Patent Publication Number: US-2019188532-A1

Title: Method, apparatus, and program for information presentation

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a continuation application of International Application PCT/JP2016/076023 filed on Sep. 5, 2016 and designated the U.S., the entire contents of which are incorporated herein by reference. 
    
    
     FIELD 
     The embodiments discussed herein are related to an information presentation method, an information presentation apparatus, and an information presentation program. 
     BACKGROUND 
     When a local government studies administrative measures for solving its own problems, the local government sometimes refers to the measures taken by the other local governments. Alternatively, when a company studies a business plan for solving its own problems, the company sometimes refers to the businesses of the other companies, or the like. In these cases, techniques are provided for supporting such a situation. 
     For example, a proposal has been made of a method of selecting at least one of decision making entities having characteristics similar to those of a target decision making entity to which information is presented from a plurality of decision making entities. In this method, calculation is performed for each decision making entity on an evaluation index, which is an evaluation index for each of a plurality of evaluation items for each of a plurality of decision making entities and based on the characteristic information representing the characteristics of each decision making entity. The decision making entity to be referenced is selected based on the calculated evaluation index for each decision making entity. For example, a related-art technique is disclosed in International Publication Pamphlet No. WO 2015/064713. 
     As described above, when a decision making entity, such as a local government, a company, or the like refers to information on the activities of the other decision making entities, it is desirable that the decision making entity be capable of understanding its features and referring to the information on the decision making entity having solved the problem among the other decision making entities having similar features. The more distinctive the feature in the other local government, the higher the priority for referring to the information of the local government. 
     However, in the related art, as a method for evaluating the similarity between decision making entities, a method of utilizing a distance function using a Euclidean distance, or the like of the evaluation values for each evaluation item. That is to say, in the related art, a selection is made of the other decision making entity having similarity in the numerical evaluation value of an evaluation item, and the selection does not have to be made of a decision making entity having a more distinctive feature. 
     According to an embodiment of the present disclosure, it is desirable to present more distinctive feature information of the other decision making entities when a decision making entity makes a decision. 
     SUMMARY 
     According to an aspect of the embodiments, an information presentation method causes a computer to perform, among a plurality of evaluation items for each of a plurality of decision making entities, extracting one or a plurality of evaluation items that indicate features of a target decision making entity being a target specified by a user for information presentation, for each of the other decision making entities other than the target decision making entity included in the plurality of decision making entities, calculating a feature similarity degree corresponding to the extracted evaluation items and becoming higher with an increase in a degree indicating a feature of the target decision making entity and each of the other decision making entities based on the evaluation value of the evaluation item; and outputting information on the other decision making entity having the calculated feature similarity degree equal to or higher than a predetermined value to an output device. 
     The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims. 
     It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of the invention. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a functional block diagram of an information presentation apparatus according to first to fourth embodiments; 
         FIG. 2  is a diagram illustrating an example of an evaluation information database in the first to the third embodiments; 
         FIG. 3  is an explanatory diagram of a feature similarity degree in the first embodiment; 
         FIG. 4  is a block diagram illustrating the schematic configuration of a computer that functions as the information presentation apparatus according to the first to the fourth embodiments; 
         FIG. 5  is a flowchart illustrating an example of information presentation processing; 
         FIG. 6  is an explanatory diagram of a feature similarity degree in the second embodiment; 
         FIG. 7  is an explanatory diagram of a feature similarity degree in the third embodiment; 
         FIG. 8  is a diagram illustrating an example of an evaluation information database in the fourth embodiment; 
         FIG. 9  is an explanatory diagram of a feature similarity degree in the fourth embodiment; 
         FIG. 10  is a diagram illustrating an example of calculation of a feature similarity degree when a decision making entity is a company; and 
         FIG. 11  is an explanatory diagram of a comparison between calculation methods of the feature similarity degree. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     In the following, a detailed description will be given of an example of a disclosed technique according to an embodiment with reference to the drawings. In each of the following embodiments, a description will be given of an example of the case in which information is presented to a local government, which is an example of a decision making entity that performs decision making, such as policy making, strategic decision making, or the like. 
     First Embodiment 
     As illustrated in  FIG. 1 , an information presentation apparatus  10  according to a first embodiment includes a reception unit  11 , an extraction unit  12 , a calculation unit  13 , and a presentation unit  14 . A predetermined storage area in the information presentation apparatus  10  includes an evaluation information database (DB)  21 . 
     The evaluation information DB  21  stores an evaluation value for each of a plurality of evaluation items for each local government.  FIG. 2  illustrates an example of the evaluation information DB  21 .  FIG. 2  illustrates the ratio of the number of employees by industry for each local government using a deviation value among all the local governments. Each industry is the evaluation item, and a deviation value is an example of the evaluation value. Hereinafter the deviation value of the ratio of the number of employees in a local government (hereinafter, also referred to as a “local government j”) having the local government name j for the i-th (the i-th column of the deviation value field) industry (hereinafter also referred to as “industry i”) stored in the evaluation information DB  21  is denoted by S i, j . For example, the deviation value of the ratio of the number of employees in the agriculture, forestry, and fisheries industry (i=1) in A City (j=A) is denoted by S 1, A . 
     The evaluation value is not limited to a deviation value, and it is possible to use an index that is capable of indicating the degree of a feature of a local government regarding each evaluation item as an evaluation value. For example, it may be possible to use a difference value produced by subtracting the average value of all the local governments from the actual value of each local government regarding an evaluation item, or the quotient when the difference value is divided by the standard deviation, or the like. The evaluation value does not have to be a continuous value, and, for example, may be a discrete value, such as a five-grade evaluation or a 10-grade evaluation. By using such an evaluation value, it is possible to determine an evaluation item having a high evaluation value or a low evaluation value to be a distinctive feature evaluation item for the local government. 
     The evaluation information DB  21  is not limited to be stored in the internal storage device of the information presentation apparatus  10  and may be stored in an external storage device or a storage device of the other apparatus connected via a network. 
     The reception unit  11  receives the identification information of a target local government input by a user, who is a clerk, or the like of a local government (hereinafter referred to as a “target local government”) to which information is presented via an input device (illustration is omitted), such as a keyboard, a mouse, or the like connected to the information presentation apparatus  10 . In the present embodiment, a description will be given of the case of using a local government name as the identification information of a local government. The reception unit  11  notifies the extraction unit  12  of the received target local government name. 
     The extraction unit  12  extracts one or a plurality of evaluation items that indicate the feature of the target local government indicted by the target local government name notified from the reception unit  11  and the evaluation values of the evaluation items from the evaluation information DB  21 . Specifically, the extraction unit  12  extracts a predetermined number of evaluation items or the specified number of evaluation items by the user via the input device in descending order of the evaluation value. The extraction unit  12  may extract an evaluation item having an evaluation value equal to or higher than a predetermined value. 
     For example, it is assumed that when the evaluation information DB  21  illustrated in  FIG. 2  is stored, and a target local government name “A City” has been notified from the reception unit  11 . In this case, the extraction unit  12  reads all the deviation values S i, A  of A City from the evaluation information DB  21  and extracts an industry (evaluation item) and the deviation value (evaluation value) of the industry in descending order of the deviation values S i, A  up to the L-th industry. Hereinafter the industries extracted by the extraction unit  12  are referred to as feature industries t ak  (k=1, . . . , L) for the target local government a. The deviation value of a local government j for a feature industry t ak  is expressed as S tak, j (k=1, . . . , L) and in particular, the deviation value of the target local government a for a feature industry t ak  is expressed as S tak, a  (k=1, . . . , L). For example, assuming that L=3, t A1, A =agriculture, forestry, and fisheries industry, t A2 =construction industry, and t A3 =manufacturing industry are extracted with the deviation values S tA1, A =80, S tA2, A =75, and S tA3, A =70 respectively as the feature industries of A City. 
     When an industry having a deviation value equal to or higher than a predetermined is extracted as a feature industry t ak , assuming that the predetermined deviation value is, for example, “70”, the feature industry t ak  and the deviation value S tak, a  are extracted in the same manner as described above. In this case, L=3 is determined afterward. Assuming that the predetermined deviation value is, for example, “75”, t A1 =agriculture, forestry, and fisheries industry, and t A2 =construction industry are extracted with the deviation values S tA1, A =80 and S tA2, A =75 respectively as the feature industries of A City, and this case results in L=2. 
     The extraction unit  12  transfers the target local government name, the feature industry t ak  of the target local government a, the deviation value S tak, a  and the value of L to the calculation unit  13 . 
     For each of the local governments, stored in the evaluation information DB  21 , other than the target local government, the calculation unit  13  calculates a feature similarity degree that indicates to what degree their features are similar to those of the target local government. It is assumed that a feature similarity degree is a value that becomes higher as the deviation value S tak, a  of the target local government a and the deviation value S tak, j  of the other local government j indicate higher degree of the feature of the target local government a and the other local government j for each feature industry t ak  of the target local government a extracted by the extraction unit  12 . 
     Specifically, the calculation unit  13  reads all the deviation values S i, j  of the ratio of employees by industry of the other local government j corresponding to the local government name other than the target local government name transferred from the extraction unit  12 . The calculation unit  13  identifies the deviation value S tak, j  from the deviation value S i, j  of the read other local government j. The calculation unit  13  calculates the feature similarity degree T j  using the identified S tak, j , the feature industry t ak  transferred from the extraction unit  12 , the deviation value S tak, a , and the value of L for each other local government j, for example, by the following expression (1). 
     
       
         
           
             
               
                 
                   
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     The feature similarity degree T j  in the expression (1) uses the product of the deviation value S tak, a  of the target local government a for the feature industry t ak  and the deviation value S tak, j  of the other local government j for the feature industry t ak  of the target local government a. That is to say, for the feature industry t ak  having a high deviation value S tak, a  of the target local government a, if the other local government j has a high deviation value S tak, j , the feature similarity degree T j  becomes high. 
     For example, it is assumed that the evaluation information DB  21  in  FIG. 2  is used, and the target local government a is A City. In this case, as illustrated in  FIG. 3 , for A City and a local government j other than A City, a feature similarity degree T j  is calculated using the deviation values (shaded portion in  FIG. 3 ) of the three industries, namely the agriculture, forestry, and fisheries (k=1), the construction industry (k=2), and the manufacturing industry (k=3). For example, the feature similarity degree T B  between A City and B City becomes T B =((80×75+75×70+70×65)/3) 1/2 =72.6. In the same manner, the feature similarity degrees T j  with C City, D City, and E City are calculated as T C =79.9, T D =64.2, and T E =73.1 respectively as illustrated in  FIG. 3 . 
     The calculation unit  13  transfers the calculated feature similarity degree T j  for each local government j to the presentation unit  14 . 
     The presentation unit  14  presents names of the other local governments having the feature similarity degree T j  transferred from the calculation unit  13 , which is equal to or higher than a predetermined value as reference local government names. 
     Specifically, the presentation unit  14  determines the M-th feature similarity degree T j  from the top in descending order of the feature similarity degree T j  as a predetermined value, and extracts the names of the local governments having a feature similarity degree T j  equal to or higher than a predetermined value, that is to say, up to the M-th local government names in descending order of the feature similarity degree T j . The presentation unit  14  presents the extracted local government name as a reference local government name with the information on the number (the M-th) of the feature similarity degree T j  in size to the user by displaying the information on a display unit connected to the information presentation apparatus  10  or printing the information on a printer. 
     For example, when the feature similarity degrees T j  between A City, which is the target local government a, and the local governments j other than A City are calculated as illustrated in  FIG. 3 , and if M=3, the presentation unit  14  presents information on the first C City, the second E City, and the third B City as reference local government names. 
     The predetermined value may be a predetermined value or a value specified by the user via the input device. In the case of  FIG. 3 , for example, if it is set that local governments having a feature similarity degree equal to or higher than T j =70 are extracted as reference local governments, the same reference local government names as described above are presented. If it is set that local governments having a feature similarity degree equal to or higher than T j =73 are extracted as reference local governments, only the first C City and the second E City are output. 
     As a reference, the rightmost column in  FIG. 3  illustrates a result produced by calculating the similarity degree between A City and the other local government j using the Euclidean distance of the vector values having deviation values as elements for each feature industry. In the reference example, if reference local governments are extracted in ascending order of the Euclidean distance, the result becomes B City, C City, D City, and E City. 
     However, it is understood that C City has higher deviation values in the agriculture, forestry, and fisheries industry, the construction industry, and the manufacturing industry than those of A City, and these industries are more distinctive feature than those of B City. Accordingly, there is a high possibility that A City will quickly find good measures if C City is referenced prior to B City. E City has high deviation values in the agriculture, forestry, and fisheries industry and the construction industry. The two industries are said to be more distinctive features compared with D City. Accordingly, there is a high possibility that good measures will be found if E City is referenced prior to D City. 
     In this manner, by only using the similarity degree in numerical value of the evaluation value (deviation value) of evaluation items, it is not possible to preferentially present a local government having more distinctive features or a local government having a more distinctive feature for a part of the evaluation items. 
     On the other hand, in the present embodiment, a feature similarity degree is used that becomes higher as the deviation values of the target local government and each of the other local governments become higher for the feature industries of the target local government. Thus, the feature similarity degrees are listed in descending order as C City, E City, B City, and D City. Accordingly, it is possible to preferentially present a local government having more distinctive features or a local government having a more distinctive feature in a part of the evaluation items. 
     It is possible to realize the information presentation apparatus  10  by, for example, a computer  40  illustrated in  FIG. 4 . The computer  40  includes a central processing unit (CPU)  41 , a memory  42  as a temporary storage area, and a nonvolatile storage unit  43 . The computer  40  includes an input and output device  44 , a read/write(R/W) unit  45  that controls reading data from and writing data to a storage medium  49 , and a communication interface (I/F)  46  connected to a network, such as the Internet, or the like. The CPU  41 , the memory  42 , the storage unit  43 , the input and output device  44 , the R/W unit  45 , and the communication I/F  46  are mutually connected via a bus  47 . The input and output device  44  includes an output device, such as a display, a printer, or the like, and is used for displaying local government names to be presented by the present embodiment, or the like. The input and output device  44  includes an input device, such as a keyboard, a touch panel, a mouse, or the like and is used for specifying a target local government by a user, or for specifying a calculation method of a feature similarity degree. 
     It is possible to realize the storage unit  43  by a hard disk Drive (HDD), a solid state drive (SSD), a flash memory, or the like. As a storage medium, the storage unit  43  stores an information presentation program  50  that causes the computer  40  to function as the information presentation apparatus  10 . The information presentation program  50  includes a reception process  51 , an extraction process  52 , a calculation process  53 , and a presentation process  54 . The storage unit  43  includes an evaluation information storage area  61  that stores information constituting the evaluation information DB  21 . 
     The CPU  41  reads the information presentation program  50  from the storage unit  43 , loads the information presentation program  50  into the memory  42 , and executes the processes of the information presentation program  50  in sequence. The CPU  41  executes the reception process  51  so as to operate as the reception unit  11  illustrates in  FIG. 1 . The CPU  41  executes the extraction process  52  so as to operate as the extraction unit  12  illustrated in  FIG. 1 . The CPU  41  executes the calculation process  53  so as to operate as the calculation unit  13  in  FIG. 1 . The CPU  41  executes the presentation process  54  so as to operate as the presentation unit  14  illustrated in  FIG. 1 . The CPU  41  reads information from the evaluation information storage area  61  and loads the evaluation information DB  21  into the memory  42 . Thereby, the computer  40  that executes the information presentation program  50  functions as the information presentation apparatus  10 . 
     It is possible to realize the functions to be performed by the information presentation program  50  by, for example, a semiconductor integrated circuit, more specifically an application specific integrated circuit (ASIC), or the like. 
     Next, a description will be given of the operation of the information presentation apparatus  10  according to the first embodiment. When a user who is a clerk, or the like of a target local government inputs a target local government name via an input device, such as a keyboard, a mouse, or the like connected to the information presentation apparatus  10 , the information presentation apparatus  10  performs information presentation processing illustrated in  FIG. 5 . 
     In step S 11 , the reception unit  11  receives an input target local government name and notifies the extraction unit  12  of the received target local government name. 
     In step S 12 , the extraction unit  12  reads all the deviation values S i, a , of each industry i for the target local government a from the evaluation information DB  21 . 
     Next, in step S 13 , the extraction unit  12  determines whether or not a value L that indicates the number of feature industries to be extracted is specified. If L is specified, the processing proceeds to step S 14 , whereas if L is not specified, the processing proceeds to step S 15 . 
     In step S 14 , the extraction unit  12  extracts up to the L-th industries having the deviation value S i, A  read in the above-described step S 12 , in descending order, as feature industries t ak  with the deviation values S tak, a , and the processing proceeds to step S 17 . 
     On the other hand, in step S 15 , the extraction unit  12  extracts industries having a deviation value equal to or higher than a predetermined deviation value as feature industries t ak  with its deviation values S tak, a . Next, in step S 16 , the extraction unit  12  sets the number of extracted feature industries t ak  in L, and the processing proceeds to step S 17 . 
     In step S 17 , the extraction unit  12  transfers the target local government name, the feature industries t ak  of the target local government a, the deviation values S tak, a , and the value of L to the calculation unit  13 . The calculation unit  13  reads all the deviation values S i, j  of the ratio of the number of employees by industry of the other local governments j corresponding to the local government names other than the target local government name transferred from the extraction unit  12 . 
     Next, in step S 18 , the calculation unit  13  identifies a deviation value S tal, j  from the read deviation values S i, j  of the other local governments j. The calculation unit  13  calculates a feature similarity degree T j  for each of the local governments j using the identified deviation value S tal, j , and the feature industry t ak , the deviation value S tak, a , and the value of L that have been transferred from the extraction unit  12 , for example, by the expression (1). The calculation unit  13  transfers the calculated feature similarity degree T j  for each local government j to the presentation unit  14 . 
     Next, in step S 19 , the presentation unit  14  determines whether or not a value M that indicates the number of the reference local government names to be presented is specified. If M is specified, the processing proceeds to step S 20 , whereas if M is not specified, the processing proceeds to step S 21 . 
     In step S 20 , the presentation unit  14  extracts up to the M-th local government names having a feature similarity degree T j  in descending order and presents the extracted local government names as reference local government names with the information on the number (the M-th) in size of the feature similarity degree T j  to the user, and the information presentation processing is terminated. 
     On the other hand, in step S 21 , the presentation unit  14  extracts the names of the local governments having a feature similarity degree T j  equal to or higher than a predetermined value and presents the extracted local government names as reference local government names to the user with the information on the number (the M-th) in size of the feature similarity degree T j  to the user, and the information presentation processing is terminated. 
     As described above, with the information presentation apparatus  10  according to the first embodiment, a feature similarity degree is calculated using the evaluation value (deviation value) of the evaluation item (feature industry) having a distinctive feature of a target decision making entity (target local government). A feature similarity degree is a value that becomes higher as each of the evaluation values of the target decision making entity and the other decision making entities becomes higher for an evaluation item having a distinctive feature. Accordingly, it is possible to present the information (name of the decision making entity, or the like) on the other decision making entity having a more distinctive feature using the feature similarity degree. 
     In the first embodiment, a feature similarity degree is calculated using the product of the evaluation value of the target decision making entity and the evaluation value of the other local government for the evaluation item having a distinctive feature in the target decision making entity. Thereby, it is possible to calculate a feature similarity degree that becomes higher as each of the evaluation values of the target decision making entity and the other decision making entities become higher for an evaluation item having a distinctive feature by simple calculation. 
     Second Embodiment 
     Next, a description will be given of a second embodiment. A same sign is given to a same part of the information presentation apparatus according to the second embodiment as that of the information presentation apparatus  10  according to the first embodiment, and the detailed description will be omitted. 
     As illustrated in  FIG. 1 , an information presentation apparatus  210  according to the second embodiment includes the reception unit  11 , the extraction unit  12 , a calculation unit  213 , and the presentation unit  14 . A predetermined storage area in the information presentation apparatus  210  stores the evaluation information DB  21 . 
     The calculation unit  213  calculates the feature similarity degree T j  with the target local government a for each of the other local governments j other than the target local government a, for example, by the expression (1) in the same manner as the calculation unit  13  according to the first embodiment. At this time, the calculation unit  213  uses a deviation value S i, j  of the industry that is also a distinctive feature for the other local government j as S tak, j  in the expression (1) out of the deviation values S i, j  read from the evaluation information DB  21 . 
     For example, in the case where the evaluation information DB  21  illustrated in  FIG. 2  is used, the target local government a is A City, and L=3, as illustrated in  FIG. 6 , the agriculture, forestry, and fisheries industry, the construction industry, and the manufacturing industry are extracted as feature industries t ak  (k=1, 2, 3). Further, in  FIG. 6 , only the deviation values of three kinds of industries are illustrated in descending order of the deviation value S i, j  for each local government j, and the other deviation values expressed as blank. The calculation unit  213  uses only the deviation values t ak, j  (described deviation value in the shaded portion in  FIG. 6 ) of the industries included in the top three kinds of industries of each local government for calculating the feature similarity degree T j . 
     Specifically, B City, C City, and D City have in common the agriculture, forestry, and fisheries industry, the construction industry, and the manufacturing industry as the top three industries in the deviation value, and these industries are all included in the feature industries t ak  of A City. Accordingly, the feature similarity degrees T B , T C , and T D  are calculated using the deviation values of the three industries. On the other hand, for E City, the feature similarity degree T E  is calculated using the deviation values of the agriculture, forestry, and fisheries industry, and the construction industry, which are included in the feature industry t Ak  of A City out of the top three industries, namely, the agriculture, forestry, and fisheries industry, the construction industry, and the service industry. That is to say, the deviation value of the service industry, which is not included in the feature industry t Ak  of A City, is not used for calculating the feature similarity degree T E  between A City and E City, and T E =((80×90+75×90)/3) 1/2 =68.2. 
     Accordingly, when M=3, unlike the first embodiment, the presentation unit  14  presents the first C City, the second B City, and the third E City to the user as reference local government names. 
     It is possible to realize the information presentation apparatus  210 , for example, by the computer  40  illustrated in  FIG. 4 . The storage unit  43  of the computer  40  stores an information presentation program  250  that causes the computer  40  to function as the information presentation apparatus  210 . The information presentation program  250  includes the reception process  51 , the extraction process  52 , the calculation process  253 , and the presentation process  54 . The storage unit  43  includes the evaluation information storage area  61 . 
     The CPU  41  reads the information presentation program  250  from the storage unit  43 , loads the information presentation program  250  into the memory  42 , and executes the processes of the information presentation program  250  in sequence. The CPU  41  executes the calculation process  253  so as to operate as the calculation unit  213  illustrated in  FIG. 1 . The other processes are the same as those of the information presentation program  50  according to the first embodiment. Thereby, the computer  40  that executes the information presentation program  250  functions as the information presentation apparatus  210 . 
     It is possible to realize the functions performed by the information presentation program  250  by, for example, a semiconductor integrated circuit, more specifically an ASIC, or the like. 
     The operation of the information presentation apparatus  210  according to the second embodiment is different from that of the first embodiment only in the feature similarity degree T j  calculated in step S 18  of the information presentation processing illustrated in  FIG. 5 , and thus the description thereof will be omitted. 
     As described above, with the information presentation apparatus  210  according to the second embodiment, the feature similarity degree is calculated using only the evaluation value of a feature evaluation item that is in common between the target decision making entity and the other decision making entities. Accordingly, it is possible to reduce the influence of an evaluation item that is not a distinctive feature for one of the decision making entities, and thus it is possible to calculate the feature similarity degree using an emphasized evaluation item having a more distinctive feature. 
     In the first embodiment and the second embodiment, the descriptions have been given of the case where an evaluation item (industry) having a high evaluation value (deviation value) is extracted as a distinctive feature evaluation item (feature industry). However, an evaluation item having a low evaluation value may be extracted as a distinctive feature evaluation item. 
     Third Embodiment 
     Next, a description will be given of a third embodiment. A same sign is given to a same part of the information presentation apparatus according to the third embodiment as that of the information presentation apparatus  10  according to the first embodiment, and the detailed description will be omitted. 
     As illustrated in  FIG. 1 , an information presentation apparatus  310  according to the third embodiment includes the reception unit  11 , an extraction unit  312 , a calculation unit  313 , and the presentation unit  14 . A predetermined storage area in the information presentation apparatus  310  includes the evaluation information DB  21 . 
     In the same manner as the extraction unit  12  according to the first embodiment, the extraction unit  312  extracts one or a plurality of evaluation items indicating the features of the target local government indicated by the target local government name notified from the reception unit  11  and the evaluation values of the evaluation items from the evaluation information DB  21 . At this time, the extraction unit  312  also extracts an evaluation item having a low evaluation value as an evaluation item having a feature of the target local government. 
     Specifically, the extraction unit  312  calculates a reverse evaluation value that is produced by subtracting the evaluation value from twice the average value or the median value for the evaluation item having an evaluation value equal to or higher than the average value or the median value of the allowable evaluation values. The extraction unit  312  uses the reverse evaluation value as the evaluation value of the evaluation item. As in the present embodiment, when the deviation value is used as an evaluation value, for an industry having a deviation value S i, a  that is lower than 50, which is the average value, the reverse deviation value S′ i, a  that is a value produced by subtracting a true deviation value from 100 is used. For example, when the deviation value S i, a =20, the reverse deviation value S′ i, a =80. 
     Moreover, the extraction unit  312  extracts a predetermined number of or the number specified by the user via the input device of evaluation items in descending order of the evaluation value. The extraction unit  312  may extract evaluation items having an evaluation value equal to or higher than the predetermined value. 
     For example, when the target local government a is A City and the evaluation information DB  21  illustrated in  FIG. 2  is used, as illustrated in  FIG. 7 , the extraction unit  312  calculates the reverse deviation value S′ 4, A =70 by subtracting the actual deviation value S 4, A =30 from 100 for the retail industry having the deviation value S i, A  less than or equal to 50. In the same manner, the extraction unit  312  calculates the reverse deviation value S′ 5, A =80 for the service industry having the deviation value S 5, A =20. When L=3, the extraction unit  312  extracts top three industries in descending order of the deviation value S i, A  or the reverse deviation value S′ i, A  as the feature industries t Ak  of A City, which is the target local government a. Here, t A1 =the agriculture, forestry, and fisheries industry, t A2 =the service industry, and t A3 =the construction industry are extracted. 
     For the feature industry t ak  extracted by using the reverse deviation value S i, a  by the extraction unit  312 , the calculation unit  313  calculates the feature similarity degree T j  (shaded portion in  FIG. 7 ) using the reverse deviation value S′ tak, j  for the deviation value S tak, j  of the other local government j. For example, in the example in  FIG. 7 , the feature similarity degree T B  between A City and B City is calculated: T B =((80×75+80×80+75×70)/3) 1/2 =76.7. 
     It is possible to realize the information presentation apparatus  310 , for example, by the computer  40  illustrated in  FIG. 4 . The storage unit  43  of the computer  40  stores an information presentation program  350  that causes the computer  40  to function as the information presentation apparatus  310 . The information presentation program  350  includes the reception process  51 , the extraction process  352 , a calculation process  353 , and the presentation process  54 . The storage unit  43  includes the evaluation information storage area  61 . 
     The CPU  41  reads the information presentation program  350  from the storage unit  43 , loads the information presentation program  350  into the memory  42 , and executes the processes of the information presentation program  350  in sequence. The CPU  41  executes the calculation process  352  so as to operate as the calculation unit  312  illustrated in  FIG. 1 . The CPU  41  executes the calculation process  353  so as to operate as the calculation unit  313  illustrated in  FIG. 1 . The other processes are the same as those of the information presentation program  50  according to the first embodiment. Thereby, the computer  40  that executes the information presentation program  350  functions as the information presentation apparatus  310 . 
     It is possible to realize the functions performed by the information presentation program  350  by, for example, a semiconductor integrated circuit, more specifically an ASIC, or the like. 
     The operation of the information presentation apparatus  310  according to the third embodiment is different from that of the first embodiment only in the point that a reverse deviation value is used in the extraction of a feature industry in step S 14  or S 15  of the information presentation processing illustrated in  FIG. 5 , and the calculation of the feature similarity degree in step S 18 , and thus the description thereof will be omitted. 
     As described above, with the information presentation apparatus  310  according to the third embodiment, an evaluation item having a low evaluation value is handled as an evaluation item having a distinctive feature for the target decision making entity. Accordingly, it is possible to present information on the other decision making entities to be referenced in consideration of all the evaluation items. For example, in the example of the embodiment described above, it is possible to present an effective reference local government name deviation value for studying the measures that influence the entire industrial structure by using both the industries having high deviation values and the industries having low deviation values as feature industries. 
     Whether to use an evaluation item having a high evaluation value as the feature evaluation item, or to use an evaluation item having a low evaluation value as the feature evaluation item, or to use both as the feature evaluation item may be selectable by the user. In this case, the reception unit  11  ought to receive a selection by the user having been input via the input device and specify an extraction method and a calculation method for the extraction unit  312  and the calculation unit  313 . 
     After extracting a feature evaluation item using a reverse evaluation value, in the same manner as the second embodiment, the feature similarity degree may be calculated by using the evaluation value of a feature evaluation item that is common to the target decision making entity and the other decision making entity or using only a reverse evaluation value. 
     Fourth Embodiment 
     Next, a description will be given of a fourth embodiment. A same sign is given to a same part of the information presentation apparatus according to the fourth embodiment as that of the information presentation apparatus  10  according to the first embodiment, and the detailed description will be omitted. 
     As illustrated in  FIG. 1 , an information presentation apparatus  410  according to the fourth embodiment includes the reception unit  11 , the extraction unit  12 , a calculation unit  413 , and the presentation unit  14 . A predetermined storage area in the information presentation apparatus  410  stores an evaluation information DB  421 . As illustrated in  FIG. 8 , the evaluation information DB  421  includes an evaluation item other than the evaluation item used for calculation of the feature similarity degree in addition to the evaluation information DB  21  according to the first embodiment.  FIG. 8  illustrates an example of including the other evaluation item, such as the deviation value of a regional production, or the like in addition to the deviation value of the ratio of employees by industry, which is used for calculating the feature similarity degree. 
     The calculation unit  413  calculates the feature similarity degree T j , in the same manner as the calculation unit  13  according to the first embodiment, and calculates a reference degree produced by adding the evaluation value of the evaluation item other than the evaluation item used for calculation of the feature similarity degree T j  to the calculated feature similarity degree T j . 
     Specifically, the calculation unit  413  identifies an evaluation item (hereinafter referred to as a “problem item”) indicating a problem of the target local government from an evaluation item other than the evaluation item used for calculating the feature similarity degree T j  in accordance with a predetermined rule. For example, the calculation unit  413  identifies an evaluation item having a deviation value less than or equal to a predetermined value as a problem item P out of evaluation items other than the evaluation item used for calculating the feature similarity degree T j . The calculation unit  413  calculates a reference degree V j  that becomes higher as a local government j has a higher deviation value for the identified problem item P for each local government j. The calculation unit  413  calculates the reference degree V j  using the deviation value S P, a  for the problem item P of the target local government a and the deviation value S P, j  for the problem item P of the other local government j, for example, by the following expression (2). 
         V   j =( S   P, j   −S   P, a )+ T   j    (2)
 
     For example, when the target local government a is A City using the evaluation information DB  421  illustrated in  FIG. 8 , and the problem item P is identified as “regional production”, the reference degree V B  of B City with respect to A City becomes V B =(50−30)+72.6=92.6 as illustrated in  FIG. 9 . In the same manner, for C City, D City, and E City, the reference degrees are calculated as V C =89.9, V D =54.2, and V E =103.1 respectively. In the example in  FIG. 9 , the reference degree V E  of E City having a high deviation value for the problem item “regional production” of A City, which is the target local government a, becomes high. 
     The calculation unit  413  transfers the reference degree V j  calculated for each local government j to the presentation unit  14 . 
     The presentation unit  14  presents the other local government name having a reference degree V j , transferred from the calculation unit  413 , equal to or higher than a predetermined value as a reference local government name to the user. 
     It is possible to realize the information presentation apparatus  410 , for example, by the computer  40  illustrated in  FIG. 4 . The storage unit  43  of the computer  40  stores an information presentation program  450  that causes the computer  40  to function as the information presentation apparatus  410 . The information presentation program  450  includes the reception process  51 , the extraction process  52 , a calculation process  453 , and the presentation process  54 . The storage unit  43  includes the evaluation information storage area  61  in which information constituting the evaluation information DB  421  is stored. 
     The CPU  41  reads the information presentation program  450  from the storage unit  43 , loads the information presentation program  450  into the memory  42 , and executes the processes of the information presentation program  450  in sequence. The CPU  41  executes the calculation process  453  so as to operate as the calculation unit  413  illustrated in  FIG. 1 . The other processes are the same as those of the information presentation program  50  according to the first embodiment. Thereby, the computer  40  that executes the information presentation program  450  functions as the information presentation apparatus  410 . 
     It is possible to realize the functions performed by the information presentation program  450  by, for example, a semiconductor integrated circuit, more specifically an ASIC, or the like. 
     The operation of the information presentation apparatus  410  according to the fourth embodiment is different from that of the first embodiment only in the point that a reference degree is calculated in step S 18  in the information presentation processing illustrated in  FIG. 5 , and a reference local government name is presented based on the reference degree in step S 20  or step S 21 , and thus the description thereof will be omitted. 
     As described above, with the information presentation apparatus  410  according to the fourth embodiment, a reference degree produced by adding the evaluation value of the evaluation item other than the evaluation item used for calculation of the feature similarity degree to the feature similarity degree, and information on the other decision making entity to be referenced is presented based on the reference degree. Accordingly, it is possible to preferentially present the other decision making entity to be referenced in particular among the other decision making entities having similar features. 
     In the fourth embodiment, a description has been given of the case where the calculation unit  413  identifies a problem item based on the evaluation value of an evaluation item other than the evaluation item used for calculating the feature similarity degree. However, the present disclosure is not limited to this. An evaluation item specified by the user may be identified as a problem item. An evaluation item to be used for calculating the reference degree is not limited to a problem item of the target local government. It is possible to suitably select an evaluation item in accordance with an object of decision making by the target local government. 
     The user may be allowed to select any one of the calculation methods among the calculation methods of a feature similarity degree or a reference degree in each of the embodiments described above. 
     In each of the embodiments described above, descriptions have been given of the cases where the decision making entity is a local government, and the evaluation item is the ratio of the number of employees by industry. However, the present disclosure is not limited to this. 
     For example, as illustrated in  FIG. 10 , it is possible to apply the disclosed technique to the case where a company, which is an example of a decision making entity, searches for the other company to be referenced when the company makes a decision, such as at the time of developing a business plan, or the like.  FIG. 10  illustrates an example in which the deviation value for each car model, such as a truck, a bus, or the like is used for the sales amount ratio of transport vehicles by transport vehicle manufacturing companies, and a car model is the evaluation item, and the deviation value of a sales amount ratio is the evaluation value. If it is assumed that A Company is a company to which information is presented, by extracting a car model having a high deviation value for A Company, a truck, a bus, and a wagon are extracted as feature car models of A Company. When a feature similarity degree is calculated with A Company for each company using the deviation values of the other companies for each feature car model of A Company, it is understood that the other companies having a similar feature to A Company are in the order of C Company, E Company, and B Company. 
     In each of the embodiments described above, a description has been given of the case where the feature similarity degree is calculated using the product of the evaluation value of the target decision making entity and the evaluation value of the other decision making entity for a feature evaluation item of the target decision making entity. However, the present disclosure is not limited to this. It is possible to calculate the feature similarity degree such that the feature similarity degree becomes higher as the both evaluation values become higher for the same feature evaluation item, for example, using the sum of the both evaluation values for each feature evaluation item, the total value of the products of that sum and a predetermined coefficient, or the like. 
     Referring to  FIG. 11 , a result of the comparison is illustrated for the calculation of a feature similarity degree among the case of using the product in the same manner as each of the embodiments described above, the case of using a weighted sum, and the case of using the sum. It is possible to calculate a weighted sum, for example, as illustrated by the following expression (3), by multiplying the sum of the deviation values of the target local government and the other target local government for each feature industry by a higher coefficient in descending order of the deviation value of the target local government. Here, for the sake of comparison with the other calculation methods, a feature similarity degree is produced by dividing the weighted sum by a predetermined fixed number (“12” here). 
     
       
         
           
             
               
                 
                   
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     For example, in the example in  FIG. 11 , it is possible to calculate a feature similarity degree T X  using the weighted sum of A City and X City as T X =(((80+80)×3+(75+75)×2+(70+70)×1))/12=76.7. 
     As illustrated in  FIG. 11 , both A City and X City have the same deviation values for the respective feature industries. However, Y City has a higher deviation value for the agriculture, forestry, and fisheries industry, which is the most distinctive feature of A City. In either the case of using the product for calculating the feature similarity degree or the case of using a weighted sum, it is understood that Y City has a higher value than X City, and thus it is possible to preferentially present Y City, which has a more distinctive feature. 
     When the sum of the deviation values of the target local government for each feature industry and the other local government is simply used without multiplying a weight in accordance with the deviation value of the feature industry of the target local government, as illustrated in the “sum” field in  FIG. 11 , the same feature similarity degree is calculated for all the other local governments. Here, for the sake of comparison with the other calculation methods, the sum is produced by dividing by a predetermined fixed number (“6” here). That is to say, in the case of using the sum, in order to identify the other local government having a distinctive feature, a suitable weight has to be multiplied in accordance with the feature industry of the target local government. However, it is difficult to uniformly determine a suitable value of the weight. On the other hand, in the case of using the product, a parameter, such as a weight, or the like does not have to be set, and thus it is possible to easily calculate a suitable feature similarity degree. 
     In each of the embodiments described above, descriptions have been given of the cases where a reference local government name is presented. However, the other information related to the reference local government may be presented together. For example, a database ought to be maintained that includes presentation information including a successful case at the time of making a decision, a failure case, a budget allocation for a decision making matter, the contents of measures introduced by decision making, the organizational structure at the time of making a decision, or the like for each local government. Accordingly, it is possible to present the presentation information that is stored in association with the reference local government together with the reference local government name. 
     In the above descriptions, the descriptions have been given of the mode in which the information presentation programs  50 ,  250 ,  350 , and  450 , which are examples of the programs according to the disclosed techniques, are stored (installed) in the storage unit  43  in advance. However, the present disclosure is not limited to this. It is possible to provide the programs according to the disclosed techniques in the form stored in the storage medium, such as a CD-ROM, a DVD-ROM, a USB memory or the like. 
     All examples and conditional language provided herein are intended for the pedagogical purposes of aiding the reader in understanding the invention and the concepts contributed by the inventor to further the art, and are not to be construed as limitations to such specifically recited examples and conditions, nor does the organization of such examples in the specification relate to a showing of the superiority and inferiority of the invention. Although one or more embodiments of the present invention have been described in detail, it should be understood that the various changes, substitutions, and alterations could be made hereto without departing from the spirit and scope of the invention.