Source: http://www.freepatentsonline.com/9037593.html
Timestamp: 2018-09-26 08:21:00
Document Index: 336870637

Matched Legal Cases: ['art 6', 'art 22', 'art 22', 'art 4', 'art 21', 'art 4', 'art 12', 'art 13', 'art 14', 'art 14', 'art 14', 'art 4', 'art 4', 'art 5', 'art 21', 'art 22', 'art 21', 'art 22', 'art 4', 'art 5', 'art 5', 'art 6', 'art 22', 'art 22', 'art 21', 'art 36', 'art 56']

Comparison of character strings - FUJITSU LIMITED
United States Patent 9037593
Mineno, Kazuo (Kawasaki, JP)
13/219817
G06F7/00; G06F17/27; G06F17/30
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8548791 Validation of the consistency of automatic terminology translation 2013-10-01 Itagaki et al. 704/2
7991987 Comparing text strings 2011-08-02 Cabot 712/300
20110106821 Semantic-Aware Record Matching 2011-05-05 Hassanzadeh et al. 707/749
20080282073 Comparing text strings 2008-11-13 Cabot 712/300
20060217954 Translation device, image processing device, translation method, and recording medium 2006-09-28 Koyama et al. 704/2
7072826 Language conversion rule preparing device, language conversion device and program recording medium 2006-07-04 Wakita
6470306 Automated translation of annotated text based on the determination of locations for inserting annotation tokens and linked ending, end-of-sentence or language tokens 2002-10-22 Pringle et al. 704/3
JP63261457 October, 1988
JP03008082 January, 1991
JP2866944 March, 1999
JP2000305930A 2000-11-02 LANGUAGE CONVERSION RULE PREPARING DEVICE, LANGUAGE CONVERTER AND PROGRAM RECORDING MEDIUM
JP2003058537A 2003-02-28 METHOD, SERVER, AND PROGRAM FOR TEXT DATA ANALYSIS AND RECORDING MEDIUM WITH THIS PROGRAM RECORDED
JP2003167898A 2003-06-13 INFORMATION RETRIEVING SYSTEM
JPH038082A 1991-01-16
JPS63261457A 1988-10-28
JPH02866944A
When performing a character string comparison process, conventional techniques employ nothing more than simple comparison between notations of character strings. With such comparison, character strings with different notations are determined to be different character strings even if these character strings have substantially the same semantic content. Even if morphological analysis and syntactic parsing are performed prior to the comparison, the morphological analysis result and accordingly, the comparison result exhibits determination of different character strings as long as the notations of the character strings are different because the conventional syntactic parsing does not reflect the semantic contents of individual words. Thus, it is difficult for the conventional techniques to determine if two character strings are consistent with each other taking the semantic contents into account.
Patent Document 1: Japanese Laid-open Patent Publication No. H3-8082A
Patent Document 2: Japanese Laid-open Patent Publication No. 2003-167898A
Patent Document 3: Japanese Laid-open Patent Publication No. S63-261457A
Patent Document 4: Japanese Laid-open Patent Publication No. 2003-58537A
Patent Document 5: Japanese Laid-open Patent Publication No. 2000-305930A
In the first embodiment, the above-described character string comparison is applied to a name identification process for determining whether records containing multiple items agree with each other. In this specification, the term “agree with” means not only perfect matching but also a certain degree of similarity between items under comparison. The name identification process may be used to detect a data item relevant to name-identification source data, from among name-identification target data. To be more precise, one record and another record contained in the same database are compared in the name identification process, and if two records under the comparison agree with each other, one of the overlapped records is deleted. Another application is comparison between a record in one database and a record in another database in the name identification process. In this case, if there is relevancy between the records (for example, having the same or similar value in a key item), these records are associated with each other.
The name-identification process definition 9 includes, for example, information for identifying the name-identification source data 2A and the name-identification target data 2B, items to be subjected to the name-identification process, evaluation functions applied to the respective items and weighting values for the evaluation results, and threshold values as determination criteria used in the determination part 6 to determine whether two records agree with each other, as illustrated in FIG. 2. An evaluation function using a conceptual structure (a conceptual evaluation function) can be selected by designating “conceptual_evaluation_XX”. In this specification, explanation is made of an example in which the conceptual evaluation function is designated as the evaluation function.
For example, in the name-identification process definition illustrated in FIG. 2, the items to be subjected to name-identification designated by the first <name-identification_item> tag are a pair of a “name” item of the “name-identification source data” and a “name” item of the “name-identification target data”. The corresponding evaluation function is “conceptual_evaluation_personal_name”, and the weighting factor for the evaluation result is “0.4”. Since “conceptual_evaluation_personal_name” is designated as the evaluation function to be applied to the processed items, the semantic domain treated by this evaluation function is “personal name”. Accordingly, the analytical grammar 16, the standard conceptual structure 17, and the normalization rule 18 that correspond to the “personal name” domain are used.
In the specification, a “semantic domain” is a domain expressed by individual character strings to be processed. In general, items (or columns) in a table in a relational database (RDB) are structured as an aggregate of character strings having specific semantic domains as typically represented by the item names. Elements in extensible markup languages (XML) are also structured so as to have character strings having specific semantic domains typically represented by the element names, such character strings being treated as values or attributes. For instance, if the semantic domain of the item “name” in the RDB is “personal name”, then the item “name” of each record has a character string representing a name of a person, such as “Ichiro Tanaka” or “Jiro Suzuki” belonging semantically to the “personal name” domain, and such a character string is treated as a value. In another example, if the semantic domain of <name> element, which is a subelement of <member> element of XML, indicates a “member's name”, then the <name> element (i.e., a subelement of <member> element) has a value of the character string such as “Ichiro Tanaka” or “Jiro Suzuki” belonging semantically to the “personal name” domain.
In this specification, a “conceptual code” is a code for identifying a concept or a general idea of a word in the real world from the semantic viewpoint (or at a conceptual level). A word has one conceptual code. For example, the word “Fujitsu” in FIG. 4 has a conceptual code “FUJITSU” which is a distinction from other companies.
In this specification, a “semantic attribute” is an attribute expressing the meaning of a word, and one word may have multiple semantic attributes. For example, let's assume two character strings, “a company in the Fuji area” and “a company Fuji Michio is working for”. The word “Fuji” in the former character string has a “regional name” as the grammatical attribute, and this character string means “a company located in an area named Fuji”. The word “Fuji” in the latter character string has a “family name” as the grammatical attribute, and this character string means “a company to which Mr. Fuji belongs”. In this manner, there are a number of words having the same notation but with different semantic attributes, and therefore, it is proper to distinguish them from each other. The word “Fujitsu” in FIG. 4 has two semantic attributes, “Company Name” and “Followed by ‘Limited’”. The word “Middleware Division” has two semantic attributes, “Organization Name” and “Division”.
The analytical grammar 16 exemplified in FIG. 6 is one corresponding to the item of “OFFICE”. In the explanation below, [COMPANY NAME] denotes a word whose semantic attribute is a company name. (The same applies to the other.)
<2> denotes that if [COMPANY NAME] and [LEGAL PERSONALITY] are consecutive, and if the semantic attribute of the word [COMPANY NAME] does not include [ANTECEDED BY “LIMITED”], then the combination of [COMPANY NAME] and [LEGAL PERSONALITY] is regarded as [COMPANY NAME].
The conceptual structure illustrated in FIG. 7A and FIG. 7B is one created for the character string of item “OFFICE”. More detailed explanation is made below.
FIG. 8A and FIG. 8B illustrates examples of the standard conceptual structure 17. The standard conceptual structure 17 is a standard form of a conceptual structure according to the semantic domain treated by the conceptual evaluation function applied to an item, and it exists according to the semantic domain of each of the conceptual evaluation functions. Since the standard conceptual structure 17 is a standard form determined separately from an actual individual conceptual structure, in the internal expression, each element has only information indicating the semantic attribute as illustrated in FIG. 8A. In the standard conceptual structure 17, each element is furnished with “$” at the beginning so as to be distinguished from the actual individual conceptual structure. FIG. 8B is a schematic diagram of the standard conceptual structure 17 of FIG. 8A.
The standard conceptual structure 17 illustrated in FIG. 8A and FIG. 8B is one provided for the item “OFFICE”.
FIG. 9 illustrates standard conceptual structures 17 corresponding to multiple semantic domains, in which a “company” domain, an “address” domain, and a “full name” domain are schematized. The standard conceptual structure 17 is held for each semantic domain treated by a conceptual evaluation function.
The normalization rule 18 illustrated in FIG. 10 is one example that is used to convert the conceptual structure of “company” domain generated from the item “OFFICE” into a standard conceptual structure 17 of the “company” domain illustrated in FIG. 9, and the particulars are explained below.
The evaluation value calculation part 22 determines a comparison value 27 representing the comparison result (for example, “1” if matching and “0” if mismatch), then weights the comparison value 27 element by element based upon the element weighting information 25, and sums up the comparison values 27 of all the elements in the character string of the processed item to output an evaluation value 28. The weighting process is carried out by multiplying the comparison value 27 of each element by a weighting factor of the weighting information 25. The evaluation value calculation part 22 calculates the evaluation value 28 by applying corresponding matching information and a conceptual evaluation function to each item subjected to the conceptual-structure-based comparison.
In step 1 (denoted as “S1” in the figure, which applies to the other steps), the conceptual structure generating part 4 performs a conceptual structure generation process on the character string of each item of a name-identification target record in the name-identification target data 2B, each item being evaluated by a conceptual evaluation function, and generates a name-identification target conceptual structure 10B. The particulars of the conceptual structure generation process are described below.
In step 44, the element comparison part 21 specifies the conceptual code of the element of the name-identification source conceptual structure 10A and the conceptual code of the element of the name-identification target conceptual structure 10B, which elements correspond to the currently processed element selected in the previous step, to compare the conceptual codes of the elements and determine the comparison value 27 representing the comparison result. The comparison value 27 is, for example, “1” if the conceptual codes under the comparison match, and “0” if mismatched.
In the example described below, it is assumed that there is one name-identification source record and one name-identification target record, and that both of the records include “ID”, “FULL_NAME”, “ADDRESS” and “OFFICE”.
First, the conceptual structure generation process is explained using the character string of the item of “OFFICE” by way of example. The character strings of the “OFFICE” of the name-identification source record and the name-identification target record are presented below.
Name-identification Source:
“Fujitsu Limited, Middleware Division, Data Management and Middleware Department, Second Development Group, Shin-Yokohama Building”
Name-identification Target:
“FUJITSU SY DMM)3 DEVELOPMENT”
In the conceptual structure generating part 4, the following operations are performed. Namely, the morphological analysis part 12 acquires a character string of the “OFFICE” item of the name-identification target record, and generates a morphological analysis result 19 of this character string, which is divided into individual words as illustrated in FIG. 5 (step 14 of FIG. 14), with reference to the terminology dictionary 15 illustrated in FIG. 4.
In addition, the syntactic parsing part 13 converts the morphological analysis result 19 generated from the character string of the “OFFICE” item of the name-identification target record into a conceptual structure, with reference to the analytical grammar 16 illustrated in FIG. 6 (step 15 of FIG. 14). The conversion is carried out according to the following procedure.
(1) According to analytical grammar <1>, a word “Fujitsu” having a semantic attribute [COMPANY NAME] is selected as the main axis node <COMMON>.
(2) According to analytical grammar <3>, the word “Fujitsu” having a semantic attribute [COMPANY NAME] and the word “SY” having a semantic attribute [BUILDING NAME] are associated with each other via the concept <BUSINESS OFFICE>.
(3) According to analytical grammar <5>, the word “)” having a semantic attribute [SEPARATOR] is absorbed in the word “DMM” having a semantic attribute [ORGANIZATION NAME].
(4) According to analytical grammar <4>, the word “Fujitsu” having a semantic attribute [COMPANY NAME] and the word “DMM” having a semantic attribute [ORGANIZATION NAME] are associated with each other via the concept <BUSINESS SEGMENT>.
(5) According to analytical grammar <6>, the word “3” having a semantic attribute [digit] is absorbed in the word “Development” having a semantic attribute of [ORGANIZATION NAME] into a single element, and the single element is converted to the conceptual code “3Develop”.
(6) According to analytical grammar <4>, the word “Fujitsu” having a semantic attribute [COMPANY NAME] and the word “Development” having a semantic attribute [ORGANIZATION NAME] are associated with each other via the concept <BUSINESS SEGMENT>.
(7) The conversion to a conceptual structure has been completed for all the words, and the process terminates because there are no more applicable rules.
As a result of the above-described process, the character string of the item “OFFICE” in the name-identification target record is converted to a conceptual structure illustrated in FIG. 7A and FIG. 7B.
Then, the normalization part 14 performs a conceptual structure normalization process on the conceptual structure (step 16 of FIG. 14). The normalization part 14 acquires a conceptual structure illustrated in FIG. 7A, as well as the standard conceptual structure 17 of the “company” domain illustrated in FIG. 8A which corresponds to the item “OFFICE” (step 21 and step 22 of FIG. 15). The normalization part 14 makes a comparison between the conceptual structure generated by the conceptual structure generating part 4 (see FIG. 7A and FIG. 7B) and the standard conceptual structure 17 (see FIG. 8A and FIG. 8B) to determine if the normalization process has been completed (step 23: the first round). In this example, the To-node of the conceptual structure <2> of FIG. 7A ([FUJITSU $COMPANY NAME]-<BUSINESS OFFICE>-[SY_Buil $BUILDING NAME]) is different from the To-node of the standard conceptual structure <2> of FIG. 8A ([FUJITSU $COMPANY NAME]-<BUSINESS OFFICE>-[$BUSINESS OFFICE NAME]), namely, the semantic attributes of the element are different between [$BUILDING NAME] and [$BUSINESS OFFICE NAME]. Accordingly, for this mismatched portion, an applicable rule is selected from the normalization rule 18 illustrated in FIG. 10 (step 24: the first round). In this example, normalization rule <1> is selected as a rule applicable to the conceptual structure <2>. Under the application of the normalization rule <1>, the conceptual structure illustrated in FIG. 7A and FIG. 7B is converted to the conceptual structure illustrated in FIG. 18A and FIG. 18B (step 25 and step 26: the first round) by changing the semantic attribute of the To-node of the conceptual structure <2> from [$BUILDING NAME] to [$BUSINESS OFFICE NAME].
The conceptual structure generating part 4 also performs the conceptual structure generating process on the character string of the “OFFICE” item in the name-identification source record. As a result of the process, a name-identification source conceptual structure 10A illustrated in FIG. 20A and FIG. 20B is generated for the character string of the “OFFICE” item of the name identification source record.
Next, explanation is made of the matching process, especially of the comparison of the conceptual structure generated for the “OFFICE” item. The matching part 5 selects the item “OFFICE” for the current processing, and acquires the name-identification source conceptual structure 10A and the name-identification target conceptual structure 10B (step 31 and step 32 of FIG. 16). The element comparison part 21 that serves as a conceptual evaluation function compares the conceptual codes of the corresponding elements between the name-identification source conceptual structure 10A and the name-identification target conceptual structure 10B to calculate the evaluation value 28 of the “OFFICE” item (step 33 of FIG. 16).
To be more precise, the evaluation value calculation part 22 initializes the evaluation value 28 to zero (step 41 of FIG. 17). Then, the element comparison part 21 sets the evaluation value of the element to “1.0” if the corresponding elements between the name-identification source conceptual structure 10A and the name-identification target conceptual structure 10B agree with each other, and sets the value to “0” if they do not agree with each other. Then, the evaluation value is weighted based upon the application of the element weighting information 25 and added to the evaluation value 28 (steps 42-45). For example, the element [COMPANY NAME] of the name-identification source conceptual structure 10A of FIG. 20A and the element [COMPANY NAME] of the name-identification target conceptual structure 10B of FIG. 19A are compared to each other. These elements are consistent with each other at [FUJITSU]. The weighting factor of element [COMPANY NAME] is 0.6 (see FIG. 12A). Accordingly, the evaluation value of element [COMPANY NAME] becomes 1.0×0.6=0.6. Similarly, element [DEPARTMENT] and element [BUSINESS OFFICE NAME] are also consistent between the source and target conceptual structures 10A and 10B. The evaluation value of element [COMPANY NAME] becomes 1.0×0.2=0.2. The evaluation value of element [BUSINESS OFFICE NAME] becomes 1.0×0.1=0.1. On the other hand, elements [GROUP] are not substantially the same between the name-identification source and the name-identification target, and accordingly, the evaluation value becomes 0×0.1=0. The evaluation value calculation part 22 sums up the evaluation values of the respective elements (0.6+0.2+0.1+0=0.9) and outputs the calculation result as the evaluation value 28 (step 46 and step 47). FIG. 21 illustrates in a table the relationships among element weighting factor, element comparison value 27, weighted comparison value, and evaluation value 28, in association with each element.
The conceptual structure generating part 4 generates and normalizes a conceptual structure for items “FULL_NAME” and “ADDRESS”, respectively, in addition to item “OFFICE”. The matching part 5 calculates an evaluation value 28 for each of the items. The matching part 5 applies a weighting factor to each of the respective evaluation values 28, as illustrated in FIG. 12B, and sums up the weighted evaluation values to output the total evaluation value 11. For example, It is assumed that the character string of item “FULL_NAME” matches semantically, that the character string of item “ADDRESS” matches semantically, and that the evaluation values 28 of the respective items are “FULL_NAME”=1, “ADDRESS”=1 and “OFFICE”=0.9. When the evaluation values of the respective items are weighted based upon the item weighting information illustrated in FIG. 12B, the value of item “FULL_NAME” becomes 1×0.5=0.5, the value of item “ADDRESS” becomes 1×0.3=0.3, the value of item “OFFICE” becomes 0.9×0.2=0.18, and the total evaluation value 11 becomes 0.5+0.3+0.18=0.98. The upper limit of the threshold value defined in the name-identification process definition 9 illustrated in FIG. 2 is 0.72, and the lower limit of the threshold value is 0.26. The total evaluation value 11 exceeds the upper limit of the threshold value. Accordingly, the determination part 6 determines that the name-identification source record and the name-identification target record agree with each other, and outputs the determination result as a name-identification result 3 (step 7 of FIG. 13).
With this name-identification process, for the character strings of items to be compared between the name-identification source record and the name-identification target record, comparison is made between conceptual codes of corresponding elements in conceptual structures generated for the respective character strings, rather than simple comparison between notations of the character strings. The conceptual structure under the comparison is normalized in accordance with a standard conceptual structure given as a semantic structure that compared items are expected to have in common. In this normalization process, each of the conceptual structures under the comparison is modified as to the semantic attribute of each element and connection between elements, while maintaining the entire meaning unchanged, and converted into a structure according to the standard conceptual structure. Consequently, the name-identification source conceptual structure 10A and the name-identification target conceptual structure 10B can specify corresponding elements conforming to the standard conceptual structure. Corresponding elements are elements that have semantically a commonality, in other words, elements that are compared essentially. For example, elements [GROUP] in FIG. 19A and FIG. 20A have a common meaning of “a group which is a business segment of a department of a company”. By comparing these two elements, strict comparison can be made as to the “group which is a business segment of a department of a company”. A conceptual code is a code for identifying a general idea of a word in the real world. By comparing the conceptual codes of the corresponding elements using conceptual structures, an actual difference can be evaluated in the strict comparison between the elements having a semantic commonality. For example, comparing the conceptual code [2Develop] of [GROUP] in FIG. 20A and the conceptual code [3Develop] of [GROUP] in FIG. 19A is synonymous with comparison between “the second Development Group” and “the third Development Group” as to a business group having substantially the same semantic concept, although the notations of the character strings are different from each other. Accordingly, in view of the general idea of the real world that “the groups are different”, but “the groups are close”, and a substantial comparison result can be obtained.
With the comparison process using a conceptual structure, two character strings can be determined as a match when using words with different notations but with substantially the same semantic content, using words having the same notations but with different orders of notations, or using words having substantially the same semantic content but with different notations and different orders of notations. In the above-described example, a comparison is made using conceptual structures between the character string of item “OFFICE” of the name-identification source and the character string of item “OFFICE” of the name-identification target, and a comparison result that the two character strings agree with each other. If the notations of these two character strings are simply compared, the comparison result will become a mismatch.
In the normalization process, if the conceptual structure cannot be brought into agreement with the standard conceptual structure, the process may proceed to the next matching process after the maximum possible processing has been carried out. In the matching process, only those elements matching the standard conceptual structure are compared. With this arrangement, a reasonable comparison result can be obtained even if the character strings under the comparison do not have sufficient information. This arrangement is effective when accepting ambiguity. In the matching process, when one of a comparison pair of elements does not exist, the evaluation value may be set to an intermediate value, such as 0.5, instead of zero, a concept “Assuming that there is information, evaluation is closer rather than different” is introduced.
In step 51, the evaluation value calculation part 22 initializes the evaluation value to zero. The evaluation value calculation part 22 selects a maximum possible value (e.g., “1”) that the evaluation value could take for the currently processed item as a residual highest value.
In step 54, the element comparison part 21 compares the name-identification source conceptual structure 10A and the name-identification target conceptual structure 10B for the selected element to identify a determination value representing the comparison result. The determination value is, for example, “1” when elements agree with each other and “0” when disagree.
FIG. 24 illustrates an actual example of the database searching process. In this example, a search formula 32A to be executed is “SELECT*FROM MEMBERSHIP_TABLE WHERE OFFICE SAME(Fujitsu SY DMM)3 Development”. The “SAME” function in this formula is a function for performing similarity searching on a character string through semantic-content-based comparison using a predetermined threshold value. The search formula 32A designates a search condition to extract a record having a character string of the “OFFICE” item whose semantic content is similar to that of the search string “Fujitsu SY DMM)3development”, from the “MEMBERSHIP_TABLE” of the database 32B. This search formula 32A is interpreted that the search target item is “OFFICE”, the search string is “Fujitsu SY DMM)3Development”, and the search condition is “SAME( )”. In the database 32B, search target conceptual structures 40B are stored in advance for the respective items, corresponding to the item values. For example, for the “OFFICE” item of record ID “103”, a search target conceptual structure 40B that structures a value string “Fujitsu Limited, Middleware Division, Data Management Middleware Department, Second Development Group, Shin-Yokohama Building” into a conceptual structure based upon the standard conceptual structure of the “company name” domain. It is presumed that the conceptual structure generating information 37 and the matching information 38 are substantially the same as the conceptual structure generating information 7 and the matching information 8 of the first embodiment.
Similar to the first embodiment, a search string conceptual structure 40A is generated, which is a conceptual structure of the search string “Fujitsu SY DMM)3 Development” produced based upon the standard conceptual structure of the “company name” domain corresponding to the “OFFICE” item of the search target. The search string conceptual structure 40A and the search target conceptual structure 40B in the database 32B are compared with each other. As a result, the evaluation value 41 becomes 0.9 as in the first embodiment. If the determination threshold 39 has an upper limit of 0.72 and a lower limit of 0.26 as in the determination threshold defined in the name-identification process definition 9, the evaluation value 41 exceeds the upper limit of the determination threshold. Accordingly, the determination part 36 determines that the search string is similar to the character string of the “OFFICE” item of the record of ID “103” (that is, the record of ID “103” matches the search), and outputs the search result 33.
In the third embodiment, an “element” of XML is referred to as a “XML element” and an “attribute” of XML is referred to as an “XML attribute” to distinguish them from the “element” of the grammatical “attribute” and the semantic “attribute”.
FIG. 26 illustrates an actual example of the XML searching process. In this example, a search formula (XQuery FLWR expression formula) 52A to be executed is “for $p in document (“all_members”)//member let $pn :=$p/office/text( ) where same($pn, “Fujitsu SY DMM)3 Development”) return <specific_member>{$p}</specific_member>”. The “same” function in this formula is a function for performing similarity searching on a character string through semantic-content-based comparison using a predetermined threshold value, as in the second embodiment. This search formula 52A designates a search condition to extract, as an XML element “specific_member”, an XML element “member” having an XML subelement “office” whose value string (text) is semantically matching with “Fujitsu SY DMM)3 Development” from the XML “all_members” contained in the XML database 52B. This search formula 52A is interpreted so that the XML element of the search target is “office” which is a subelement of the XML element “member”, the search string is “Fujitsu SY DMM)3 Development”, and the search condition is “same( )”. In the XML database 52B, a search target conceptual structure 60B is stored. The search target conceptual structure 60B is generated by conceptually structuring the character string “Fujitsu Limited, Middleware Division, Data Management Middleware Department, Second Development Group, Shin-Yokohama Bld.”, which is a value string of the XML element “office” as a subelement of XML element “member” having a subelement ID “005”, based upon the standard conceptual structure of the “company name” domain corresponding to the XML element “office”. It is presumed that the conceptual structure generating information 57 and the matching information 58 are substantially the same as the conceptual structure generating information 7 and the matching information 8 of the first embodiment.
Similar to the first embodiment, a search string conceptual structure 60A is compared to the above-descried search target conceptual structure 60B. The search string conceptual structure 60A is generated from the search string “Fujitsu SY DMM)3 Development” based upon the standard conceptual structure of the “company name” domain corresponding to the XML element “office”. As a result of the comparison, the determination part 56 determines that the search string is similar to the value string of the XML element “office”, which is a subelement of “member” having a subelement ID “005”. Thus, the XML element “member” having a subelement of ID “005” matches the search formula 52A, and this XML element “member” is output as a search result 53 of the XML element of “specific_member”.
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