rule, thereby enabling a user to select an appropriate conversion method in a dynamically changed environment. 
     A domain expert is not a person who knows a structure of the adaptive knowledge base construction system according to an embodiment of the present invention, but is a person who has sufficient knowledge about information about a semantic rule. Therefore, instead of immediately storing a generated semantic rule in a knowledge base, the domain expert may determine whether to use the generated semantic rule, and based on the determination, the rule modeler  240  may operate. 
     The rule modeler  240  may determine a method of converting a machine learning rule into a semantic rule through a separate learning process and may transfer the determined conversion method to the semantic rule engine  230 , and the semantic rule engine  230  may construct a knowledge base by using the conversion method provided from the rule modeler  240 . 
     The semantic rule engine  230  may construct the knowledge base, based on the machine learning-based rule provided from the rule generator  220 , the second data set, and the rule model provided from the rule modeler  240 . Such a process may not be immediately performed but may be performed at appropriate periods. 
     The semantic rule engine  230  may have a characteristic where a knowledge base is differently constructed based on an input order in which the first data set is input, an input order in which the second data set is input, and an input order in which the rule model is input. 
     However, when input data is actually changed with time, an analysis of the data is generally changed. For example, when a home boiler operates in an Internet of things (IoT) environment, machine learning content may be changed according to a time when the boiler operates. That is, the adaptive knowledge base construction system according to an embodiment of the present invention has a more robust characteristic in a dynamically changed environment. 
       FIG. 8  is a flowchart of an adaptive knowledge base construction method according to another embodiment of the present invention. 
     Referring to  FIG. 8 , the adaptive knowledge base construction method may include: an operation (S 810 ) of generating a semantic inference model corresponding to a second data set; an operation (S 820 ) of analyzing the semantic inference model to generate a prediction result for generation of a rule; an operation (S 830 ) of generating a machine learning rule from the prediction result; an operation (S 840 ) of regularly converting the machine learning rule into a semantic rule; and an operation (S 850 ) of storing the semantic rule to extend a knowledge base. 
     Unlike the embodiment of  FIG. 3 , in the present embodiment, a knowledge base which is analyzed by using a first data set as input data may be based on a construction environment. 
     There is a difference in that in such an environment, a new second data set is input to a machine learning engine instead of a semantic rule engine, and semantic inference is performed. 
     When the new second data set is directly input to the semantic rule engine, the semantic inference may be performed the constructed knowledge base, but since the machine learning engine needs a high-specification server generally, a computing power of the machine learning engine is better. 
     In a case of inputting the second data set to the machine learning engine, inference may be quickly performed on the second data set by using the high-specification server. 
     Data which is previously used may be again input to the semantic rule engine and may be checked by using the constructed knowledge base, and the knowledge base may extend. 
     As described above, according to the embodiments of the present invention, a knowledge base may be constructed by combining machine learning technology and semantic technology, and thus, intervention of a person is prevented, thereby obtaining an optimal analysis and high efficiency. 
     Moreover, the present invention may be applied to IoT technology, an analysis associated with big data technology, and the intelligent service industry field related to a context-aware service, and may be used as a platform in the analysis technology field using machine learning. 
     A number of exemplary embodiments have been described above. Nevertheless, it will be understood that various modifications may be made. For example, suitable results may be achieved if the described techniques are performed in a different order and/or if components in a described system, architecture, device, or circuit are combined in a different manner and/or replaced or supplemented by other components or their equivalents. Accordingly, other implementations are within the scope of the following claims.