Patent Publication Number: US-11023459-B2

Title: Method, apparatus for data generation, and non-transitory computer-readable storage medium for storing program

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is based upon and claims the benefit of priority of the prior Japanese Patent Application No. 2018-93899, filed on May 15, 2018, the entire contents of which are incorporated herein by reference. 
     FIELD 
     The embodiments discussed herein are related to a method, an apparatus for data generation, and a non-transitory computer-readable storage medium for storing a program. 
     BACKGROUND 
     In recent years, there has been growing interest in collecting, accumulating, and retrieving data on various knowledge. Such data is considered to be represented by some kinds of graph data models. Known graph data models include a resource description framework (RDF). 
     In the data model of the RDF, the relationship in resources is represented by three elements of a subject, a predicate, and an object, which are referred to as a triple. In terms of the field of the graph, the subject and the predicate correspond to a node, and the predicate corresponds to an edge or a link. For example, the triple corresponds to two nodes and the edge connecting them in the graph. In the RDF, a set of a subject, a predicate, and an object, which is called a triple, is the minimum unit. The RDF data set is a set of triples. The predicate may be referred to as an attribute or a property, and the object may be referred to as a value of the attribute or a value of the property. 
     For example, the triple represented by “Fujitsu, Industry Type, Electrical Equipment” has the subject which is “Fujitsu”, the predicate which is “industry type”, and the object which is “electrical equipment”, and indicates that “Fujitsu&#39;s industry type is electrical equipment”. The subject and the predicate are represented by a uniform resource locator (URL), and the object is represented by a character string which is also called a URL or a character string literal. 
     A process of finding a triple corresponding to values in a table described by comma separated values (CSV) or the like from the RDF data set is conceivable. Performing this process makes it possible to use information which does not exist as information in the table but exists as an RDF data set. For example, the process is used in the case of determining the availability of financing by joining a table in which financial information of a loan customer company held by the bank is registered with the RDF data set as the company&#39;s open data. 
     When joining such an RDF data set and a table, the joining has been performed in the related art in the following procedure. First, a query is constructed using the values of the table, and the subject and the predicate that satisfy the condition are identified. Next, a score is calculated for each column of the table with respect to the identified predicate. The score is a value representing the likelihood that the predicate is a predicate corresponding to the column in the table, and represents the frequency at which the predicate appears in each column. Next, a query for finding, for each row of the table, the common subject whose predicate is the label of each column, and whose object is the value is created, and the subjects satisfying the conditions are identified. In a case where the result of collation with the predicate with the highest score has higher collation frequency than the result of collation with the predicate with the second highest score, the result of collation with the predicate with the highest score is made the final result. In a case where the result of collation with the predicate with the highest score has lower collation frequency than the result of collation with the predicate with the second highest score, the result of collation with the predicate with the second highest score and the result of collation with the predicate with the third highest score are compared. This comparison is repeated until the result of collation with the predicate with a specific score has higher collation frequency than the result of collation with the predicate with the highest score following the specific score. The predicate with the specific score is made the final result. 
     A technique of data collection using an RDF data set includes a related art technique of storing association information of an RDF description indicating an association between a plurality of resources, and updating the association information according to an operation on the resources. A related art technique includes identifying attribute information of each numerical value according to the arrangement of input areas to which the numerical values n numerical table data are input. 
     Examples of the related art include Japanese Laid-open Patent Publication No. 2006-221312, and Japanese Laid-open Patent Publication No. 2017-37486. 
     SUMMARY 
     According to an aspect of the embodiments, a method for data generation performed by a computer includes: executing a collation process that includes acquiring reference source data representing a first axis and reference destination data representing a second axis and having a graph structure including a node and an edge, and collating a node of the reference destination data using a value of the acquired reference source data; executing a identification process that includes identifying a second node representing a kind of a first node sharing a first edge between a plurality of collation nodes collated by the collation process, and identifying a third node representing a definition of a domain of the first edge between the first node and the collation nodes; and executing a join process that includes associating the reference source data with the reference destination data based on the second node and the third node. 
     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 block diagram of an information processing device; 
         FIG. 2  is a diagram illustrating an example of table type data; 
         FIG. 3  is a diagram illustrating an example of RDF data; 
         FIG. 4  is a diagram illustrating a collation process using table values; 
         FIG. 5  is a diagram illustrating the acquisition result of the type of a subject; 
         FIG. 6  is a diagram illustrating the acquisition result of the domain of a predicate; 
         FIG. 7  is a diagram illustrating a process of extracting a subject related to each row; 
         FIG. 8  is a flowchart of an entire join process of data; 
         FIG. 9  is a flowchart of the construction of a query and the collation process using the table values; 
         FIG. 10  is a diagram illustrating an example of the query constructed by a first collation unit and collation results; 
         FIG. 11  is a flowchart of an acquisition process of additional information; 
         FIG. 12  is a diagram illustrating the query for obtaining additional information and the acquisition result of additional information; 
         FIG. 13  is a flowchart of a predicate score calculation process; 
         FIG. 14  is a diagram illustrating an example of the calculation result of the predicate score; 
         FIGS. 15A and 15B  are a flow chart of construction of a query and a collation process of a predicate and an object; and 
         FIG. 16  is a diagram illustrating an example of the hardware configuration of the information processing device. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     However, the data set of graph data represents various kinds of things and events and relationships between them. For example, data sets in which various kinds of data coexist in such a manner that a plurality of databases are integrated using flexibility of a graph structure exist in graph data. As a specific example, some data sets of the RDF which is one of the graph data represent information such as companies, affiliated companies, persons, products, events and the like together with their relations. 
     In the process of detecting the subject and the predicate by collating the value of the table with the object in the RDF data set, various kinds of information included in the RDF data set related to that value are collated. For example, in a case where each row of the table data represents information on a company, when the value of the table data is collated with the RDF data set, the persons and products related to the company are also collated in addition to the company in the RDF data set. When the kind of information in the row of the table data and the kind of the RDF data are the same in the collation result, the correctness of the results is high, but when the kinds are different, the correctness of the results is low. Therefore, it may not be possible to derive the correct subject and predicate with respect to the value of the table in the collation described above by a score representing the mere appearance frequency of information in the RDF data set with respect to the information on the table data. 
     In the related art score calculation, the calculation is performed on the hypothesis that the predicate which corresponds to the values in the column with many appearance frequencies in the RDF data set of the collation result is likely to be the predicate corresponding to this column. For example, the predicate corresponding to each column is determined using a score expressing this hypothesis. However, various predicates are mixed in the collation result as described above. Therefore, the score of the predicate in which the kind of row information in the table data is different from the kind of the RFD data set may be high. From this, it is difficult to derive correct predicates under the above-mentioned hypothesis. For example, the predicate in the RDF data set corresponding to the column representing the company name in the table data is actually “name”. However, when collating the name of a company, the score of “affiliation” related to a person may be high as a result of collation with a different kind of the RDF data set. 
     Even with the related art technique of updating the association information according to the operation on the resource, consideration is not given to a selection mistake of the predicate due to collation with a different kind of the RDF data set and a selection mistake of the predicate due to incorrectness of the score obtained by the related art technique. Therefore, it is difficult to improve the correctness of the data association even with this related art technique. In the related art technique of identifying the attribute information of each numerical value according to the arrangement of the input areas in the numerical table data, adaptation to association between data of different groups is not taken into consideration, so that it is difficult to improve the correctness of data association. 
     The disclosed technique is made in view of the above, and has an object to provide a data generation method, a data generation program, and an information processing device that improve the correctness of data association. 
     Hereinafter, embodiments of a data generation method, a data generation program, and an information processing device will be described in detail with reference to the drawings. The data generation method, the data generation program and the information processing device disclosed are not limited by the following embodiments. 
     Embodiments 
       FIG. 1  is a block diagram of an information processing device. As illustrated in  FIG. 1 , an information processing device  1  according to the present embodiment includes an RDF data storage unit  11 , an SPARQL processing unit  12 , a first collation unit  13 , an additional information acquisition unit  14 , an intermediate data DB  15 , a score calculation unit  16 , a second collation unit  17 , a temporary storage unit  18 , and a result management unit  19 . 
     The information processing device  1  may access a table  2  and an RDF data set  3 . The table  2  has a matrix structure. Items are assigned for each column of the table  2 , and the values arranged in each column are classified into items. Values corresponding to each row of the table  2  are arranged. The table  2  is, for example, a CSV file or the like. 
       FIG. 2  is a diagram illustrating an example of table type data. The table  2  holds data of the format illustrated in  FIG. 2 , for example. In the table  2 , each column represents items such as name, location, and year of establishment. In the table  2 , values corresponding to each item are described for each item. In the table  2 , the values of each item correspond to each row. This table  2  is an example of “reference source data”. An arrangement of data represented by items corresponding to each column of the table, for example, an arrangement of data in each row, corresponds to an example of the “first axis”. 
     The RDF data set  3  has a graph structure that expresses the relationship by connecting nodes by edge. The edge extends from one node to another node. In the triple represented by the subject, the predicate and the object, the node at the starting point of the edge represents the subject. The edge represents the predicate. The node at the edge arrival point is the object derived from the edge from the subject which is the node at the starting point of the edge. This RDF data set  3  is an example of “reference destination data”. 
     The arrangement of data expressed by the graph structure corresponds to the “second axis”. 
       FIG. 3  is a diagram illustrating an example of RDF data. The RDF data set  3  has subjects such as _: a, _: a1, _: a2, _: b and _: c in the case of  FIG. 3 . The subject is represented by a URL. Here, the information expressed in the form of underscores, colons and characters represents the URL corresponding to the character displayed there. In the case of  FIG. 3 , the RDF data set  3  has predicates such as &lt;name&gt;, &lt;location&gt;, &lt;industry type&gt;, &lt;major shareholder&gt; and &lt;establishment&gt;. The predicate is also represented by the URL. Here, the character enclosed by &lt; &gt; represents the URL corresponding to the character. In the case of  FIG. 3 , the RDF data set  3  has objects such as “company A”, “city A”, “manufacturing industry”. The object is a URL or a literal (character string). Here, it is assumed that the object is represented by a character string, and the character surrounded by double quotation marks represents the character string itself. 
     The predicate representing a type exists in the RDF data. The type is a kind of specific subject or predicate. In the case of a triple having a predicate representing a type, the object is information indicating the type of the subject. The predicate representing a domain exist in the RDF data. The domain is a constraint on the type of the subject that a specific predicate may take. For example, in a case where “XXX” is described as a domain of definitions of a specific predicate, the type of the subject of the specific predicate is “XXX”. In a case where a domain is not listed, there is no restriction on the type of the subject of the predicate. In the case of a triple having a predicate representing a domain, the object is information representing the domain in a case where the subject of the literal is a predicate. 
     The RDF data storage unit  11  has a storage device. The RDF data storage unit  11  acquires and stores the RDF data from the RDF data set  3 . Hereinafter, a set of RDF data stored in the RDF data storage unit  11  is referred to as an RDF store. 
     The SPARQL processing unit  12  receives, from the first collation unit  13 , the input of a query that instructs an acquisition of the subject and the predicate whose object is the value designated from the RDF store held by the RDF data storage unit  11 . In accordance with the query received from the first collation unit  13 , the SPARQL processing unit  12  acquires RDF data representing the subject or the predicate whose object is the value designated from the RDF store stored in the RDF data storage unit  11 . When acquiring the subject or the predicate in the RDF data, the SPARQL processing unit  12  acquires the URL as a value. When acquiring the object in the RDF data, the SPARQL processing unit  12  acquires the literal as a value. The SPARQL processing unit  12  outputs the acquired value to the first collation unit  13  as the output source of the query. 
     The SPARQL processing unit  12  receives, from the additional information acquisition unit  14 , the input of a query instructing an acquisition of information of the type of the subject or information of the domain of the predicate specified that is designated from the RDF store held by the RDF data storage unit  11 . In accordance with the query received from the additional information acquisition unit  14 , the SPARQL processing unit  12  acquires, from the RDF store stored in the RDF data storage unit  11 , the value of the RDF data corresponding to the information of the type or the information of the domain. The SPARQL processing unit  12  outputs the acquired value to the additional information acquisition unit  14  which is the output source of the query. 
     The first collation unit  13  acquires each value of the table  2 . The first collation unit  13  collates each value of the table  2  with the data included in the RDF store. 
     Specifically, the first collation unit  13  creates a query for acquiring the subject and the predicate whose object is each acquired value.  FIG. 4  is a diagram illustrating a collation process using table values. For example, a description will be given of a case having the table  2  illustrated in  FIG. 4 . In this case, the table  2  has values, FUJITSU LIMITED, Kawasaki-city, Kanagawa Prefecture and 1935. The first collation unit  13  creates a query  21  for acquiring the subject and the predicate whose object is FUJITSU LIMITED, Kawasaki-city, Kanagawa Prefecture, or 1935. “?S” in the query  21  represents any subject. Each of “?p1, ?p2, ?p3” represents any predicate. An optional phrase is used in the query  21  to ignore a case where the subject and the predicate whose object is a character string do not exist. 
     The first collation unit  13  outputs the created query to the SPARQL processing unit  12  and requests an acquisition of the subject and the predicate whose object is the word designated by the query. Thereafter, the first collation unit  13  receives, from the SPARQL processing unit  12 , the input of the subject and the predicate whose object is the word designated by the query. Extraction by the first collation unit  13  of the subject and predicate whose object is the value of the table  2  corresponds to collation of the RDF data set  3  using the value of the table  2 . The fact that the subject and the predicate corresponding to the values of the table  2  has been extracted is referred to as “collated”. The object in the RDF data set collated with the value in the table  2  by this collation is an example of “collation node”. 
     For example, the first collation unit  13  acquires RDF data  22  as a result of the acquisition request by the query  21  as illustrated in  FIG. 4 . For example, the first collation unit  13  acquires the subject “_: 1” whose predicates are the name and the location with respect to the objects, FUJITSU LIMITED and Kawasaki-city, Kanagawa Prefecture. In this case, “_: 1” is a URL representing FUJITSU LIMITED indicated by supplement of the RDF data  22 . The first collation unit  13  acquires the subject “_: 2” whose predicate is the name and location with respect to the objects, FUJITSU LIMITED and Kawasaki-city, Kanagawa Prefecture. In this case, “_: 2” is a URL representing Kawasaki Frontale indicated by supplement of the RDF data  22 . The first collation unit  13  acquires the subject “_: 3” whose predicate is the establishment with respect to the object, 1935. In this case, “_: 3” is a URL representing the Tsukiji Market indicated by supplement of the RDF data  22 . 
     The first collation unit  13  stores the data representing the acquired subject and predicate in the data base (DB)  15  for intermediate data. The acquired subject corresponds to the “first node”. The acquired predicate corresponds to an example of the “first edge”. This first collation unit corresponds to an example of the “collation unit”. 
     The additional information acquisition unit  14  acquires all the subjects stored in the intermediate data DB  15 , and removes duplication. Next, the additional information acquisition unit  14  selects one from the subjects from which duplication has been removed. Next, the additional information acquisition unit  14  creates a query for acquiring the type of the selected subject. For example, the additional information acquisition unit  14  may create a query for acquiring, as the object, the type of the subject selected as the object by using the predicate for acquiring the type of the subject with the selected subject as the subject as it is. 
     The additional information acquisition unit  14  outputs the created query to the SPARQL processing unit  12  and requests an acquisition of the type of the selected subject. Thereafter, the additional information acquisition unit  14  receives, from the SPARQL processing unit  12 , the input of the type of the selected subject. Next, the additional information acquisition unit  14  stores the acquired type of the subject in association with the selected subject in the intermediate data DB  15 . The additional information acquisition unit  14  sequentially selects the subjects from which duplication has been removed one by one, and repeatedly performs an acquisition of the types and a storage of the types into the intermediate data DB  15  with respect to all the subjects from which duplication has been removed. 
       FIG. 5  is a diagram illustrating the acquisition result of the type of the subject. For example, the additional information acquisition unit  14  acquires a type  31  and a type  32  as the type of the subject “_: 1”. The type  31  is a URL representing an organization, and the type  32  is a URL representing a company. The additional information acquisition unit  14  stores the type  31  and the type  32  as the type of “_: 1” in the intermediate data DB  15 . The additional information acquisition unit  14  acquires a type  33  as the type of the subject “_: 2”. The type  33  is a URL representing an organization. The additional information acquisition unit  14  stores a type  332  as the type of “_: 2” in the intermediate data DB  15 . The additional information acquisition unit  14  acquires a type  34  as the type of the subject “_: 3”. The type  34  is a URL representing a market. The additional information acquisition unit  14  stores the type  33  as the type of “_: 3” in the intermediate data DB  15 . The type of the subject is an example of the “second node”. 
     Next, the additional information acquisition unit  14  acquires all the predicates stored in the intermediate data DB  15 , and removes duplication. Next, the additional information acquisition unit  14  selects one from the predicates from which duplication has been removed. Next, the additional information acquisition unit  14  creates a query for acquiring the domain of the selected predicate. For example, the additional information acquisition unit  14  may create a query for acquiring, as the object, the domain of the predicate selected as the object with the selected predicate as the subject and the command, rdfs: domain, as the predicate. 
     The additional information acquisition unit  14  outputs the created query to the SPARQL processing unit  12  and requests an acquisition of the domain of the selected predicate. Thereafter, the additional information acquisition unit  14  receives, from the SPARQL processing unit  12 , the input of the domain of the selected predicate. Next, the additional information acquisition unit  14  stores the acquired domain of the predicate in association with the selected predicate in the intermediate data DB  15 . The additional information acquisition unit  14  sequentially selects the predicates from which duplication has been removed one by one, and repeatedly performs an acquisition of the domains and a storage of the domains into the intermediate data DB  15  with respect to all the subjects from which duplication has been removed. 
       FIG. 6  is a diagram illustrating the acquisition result of the domain of a predicate. For example, the additional information acquisition unit  14  acquires a domain  41  and a domain  42  as the domains of the major shareholder that is the predicate. The domain  41  is a URL representing an organization, and the domain  42  is a URL representing a company. The additional information acquisition unit  14  stores the domain  41  and the domain  42  as the domains of the major shareholder in the intermediate data DB  15 . The additional information acquisition unit  14  acquires the domain  43  and the domain  44  as the domains of the location which is the predicate. The domain  43  is a URL representing an organization, and the domain  44  is a URL representing a company. The additional information acquisition unit  14  stores the domain  43  and the domain  44  as the domains of the location in the intermediate data DB  15 . The domain of the predicate is an example of the “third node”. The additional information acquisition unit  14  corresponds to an example of the “identification unit”. 
     The intermediate data DB  15  is a database for storing intermediate data for the process of associating the table  2  with the RDF data. The intermediate data DB  15  may be created in advance and stored in the information processing device  1  or may be created by the information processing device  1  at the start of the process of associating the table  2  with the RDF data. The intermediate data DB  15  stores the subject and predicate whose object is each value of the table  2  acquired by the first collation unit  13 , and the type of each subject and the domain of each predicate. 
     The score calculation unit  16  acquires the type used as the type of the subject stored in the intermediate data DB  15  after excluding duplication. In the following, the type acquired in this process is called the “constraint type”. The score calculation unit  16  calculates the score of each constraint type. This score is a value indicating how highly probable each constraint type may be as candidate in collation. The calculation of the score of each constraint type by the score calculation unit  16  will be described in detail below. 
     First, the score calculation unit  16  acquires the number of columns and the number of rows of the table  2 . The score calculation unit  16  refers to the RDF store stored in the RDF data storage unit  11  and calculates the number of different subjects representing the number of different subjects included in all the RDF data. The score calculation unit  16  acquires the number of different subjects having each constraint type in all the RDF data included in the RDF store. The score calculation unit  16  calculates the maximum number of columns that may be collated under the constraint under which the type of the subject is each constraint type in the table  2 . 
     The score calculation unit  16  calculates the score of each constraint type using the following Expression (1). Here, the constraint type selected from the constraint types is represented as the type t. 
     
       
         
           
             
               
                 
                   
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     where U t  is the score of the type t, C is the number of columns in the table  2 , R is the number of rows of the table  2 , N is the number of different subjects in all the RDF data included in the RDF store, n t  is the number of different subjects having the type t as a type in all the RDF data included in the RDF store, and c t,r  is the maximum number of columns that has been collated under the constraint under which the subject is the type t. 
     In Expression (1), N/n t  is used to reduce the score, as regarded the frequently appearing type as an abstract type. c t,r /C is used to increase the score of a type that satisfies more columns. 
     Next, the score calculation unit  16  acquires, from the intermediate data DB  15 , all the predicates after excluding duplication. The score calculation unit  16  acquires the domain of each predicate from the intermediate data DB  15 . The score calculation unit  16  calculates the score of the predicate corresponding to each column by the following Expression (2) using the calculated score of each constraint type. The score of the predicate represents the probability of associating a specific predicate with a certain column. 
     
       
         
           
             
               
                 
                   
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     where T is the number of kinds of constraint types, S p,c  is the score of the column c for the predicate p, and f t,p,c,r  is a function satisfying the following conditions. For example, in a case where the subject of the type t and the predicate p corresponding to the column c of the row r exist, and the type t of the subject satisfies the constraint of the domain of the predicate p, f t,p,c,r  is 1. In a case where the subject of the type t and the predicate p corresponding to the column c of the row r exist, but the type t of the subject does not satisfy the constraint of the domain of the predicate p, f t,p,c,r  is 0.5. In a case where the subject of the type t and the predicate p corresponding to the column c of the row r do not exist, but the subject of the type t corresponding to the row r exist, and the type t of the subject does not satisfy the constraint of the domain of the predicate p, f t,p,c,r  is 0.5. f t,p,c,r  is 0 in cases other than the upper limit above. f t,p,c,r  represents how closely a specific predicate satisfies the constraint of domain. 
     Thereafter, the score calculation unit  16  outputs the calculated score of the predicate corresponding to each column to the second collation unit  17 . The score of the predicate calculated by the score calculation unit  16  corresponds to the “correlation strength”. 
     The second collation unit  17  receives the input of the score of the predicate corresponding to each column of the table  2  from the score calculation unit  16 . The second collation unit  17  selects one column from the columns of the table  2 . Next, the second collation unit  17  identifies the predicate with the highest score and the predicate with the second highest score in the column of the item. The predicate with the highest score and the predicate with the second highest score are the first candidates for the predicate representing the column. The second collation unit  17  creates a query for acquiring the subject whose predicate is the predicate with the highest score and whose object is each value in the selected column. Next, the second collation unit  17  outputs the created query to the SPARQL processing unit  12  and requests an acquisition of the subject. Thereafter, the second collation unit  17  receives, from the SPARQL processing unit  12 , the input of the subject whose predicate is the predicate with the highest score and whose object is each value in the selected column. Similarly, the second collation unit  17  acquires, from the SPARQL processing unit  12 , the subject whose predicate is the predicate with the second highest score and whose object is each value in the selected column. 
     Extraction by the second collation unit  17  of the subject corresponding to the object and the predicate corresponds to collation by the value of the table  2  and the selected object. The fact that the subject corresponding to the object and predicate used for the collation has been extracted is referred to as “collated”. 
     In a case where the result of collation with the predicate with the highest score has higher collation frequency than the result of collation with the predicate with the second highest score, the second collation unit  17  determines that the predicate with the highest score represents the selected column. In this case, the second collation unit  17  stores the determined result in the temporary storage unit  18 . 
     On the other hand, in a case where the result of collation with the predicate with the highest score has lower collation frequency than the result of collation with the predicate with the second highest score, the second collation unit  17  performs collation on the column selected by using the predicate with the third highest score. In a case where the result of collation with the predicate with the second highest score has higher collation frequency than the result of collation with the predicate with the third highest score, the second collation unit  17  determines that the second highest predicate represents the selected column. In this way, the second collation unit  17  repeats the collation and comparison process until the result of collation with the predicate with a specific score has higher collation frequency than the result of collation with the predicate with the highest score following the specific score. 
     The second collation unit  17  repeats storage of the determination result of the predicate corresponding to each column into the temporary storage unit  18 . The second collation unit  17  finally stores the determination result of the corresponding predicate for all the columns in the temporary storage unit  18 . Each column of the table  2  corresponds to an example of “item”. Identification of the corresponding predicate for each column is an example of “identification of the first edge corresponding to each item”. 
     Next, using the information of the predicate corresponding to each column stored in the temporary storage unit  18 , the second collation unit  17  creates, for each column, a query for acquiring the subject whose object is each value in each column and whose predicate is the predicate corresponding to each column. Thereafter, the second collation unit  17  outputs the created query to the SPARQL processing unit  12 . As a result, the second collation unit  17  requests, for each row, an acquisition of the common subject for each column whose object is each value in each column and whose predicate is predicate corresponding to each column. Thereafter, the second collation unit  17  acquires, for each row, from the SPARQL processing unit  12 , the subject common to each column corresponding to each value in each column. The acquisition of this subject is an example of “extraction of the first node sharing the first edge identified for each item between the collation nodes and common to each item. 
     The second collation unit  17  outputs, to the result management unit  19 , the information of the subject common to each column corresponding to each value in each column and the information of the predicate corresponding to each column stored in the temporary storage unit  18 . The subject common to each column corresponding to each value in each column for each row may be regarded as related information of each row. 
     The second collation unit  17  is an example of the “identification unit”. Each row of the table  2  is an example of “row data”. The association between each row of the table  2  and the related information which is RDF data corresponds to an example of “association between each extracted first node and each row data” and “association between reference source data and reference destination data”. 
       FIG. 7  is a diagram illustrating a process of extracting a subject related to each row. Here, the case where the second collation unit  17  has determined that the predicate representing the first row of the table  2  is &lt;name&gt;, the predicate representing the second row is &lt;location&gt;, and the predicate representing the third row is &lt;year of establishment&gt; will be described. For the first row of the table  2 , the second collation unit  17  creates a query  71  for acquiring the subject common to each row whose predicate is &lt;name&gt; and whose object is FUJITSU LIMITED in the first column, whose predicate is &lt;Location&gt; and whose object is Kawasaki-city, Kanagawa Prefecture in the second column, and whose predicate is &lt;year of establishment&gt; and whose object is 1935 in the third column. In this case, the optional phrase is used in the query  71  in order to ignore the case where the subject having these as the object and the predicate does not exist. The second collation unit  17  requests the SPARQL processing unit  12  to acquire the subject using the created query  71 . As a result, as illustrated by RDF data  72 , the second collation unit  17  extracts “_: F” as the subject whose object is FUJITSU LIMITED when &lt;name&gt; is the predicate, and whose object is Kawasaki-city, Kanagawa Prefecture when &lt;Location&gt; is the predicate. In the RDF data  72 , although “_: f” has the predicate which is &lt;industry type&gt; and has the object which is Electrical Equipment, the information, which exists in the RDF data set  3 , is not the target of collation. 
     The result management unit  19  receives, from the second collation unit  17 , the input of information of the subject corresponding to each value in each column and information of the predicate corresponding to each column stored in the temporary storage unit  18 . The result management unit  19  stores, as the related information of each row and each column of the table  2 , information of the subject corresponding to each value in each column and information of the predicate corresponding to each column stored in the temporary storage unit  18 . With this related information, the data of the table  2  and the data of RDF data set  3  are joined. For example, when retrieving a row of the table  2 , the related information corresponding to the row and the value of the RDF data set  3  associated with the related information are acquired. In this way, by associating each row of the table  2  with the related information that is the RDF data by the information processing device  1 , an appropriate triple corresponding to the table data value may be reliably detected from the RDF data. For example, it is possible to effectively utilize the information of the RDF data set which is not in the table information by using this association. 
     The result management unit  19  may output the stored information to another device that performs processing using the table  2  and the another device may perform processing using this related information. The result management unit  19  may cause an output device such as a monitor to output the related information and notify the user of the related information. 
     Next, with reference to  FIG. 8 , a flow of the overall data join process by the information processing device  1  will be described.  FIG. 8  is a flowchart of an entire join process of data. 
     The administrator of the information processing device  1  prepares the table  2 , the intermediate data DB  15  which is a DB for holding intermediate data, and the temporary storage unit  18  which is a temporary area for storing the collation result. The administrator loads RDF data from the RDF data set  3  using an input device (not illustrated) and stores the RDF data in the RDF data storage unit  11  of the information processing device  1 , thereby preparing the RDF store (step S 1 ). 
     Next, the first collation unit  13  constructs a query for acquiring the subject and the predicate whose object is the value of the table  2 , and collates the RDF data using the value of the table  2  (step S 2 ). 
     Next, the additional information acquisition unit  14  acquires the type of the subject and the domain of the predicate as additional information (step S 3 ). 
     Next, the score calculation unit  16  calculates the score of the predicate for each column (step S 4 ). 
     Next, the second collation unit  17  selects the predicate according to the score for each column, constructs a query for acquiring the subject whose object is the value in each column of the table  2  according to the selected predicate, collates the predicate and the object, and extracts the related information of each row and each column using the collation result (step S 5 ). 
     Thereafter, the result management unit  19  stores the related information of each row and each column extracted by the second collation unit  17 , transmits the information to other devices, and makes a notification to the user (step S 6 ). 
     Next, referring to  FIGS. 9 and 10 , the flow of the construction of the query by the first collation unit  13  and the collation process using the value of the table  2  will be described.  FIG. 9  is a flowchart of the construction of a query and the collation process using the table values. The process illustrated in  FIG. 9  corresponds to an example of the process performed in step S 2  in  FIG. 8 .  FIG. 10  is a diagram illustrating an example of the query constructed by a first collation unit and collation results. Here, the case will be described where the first collation unit  13  performs collation using the table  2  and the RDF data  23  illustrated in  FIG. 10 . 
     The first collation unit  13  selects one unselected row in the table  2  (step S 101 ). 
     Next, the first collation unit  13  reads each value of the selected row (step S 102 ). 
     Next, using each value of the read row, the first collation unit  13  constructs a query for acquiring the subject and the predicate whose object is each value (step S 103 ). For example, the first collation unit  13  constructs a query  24  for acquiring the subject and the predicate whose object is each value in the first row of the table  2  illustrated in  FIG. 10 . The query  24  is a query for acquiring the subject and the predicate whose objects are company A, city A and 1935. An optional phrase is used in the query  24  to ignore a case where the subject and the predicate whose object is a character string do not exist. 
     Next, the first collation unit  13  outputs the constructed query to the SPARQL processing unit  12 . Thereafter, the first collation unit  13  acquires data from the RDF store stored in the RDF data storage unit  11  via the SPARQL processing unit  12  (step S 104 ). For example, the first collation unit  13  acquires data  25  of  FIG. 10  as a response of the query  24 . For example, the first collation unit  13  acquires “_: a” and “name” as the subject and the predicate whose object is company A. Besides, the first collation unit  13  acquires “_: a1” and &lt;major shareholder&gt; as the subject and the predicate whose object is company A. The first collation unit  13  acquires “_: a” and &lt;location&gt; as the subject and the predicate whose object is city A. The first collation unit  13  acquires “_: a2” and &lt;establishment&gt; as the subject and the predicate whose object is 1935. 
     Next, the first collation unit  13  stores the acquired data in the intermediate data DB  15  (step S 105 ). For example, the first collation unit  13  stores the data  25  in the intermediate data DB  15 . 
     Thereafter, the first collation unit  13  determines whether selection of all the rows in the table  2  has been completed (step S 106 ). When unselected rows remain (step S 106 : “NO”), the first collation unit  13  causes the process to return to step S 101 . 
     On the other hand, when selection of all the rows has been completed (step S 106 : “YES”), the first collation unit  13  ends the collation process. 
     Next, with reference to  FIGS. 11 and 12 , the flow of acquisition processing of additional information by the additional information acquisition unit  14  will be described.  FIG. 11  is a flowchart of an acquisition process of additional information.  FIG. 11  corresponds to an example of the process performed in step S 3  in  FIG. 8 .  FIG. 12  is a diagram illustrating the query for obtaining additional information and the acquisition result of additional information. A case where the data  25  illustrated in  FIG. 12  is stored in the intermediate data DB  15  will be described. 
     The additional information acquisition unit  14  removes duplication of the subject in the intermediate data DB  15  (step S 202 ). For example, in the data  25  of  FIG. 12 , in the additional information acquisition unit  14 , the subject of the first row and the subject of the second row are duplicate as “_: a”. Therefore, the additional information acquisition unit  14  leaves one “_: a” as the subject. For example, the subjects are three “_: a”, “_: a1” and “_: a2”. 
     Next, the additional information acquisition unit  14  selects one unselected subject from the subjects from which duplication is removed (step S 203 ). 
     Next, the additional information acquisition unit  14  constructs a query for acquiring the type of the selected subject (step S 204 ). For example, the additional information acquisition unit  14  constructs a query  35  in  FIG. 12 . The query  35  is a query for acquiring the type of the subject “_: a”. The predicate a in the query  35  is a predicate for acquiring the type. 
     Next, the additional information acquisition unit  14  outputs the constructed query to the SPARQL processing unit  12 , and acquires the type of the selected subject from the SPARQL processing unit  12  (step S 205 ). For example, the additional information acquisition unit  14  acquires, as the type of each subject of the data  25 , the objects of the third to seventh rows of data  50  as the type of each subject. Here, the row in which the value is first stored in the data  50  is set to 0 row. The additional information acquisition unit  14  may acquire a plurality of types for one subject. 
     Next, the additional information acquisition unit  14  stores the information of the acquired type information in the intermediate data DB  15  (step S 206 ). 
     Thereafter, the additional information acquisition unit  14  determines whether the acquisition of the types of all the subjects stored in the intermediate data DB  15  has been completed (step S 207 ). When the subject whose type has not been acquired still remains (step S 207 : “NO”), the additional information acquisition unit  14  causes the process to return to step S 203 . 
     On the other hand, in a case where the acquisition of the types of all the subjects has been completed (step S 207 : “YES”), the additional information acquisition unit  14  removes duplication of the predicates in the intermediate data DB  15  (step S 208 ). For example, in the data  25  of  FIG. 12 , the additional information acquisition unit  14  has no duplication of predicates. Therefore, the additional information acquisition unit  14  acquires four of &lt;name&gt;, &lt;location&gt;, &lt;major shareholder&gt;, and &lt;establishment&gt; as the predicates from which duplication is removed. 
     Next, the additional information acquisition unit  14  selects one unselected predicate from the predicates from which duplication is removed (step S 209 ). 
     Next, the additional information acquisition unit  14  constructs a query for acquiring the domain of the selected predicate (step S 210 ). For example, the additional information acquisition unit  14  constructs a query  45  in  FIG. 12 . The query  45  is a query for acquiring the domain of the predicate &lt;location&gt;. rdfs: domain is a predicate for acquiring the domain of the subject. 
     Next, the additional information acquisition unit  14  outputs the constructed query to the SPARQL processing unit  12 , and acquires the domain of the selected predicate from the SPARQL processing unit  12  (step S 211 ). For example, the additional information acquisition unit  14 , as the domain of each predicate, acquires the objects of the 8th to 12th rows of the data  50  as the type of each subject of the data  25 . The additional information acquisition unit  14  may acquire a plurality of domains for one predicate. 
     Next, the additional information acquisition unit  14  stores the information of the acquired domain in the intermediate data DB  15  (step S 212 ). 
     Thereafter, the additional information acquisition unit  14  determines whether the acquisition of the domains of all the predicates stored in the intermediate data DB  15  has been completed (step S 213 ). When the predicate whose domain has not been acquired still remains (step S 213 : “NO”), the additional information acquisition unit  14  causes the process to return to step S 209 . 
     On the other hand, in a case where the acquisition of the domains of all the predicates has been completed (step S 213 : “YES”), the additional information acquisition unit  14  ends the acquisition process of the additional information. In this case, the data  50  is stored in the intermediate data DB  15 . In the data  50 , the relationship between the subject and the type and the relationship between the predicate and the domain are stored in the form of triple, but the storage method is not limited thereto. 
     Next, with reference to  FIGS. 13 and 14 , the flow of the predicate score calculation processing by the score calculation unit  16  will be described.  FIG. 13  is a flowchart of a predicate score calculation process.  FIG. 13  corresponds to an example of the process performed in step S 4  in  FIG. 8 .  FIG. 14  is a diagram illustrating an example of the calculation result of the predicate score. 
     The score calculation unit  16  calculates the score of each type using Expression (1) (step S 301 ). 
     Next, the score calculation unit  16  selects one unselected predicate from the intermediate data DB  15  (step S 302 ). 
     Next, the score calculation unit  16  selects one unselected column from the table  2  (step S 303 ). 
     Next, the score calculation unit  16  calculates the score of the selected predicate in the selected column using the score of each type calculated by Expression (2) (step S 304 ). 
     Next, the score calculation unit  16  determines whether calculation of the score for all the columns with respect to the selected predicate has been completed (step S 305 ). When a column for which no score has been calculated remains (step S 305 : “NO”), the score calculation unit  16  causes the process to return to step S 303 . 
     On the other hand, when the calculation of the scores for all the columns has been completed (step S 305 : “YES”), the score calculation unit  16  determines whether the calculation of the scores has been completed with respect to all the predicates stored in the intermediate data DB  15  (step S 306 ). 
     In a case where a predicate whose score has been calculated remains (step S 306 : “NO”), the score calculation unit  16  causes the process to return to step S 302 . On the other hand, when the calculation of the scores of all the predicates has been completed (step S 306 : “YES”), the score calculation unit  16  ends the score calculation process. 
     The score calculation unit  16  acquires the score for each column with respect to each predicate described in the score table  60  illustrated in  FIG. 14 , for example. For example, the score calculation unit  16  calculates the score of the predicate, “name”, in the 0th column of the table  2  as 0.3. The score calculation unit  16  calculates the score of the predicate, “name”, in the first column of the table  2  as 0.2. The score calculation unit  16  calculates the score of the predicate, “name”, in the second column of the table  2  as 0.1. Similarly, the score calculation unit  16  calculates the score of the predicate, “location”, in the 0th column of the table  2  as 0.2. The score calculation unit  16  calculates the score of the predicate, “location”, in the first column of the table  2  as 0.3. The score calculation unit  16  calculates the score of the predicate, “location”, in the second column of the table  2  as 0.1. 
     Next, referring to  FIGS. 15A and 15B , the flow of the construction of the query and the collation process of the predicate and the object by the second collation unit  17  will be described.  FIG. 15  (i.e.  FIGS. 15A and 15B ) is a flow chart of construction of the query and the collation process of the predicate and the object.  FIG. 15  corresponds to an example of the process performed in step S 5  in  FIG. 8 . 
     The second collation unit  17  sets N representing the predicate number to −1 and C representing the column number to −1 (step S 401 ). 
     Next, the second collation unit  17  increments C by 1 (step S 402 ). 
     Next, the second collation unit  17  increments N by 1 (step S 403 ). 
     Next, the second collation unit  17  acquires the predicate with the N-th highest score and the predicate with the (N+1)-th highest score (step S 404 ). 
     Next, the second collation unit  17  sets, to 0, i representing the number of rows for which the predicate with the N-th highest score may be collated. The second collation unit  17  sets, to 0, j representing the number of rows for which the predicate with the (N+1)-th highest score may be collated (step S 405 ). 
     Next, the second collation unit  17  selects one unselected row from the rows of the table  2  (step S 406 ). 
     Next, the second collation unit  17  constructs the query Q 0  from the value of the C-th column and the predicate with the N-th highest score (step S 407 ). 
     Next, the second collation unit  17  constructs the query Q 1  from the value of the C-th column and the predicate with the (N+1)-th highest score (step S 408 ). 
     Next, the second collation unit  17  performs an acquisition of data from the RDF store using the Q 0  (step S 409 ). In this case, the second collation unit  17  acquires the subject whose object is the value of the C-th column when using the predicate with the N-th highest score. 
     The second collation unit  17  determines whether the data has been acquired (step S 410 ). When the data is not acquired (step S 410 : “NO”), the second collation unit  17  causes the process to proceed to step S 412 . 
     On the other hand, when the data has been acquired (step S 410 : “YES”), the second collation unit  17  increments i by 1 (step S 411 ). 
     Next, the second collation unit  17  performs an acquisition of the data from the RDF store using the Q 1  (step S 412 ). In this case, the second collation unit  17  acquires the subject whose object is the value of the C-th column when using the predicate with the (N+1)-th highest score. 
     The second collation unit  17  determines whether the data has been acquired (step S 413 ). In a case where the data is not acquired (step S 413 : “NO”), the second collation unit  17  causes the process to proceed to step S 415 . 
     On the other hand, when the data has been acquired (step S 413 : “YES”), the second collation unit  17  increments j by 1 (step S 414 ). 
     Thereafter, the second collation unit  17  determines whether selection of all the rows of the table  2  has been completed (step S 415 ). In a case where a row that has not yet been selected remains in the rows of the table  2  (step S 415 : “NO”), the second collation unit  17  causes the process to return to step S 406 . 
     On the other hand, when selection of all the rows of the table  2  has been completed (“YES” at step S 415 ), the second collation unit  17  determines whether the score of the predicate with the (N+1)-th highest score is the lowest score (step S 416 ). In a case where the score of the predicate with the (N+1)-th highest score of is the lowest score (step S 416 : “YES”), the second collation unit  17  sets N to 0 (step S 147 ). 
     On the other hand, in a case where the score of the predicate with the (N+1)-th highest score is not the lowest score (step S 416 : “NO”), the second collation unit  17  determines whether the number of rows i is less than the number of rows j (step S 418 ). When the number of rows i is less than the number of rows j (step S 418 : “YES”), the second collation unit  17  causes the process to return to step S 403 . 
     On the other hand, when the number of rows i is equal to or larger than the number of rows j (step S 418 : “NO”), the second collation unit  17  stores, in the temporary storage unit  18 , the information that the predicate with the N-th highest score represents the C-th column in the table  2  (step S 419 ). 
     The second collation unit  17  determines whether C is the last column in the table  2 , that is, whether all the columns have been selected (step S 420 ). In a case where C is not the last column (step S 420 : “NO”), the second collation unit  17  causes the process to return to step S 402 . 
     On the other hand, in a case where C is the last column (step S 420 : “YES”), the second collation unit  17  constructs a query using the information of the predicate corresponding to each column in the table  2  stored in the temporary storage unit  18  (step S 421 ). For example, the second collation unit  17  constructs, for each row, a query for extracting the subject common to all the columns whose object is the value in each column when using the predicate corresponding to each column. 
     The second collation unit  17  acquires the data from the RDF store using the constructed query (step S 422 ). For example the second collation unit  17  acquires, for each row, the information of the subject common to all the columns whose object is the value in each column when using the predicate corresponding to each column. 
     As described above, in the data generation method according to the present embodiment includes performing collation using the value of the table to acquire the subject and the predicate, and acquiring additional information indicating the restriction on the subject and the predicate from the RDF data set. The data generation method according to the present embodiment includes calculating the score of the predicate for each column using the acquired restriction to obtain the predicate corresponding to each column using the calculated score. The data generation method according to the present embodiment includes performing collation using the obtained predicate to extract the subject corresponding to each row. 
     As described above, the data generation method according to the present embodiment may derive an appropriate predicate representing each column by using ontology information such as constraint of the type of the subject and the domain of the predicate of the RDF data set. It is possible to extract an appropriate subject corresponding to each row by performing collation using an appropriate predicate. As a result, it is possible to improve the correctness of associating the table type data with the RDF data set, and it is possible to properly join the table type data and the RDF data set. Therefore, it is possible to reliably detect an appropriate triple corresponding to the value of the table data from the RDF data, and it is possible to effectively utilize the information of the RDF data set which the information in the table does not include. 
     &lt;Hardware Configuration&gt; 
     The information processing device  1  according to the above-described embodiment has a hardware configuration as illustrated in  FIG. 16 , for example.  FIG. 16  is a diagram illustrating an example of the hardware configuration of the information processing device. The information processing device  1  includes a central processing unit (CPU)  91 , a random access memory (RAM)  92 , a read only memory (ROM)  93 , and a hard disk drive (HDD)  94 . The information processing device  1  includes a communication interface (I/F)  95 , an input/output interface (I/F)  96 , and a media interface (I/F)  97 . 
     The CPU  91  operates based on a program stored in the ROM  93  or the HDD  94  and controls respective units. The ROM  93  stores a boot program executed by the CPU  91  when the information processing device  1  is activated, a program depending on the hardware of the information processing device  1 , and the like. 
     The HDD  94  stores programs executed by the CPU  91 , data used by such programs, and the like. The communication interface  95  receives data from another device via the network, transmits the data to the CPU  91 , and transmits the data generated by the CPU  91  to another device via the network. 
     The CPU  91  controls an output device such as a display and a printer, and an input device such as a keyboard and a mouse via the input/output interface  96 . The CPU  91  acquires data from the input device via the input/output interface  96 . The CPU  91  outputs the generated data to the output device via the input/output interface  96 . 
     The media interface  97  reads a program or data stored in a recording medium  98  and provides them to the CPU  91  via the RAM  92 . The CPU  91  loads the program from the recording medium  98  onto the RAM  92  via the media interface  97  and executes the loaded program. The recording medium  98  includes an optical recording medium such as a digital versatile disc (DVD), a phase change rewritable disk (PD), a magneto-optic recording medium such as a magneto-optical disk (MO), a tape medium, a magnetic recording medium, a semiconductor memory, or the like. 
     For example, the RAM  92  and the HDD  94  of the information processing device  1  implement functions of the RDF data storage unit  11 , the intermediate data DB  15 , and the temporary storage unit  18 . By executing the programs loaded on the RAM  92 , the CPU  91  of the information processing device  1  implements the functions of the SPARQL processing unit  12 , the first collation unit  13 , the additional information acquisition unit  14 , the score calculation unit  16 , the second collation unit  17  and the result management unit  19 . The CPU  91  of the information processing device  1  reads and executes these programs from the HDD  94 . As another example, the CPU  91  of the information processing device  1  may read the programs from the recording medium  98 , or may acquire these programs from another device via the network. 
     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.