WO2014207827A1 - Dispositif d'analyse de données, procédé d'expansion de données rdf, et programme d'analyse de données - Google Patents

Dispositif d'analyse de données, procédé d'expansion de données rdf, et programme d'analyse de données Download PDF

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Publication number
WO2014207827A1
WO2014207827A1 PCT/JP2013/067418 JP2013067418W WO2014207827A1 WO 2014207827 A1 WO2014207827 A1 WO 2014207827A1 JP 2013067418 W JP2013067418 W JP 2013067418W WO 2014207827 A1 WO2014207827 A1 WO 2014207827A1
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node
data
sparql
rdf
variable
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PCT/JP2013/067418
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English (en)
Japanese (ja)
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安田　知弘
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株式会社日立製作所
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Priority to PCT/JP2013/067418 priority Critical patent/WO2014207827A1/fr
Priority to JP2015523704A priority patent/JP6001173B2/ja
Publication of WO2014207827A1 publication Critical patent/WO2014207827A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
    • G06F16/367—Ontology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2452—Query translation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2453—Query optimisation
    • G06F16/24534—Query rewriting; Transformation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/36—Creation of semantic tools, e.g. ontology or thesauri

Definitions

  • the present invention relates to a search for RDF data by a SPARQL search query, and particularly relates to a search for RDF data derived from a plurality of information sources.
  • RDF expresses things and their relations as a set of three values (hereinafter referred to as triples) of the thing 1 (hereinafter referred to as S), the kind of relation (hereinafter referred to as P), and the thing 2 (hereinafter referred to as O).
  • triples the thing 1
  • P the kind of relation
  • O the thing 2
  • One triple O can be another triple S
  • S can also be another triple O. Therefore, RDF data is represented by a directed graph.
  • a directed graph is a point connected by a line with a direction.
  • points are called nodes and lines are called edges.
  • nodes and edges are given uniform identifiers (URI), which are identifiers of S and O, and can identify arbitrary things.
  • URI uniform identifiers
  • the URI of the node represents the thing corresponding to the node, and the URI of the edge represents the relationship between the connected things.
  • a directed graph based on RDF data can be constructed by creating an edge with P as a label in the direction from the URI of S to the URI of O.
  • Patent Document 1 discloses a technology for comparing resources (S or O) and assigning the same URI to the resources when both resources are determined to be the same.
  • What is required when handling a large amount of RDF data is a process of searching for information required by the user from a large amount of RDF data and presenting a location that matches the search condition.
  • SPARQL Protocol and RDF Query Language
  • W3C World Wide Web Consortium
  • SPARQL describes a partial structure that satisfies a search condition in an RDF graph structure. It is important to speed up a search query described in SPARQL when utilizing RDF data.
  • RDF Primer http://www.w3.org/TR/rdf-primer/”, “W3C” Recommendation, “10” February, “2004”
  • SPARQL Query Language for RDF http://www.w3.org/TR/rdf-sparql-query/, W3C Recommendation, 15 January, 2008
  • RDF data When searching RDF data, there is a case where it is desired to search by combining RDF data obtained from a plurality of information sources. For example, the name, date of birth and address are recorded in the RDF data obtained from the information source 1, and the name, date of birth and occupation are recorded in the RDF data obtained from the information source 2.
  • the RDF data of each information source 1 and 2 the data whose name and date of birth match are regarded as the same person, and both data are integrated to obtain a list of names, addresses and occupations.
  • RDF data of each information source 1 and 2 are increased, the number of places on the RDF graph where it is necessary to consider whether or not the query matches is increased, and the processing time increases.
  • Patent Document 1 provides a means for searching data stored in RDF (multiple information sources) for what seems to be equal, and integrating such data. .
  • RDF multiple information sources
  • the main problem to be solved by the present invention is that a large amount of RDF data obtained from a plurality of information sources is associated with each other by a search query described in SPARQL, and can be searched at high speed. Is to prevent influence.
  • a data analysis apparatus can execute a SPARQL search query for RDF data provided from a plurality of information sources.
  • a data analysis device for searching comprising: a variable that matches a character string, a numerical value, or a date for associating a node included in a first information source with a node included in a second information source from the SPARQL search query;
  • Query analysis means for extracting a set as a set called a comparison target variable set, and a variable matching the node included in the first information source and the node included in the second information source from the SPARQL search query.
  • Corresponding variable calculation means for selecting each and selecting as a variable called a corresponding variable, and a SPARQL search class input to the processor.
  • the comparison target variable set and the corresponding variable are frequently used, and a string, numerical value, or date value to be matched by the comparison target variable set can be determined in advance.
  • a node adding means for generating a URI composed by combining the character strings sandwiched as a new node, connecting the node matching the corresponding variable and the URI of the new node, and extending the RDF data; Search means for searching a SPARQL search query for the RDF data, wherein the search means searches the expanded RDF data in addition to the SPARQL search query for searching the original RDF data.
  • a SPARQL search query is configured to be searchable.
  • a large amount of given RDF data obtained from a plurality of information sources can be searched as RDF data expanded and associated with each other without affecting the original information sources. So you can search quickly.
  • the flowchart which shows the control logic of the query analysis means of 1st embodiment.
  • FIG. 6 is a diagram for explaining an example of expanded RDF data in which nodes are added to the RDF of FIG. 5 by a node addition unit. The figure explaining an example of medical data based on 2nd embodiment of this invention.
  • the present invention is applicable to improving the performance of a system that uses RDF data obtained based on a plurality of information sources in a transverse manner. Embodiments of the present invention will be described below with reference to the drawings.
  • FIG. 1 is a diagram illustrating a configuration example of a data analysis apparatus 100 according to the first embodiment of the present invention.
  • the data analysis apparatus 100 includes a CPU (Central Processing Unit) 101, a main storage device (memory) 102, an auxiliary storage device 103, a removable medium 104, and a user interface unit 106.
  • the data analysis apparatus 1 is connected to an external network via a network 105 such as a LAN (Local Area Network).
  • the main storage device 102 holds various programs executed by the CP and various data necessary for the CPU 101 to execute these programs.
  • the main storage device 102 is a storage device such as a RAM (Random Access Memory) that stores at least a data analysis program and RDF data 111 (1) that is an input to the data analysis program and is a search target.
  • the CPU 101 executes the data analysis program stored in the main storage device 102, the computer is caused to function as the query analysis means 107, the corresponding variable calculation means 108, the node addition means 109, and the search means 114.
  • the query analysis unit 107, the corresponding variable calculation unit 108, and the node addition unit 109 constitute an extended node addition unit 110 as a whole, and convert the original individual RDF data into expanded RDF data by adding the expansion node.
  • the auxiliary storage device 103 is a storage device such as an HDD capable of recording the RDF data 111 (2) and the like.
  • the removable medium 104 is a recording medium such as a CD-ROM or DVD that can record RDF data 111 (3) or the like. Each data recorded in the auxiliary storage device 103 and the removable medium 104 is read into the main storage device 102 when the data analysis device 1 is started up as necessary.
  • Each RDF data 111 includes a plurality of information sources 113.
  • the user interface unit 106 is an input / output device (for example, a keyboard, a mouse, a display) that provides a user interface.
  • the CPU 101 acquires the RDF data 111 (4) and the like as needed from the outside via the main storage device 102, the auxiliary storage device 103, the removable medium 104, or the network 105. Thereafter, a node for speeding up the search, which will be described later, is added to the acquired RDF data 111 or a search by SPARQL is performed.
  • FIG. 2 is a diagram for briefly explaining an example of the RDF data 111 including the two information sources 113a and 113b.
  • the RDF data 111 obtained from the information source 1 (113a) how the nodes having the identifier of ex1: person1 as the URI and the nodes in which (Alice), (19800101), and (London) are recorded respectively Are connected by an edge that indicates whether the relationship exists (name, date of birth, address).
  • a node having the identifier ex2: customer1 as a URI and each node in which the name, telephone number, date of birth, and occupation are recorded are It is tied indirectly or directly.
  • the two information sources 1 and 2 include, for example, the same name (Alice), there is no edge indicating whether or not they are the same person in the RDF data at this stage.
  • the extended node adding unit 110 of the present invention performs a process of adding a new node and edge when there is a specific relationship between nodes existing in different RDF data.
  • FIG. 3 is a diagram showing an overview of processing for performing query analysis and node addition according to the first embodiment of the present invention
  • FIG. 4 is a diagram showing a processing sequence in which the processing means cooperate.
  • FIG. 5 is an example of RDF including a larger amount of data than the example of FIG.
  • the RDF data 111 is stored in advance as a plurality of information sources in the main storage device 102, the auxiliary storage device 103, or the like. Further, the user inputs a SPARQL search query (hereinafter referred to as a SPARQL query) 400 to the CPU 101 via the user interface unit 106, and based on this, the search means 114 searches for the SPARQL query for the RDF data 111 of a plurality of information sources. The search result is held in the main storage device 102 and also output to the user interface unit 106.
  • a SPARQL search query hereinafter referred to as a SPARQL query
  • the query analysis unit 107 analyzes a query described in SPARQL, and obtains a set of variables that match values to be compared in order to obtain corresponding data from a plurality of information sources (hereinafter, information sources 1 and 2). get.
  • information sources 1 and 2 the data to be associated are nodes such as ex1: person1 and ex2: customer1, and the values to be compared are the name and date of birth.
  • the query analysis means 107 receives a SPARQL query 400 as shown in FIG.
  • the SPARQL query 400 is a query for searching a partial structure matching the graph as shown in FIG. 7 from the RDF data.
  • This graph is hereinafter referred to as a query graph.
  • variables such as “? Target_a” that begin with “?” And continue with alphabets and “_ (underscore)” are variables, such as RDF data strings, numerical values, dates, and other nodes. Can be matched.
  • the name and date of birth are obtained from the graph of the information source 1 on the left of FIGS. 2 and 5, and the name and date of birth are also obtained from the graph of the information source 1 on the right of FIG. Get the day.
  • the query analysis means 107 determines whether each variable of the SPARQL query 400 is a variable to be compared in order to obtain corresponding data from a plurality of information sources by the method shown in FIG. Generate a set of determined items.
  • the variable name to be determined is? X.
  • S801 Determine whether? X matches a character string, numeric value, or date constant. If it matches anything other than a constant, it will not be a comparison target variable.
  • S802 It is determined whether or not a node or edge with a URI that exists only in one information source and? X can be connected by a path using only P existing in this information source. If it cannot be connected,? X is not a comparison variable.
  • S803 It is determined whether or not a node or edge with a URI that exists only in the other information source and? X can be connected by a route using only P existing in that information source.
  • ex1 addr, which exists only in the information source 1, can be reached by P existing in the information source 1.
  • ? birthday1 is compared with? birthday2 in the filter, but? birthday2 is reachable to ex2: workFor, which exists only in information source 2, with P existing in information source 2. Therefore,? Birthday1 is also a comparison target variable.
  • each variable included in the query 400 is a comparison target when associating data from different information sources.
  • a set of variables determined as comparison targets is hereinafter referred to as a comparison target variable set.
  • Corresponding variable calculation means 108 selects one corresponding variable that matches the node from each of the two information sources associated with the comparison target variable set.
  • the user may select the user via the user interface unit 106, the calculation may be automatically performed as follows. First, as the corresponding variables, only the triples of each information source can be used to reach the variables of all the comparison target variables by the path (directed path) that sequentially follows the triples in the direction of S ⁇ O. If there is, it is preferable to select it. For example, in the example of FIG. 7,? Target_a and? Target_b are applicable. If there is no such node, a node having the smallest possible number of triples in the reverse direction (the direction of O ⁇ S) is selected.
  • the variable U is set to the comparison target variable set, and the variable i is set to 1.
  • corresponding variables are selected for each information source.
  • i is a variable that varies from 1 to 2 corresponding to each information source.
  • S902 Let Vi be a set of variables in the query graph connected to P existing in the i-th information source. For example, in the example of FIG. 7,? Addr,? Name,? Birthday,? Target_a connected to ex1: name, ex1: date_of_birth, ex1: addr of the information source 1 are Vi elements.
  • the process proceeds so that? V2 is a candidate for the corresponding variable.
  • Set variable D to infinity ( ⁇ ) as the value.
  • V is the jth variable of Vi.
  • j is a variable that varies from 1 to a value that matches the magnitude of Vi.
  • the variable d is initialized with 0.
  • k is a variable that varies from 1 to a value that matches the size of U.
  • S904 Let? U be the kth variable of U.
  • S905 Find the shortest route from? V to? U, passing only P of information source 1. For example, in the example of FIG. 7, the shortest path from? Addr to? Name is? Addr-? Target_a-? Name. This path is hereinafter referred to as p.
  • a known Dijkstra algorithm (Cormen et al., Introduction to algorithms 3rd edition, the MIT press, pages 658-662) can be used.
  • the score of this path p is calculated by e1 + e2 ⁇ r.
  • r is a parameter given by the user.
  • Node addition means 109 The node adding means 109 adds a node for speeding up the search to two information sources and expands the RDF data in order to efficiently process the frequently used comparison target variable set and speed up the search. To do. For example, as a means for determining “frequently used”, when f is a parameter given by the user, the ratio of queries in which the comparison target variable set in all the SPARQL queries input is used is it is possible to consider a method in which it is determined that a thing of f or more is frequently used.
  • node adding means Details of the node adding means will be described with reference to the control logic of FIG. S1001: First, a comparison target variable set for which a search is to be speeded up by adding a node is selected.
  • S1002 Next, a simplified query in which conditions unnecessary for adding a node are deleted from the SPARQL query used when calculating the comparison target variable set. A method of creating a simplified query will be described later with reference to FIG. S1003: A node is added using a simplified query. This method will be described later with reference to FIG.
  • a variable U is a set of variables to be compared selected in S1001
  • V is a set of corresponding variables calculated by the corresponding variable calculation means 108
  • Q is a SPARQL query used to obtain U and V.
  • the variable i is changed from 1 to 2, and processing is performed for both information sources.
  • S1102 Let Vi be a set of variables connected on the query graph to P existing in the i-th information source. Also,? V is the corresponding variable of the i-th information source. Furthermore, the variable S is initialized to an empty set.
  • S1103 Let? U be the kth variable of U.
  • S1104 As in S905, the shortest path p connecting? V and? U is obtained. By using the same method as S905, the same route as S905 is obtained.
  • FIG. 13 shows an example of the simplified query 500.
  • the simplified query 500 is obtained by erasing unnecessary conditions in the SPARQL query 400 in order to associate a comparison target variable set and a corresponding variable whose search speed is to be increased by extending RDF data.
  • a variable U is a set of variables to be compared selected in S1001
  • V is a set of corresponding variables calculated by the corresponding variable calculation means 108.
  • search processing by the simplified query Q ′ is executed, and the obtained search result is set as B.
  • I and J are parameters given by the user.
  • the variable i is set to 1 and the variable s is set to the character string “_”. Note that i is a variable that changes from 1 to a value equal to the size of B.
  • S1202 Let b be the i-th search result included in B. Also, variables j and k are set to 1 and x is set to an empty character string. S1203: If the variable x is not an empty character string, the character string of the variable s is added to the right end of x. S1204: Let? U be the k-th variable of U. In the search result b, the value associated with the variable? U is added to the right end of x. S1205: The variable k is changed from 1 to a value equal to the size of U, and all comparison target variables are processed. S1206: A new node is created, and the URI of this node is set to I / x /.
  • S1207 When n is the URI of the node to which the jth variable of V is assigned in the search result b, a triple “ ⁇ I / x /> ⁇ J> ⁇ n>.” Is added to the RDF data. However, if this triplet already exists in the RDF data, no addition is performed.
  • S1209 Variable i is changed from 1 to a value equal to the size of B, and all search results are processed.
  • FIG. 14 is an example of expanded RDF data in which a new node is added to the RDF of FIG.
  • the variable representing the comparison target variable set and the corresponding node are output together with the simplified query, and the node of the original RDF data is displayed on the user interface unit as to which node of the original RDF data has been added.
  • the user can discriminate.
  • ex1 person1 of information source 1 which is the original data
  • ex2 customer of information source 2 are compared by comparing the names and birthdays of information sources 1 and 2 in FIG.
  • the generated triple set “ ⁇ I / x /> ⁇ J> ⁇ ” is used instead of describing the comparison target variable set in the SPARQL query.
  • n>. that is, the expanded new node 1001 may be designated as a search condition. This speeds up the search process by the processor.
  • the addition of a new node does not require the integration of the RDF data of the original information sources 1 and 2.
  • the RDF data of the original information sources 1 and 2 remains as they are without any influence. Yes.
  • the search of the information source 1 and the information source 2 by the conventional program is not affected at all. Since only new nodes and edges are added, it is possible to perform a search under a condition that does not include a new node using a conventional SPARQL query by a conventional program by the search unit 114.
  • the conventional program itself is not changed, and it is sufficient to add a function for adding a new node to the expanded RDF data of this embodiment or a function for searching for a new node in the preprocessing of the program. .
  • the extension node Alice Since the original RDF data of the original information sources 1 and 2 can be used as they are, in the example of FIG. 14, even though the name and the birthday coincide, the extension node Alice is not the same person. When a case arises, it is possible to search again using the original RDF data of the original information sources 1 and 2 and generate an appropriate extended node with more accurate information.
  • a large amount of RDF data given from a plurality of information sources is searched as RDF data expanded and associated with each other without affecting the original information sources. Because it is a target, it becomes possible to search faster.
  • FIG. 15 An example of the processing target data is shown in FIG.
  • medical accounting data converted to RDF and electronic medical record data converted to RDF are used across.
  • Medical accounting data can be obtained, for example, from receipt information data submitted by the medical institution to the Ministry of Health, Labor and Welfare (refer to the 2013 “Survey on Impact Assessment of DPC Introduction”).
  • the information source 1 (113a) on the left side of FIG. 15 is an example of the RDF graph 111 derived from medical accounting data, where account: ID is the patient ID, account: admission_date is the date of hospitalization, and account: point is used to calculate medical fees. Represents the number of points used.
  • echart: ID is the patient ID
  • echart: date_admission is the hospitalization date
  • diagnosis represents the diagnosis.
  • a process of obtaining the medical accounting data (113a) score for a case diagnosed as myocardial infarction in the electronic medical record data (113b) will be considered.
  • it is not sufficient to simply collate the medical accounting data with the patient ID of the electronic medical record because there may be a patient who has been discharged and discharged multiple times. It is also necessary to collate the hospitalization date at the same time.
  • FIG. 17 is a diagram illustrating an example of a state in which an additional node is added to medical data according to the second embodiment of this invention.
  • a node 1201 in which the patient ID and the hospitalization date are collected is generated, and is added to the RDF graph to be expanded RDF data.
  • the processing for searching both (113a, 113b) can be accelerated.
  • a patient collated with the expanded new node: 135791_20240608 is a case where the patient ID (135791) and one of multiple hospitalization dates (20240608) match the medical accounting data and the electronic medical record.
  • the electronic medical record can easily obtain the medical accounting data score of a case diagnosed as myocardial infarction.
  • Extended RDF data may be formed with more other medical data.
  • a large amount of RDF data given from a plurality of medical data information sources is searched as RDF data expanded and associated with each other without affecting each information source. Since it can be made a target, the user can search medical data quickly according to the application.
  • the present invention provides a means for automatically rewriting SPARQL of a search query to increase the speed.
  • a program for causing a computer to function as SPARQL automatic rewriting means is stored in the main storage device 102 of the data analysis apparatus 100 described with reference to FIG. 1 regarding the first embodiment.
  • the SPARQL query input by the user using the search unit 114 is automatically rewritten to a condition for a node in which a condition that matches the simplified query is newly added.
  • Other configurations are the same as those of the first embodiment.
  • FIG. 7 is an example of a query graph generated from the SPARQL query 400 of FIG.
  • S1806 If? X is described immediately after select,? X is not processed any more and proceeds to the next variable. This is because the variable described immediately after select is a variable necessary for the output of the SPARQL query q and cannot be replaced.
  • S1807 In g, add all variables directly connected to? X by an edge to S. Furthermore, the triple including? X is deleted from q.
  • S1808 If the filter condition variable of q does not appear in the rewritten q triple, the filter condition is deleted.
  • the automatic rewriting of SPARQL using the expanded RDF data as a search target is automatically processed in response to the user inputting a SPARQL query to the CPU 101 by the search unit 110 in FIG. 4, and based on the result.
  • a search for the RDF data 111 in the main storage device 102 is executed. Therefore, the user can search the extended RDF data at high speed by using the original SPARQL query as it is, that is, without rewriting the extended RDF data into a SPARQL query that is a search target.

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Abstract

Selon l'invention, lors d'une requête de recherche SPARQL, une variable de requête de recherche SPARQL associée à une valeur fréquemment comparée est extraite afin d'associer des données en provenance de multiples sources d'information. Un nouveau nœud créé en liant la valeur associée à cette variable est ajouté aux données RDF. En incluant les données récemment ajoutées en tant que cible de recherche lors de la recherche, la nécessité d'acquérir des valeurs individuelles est éliminée et le processus de recherche est accéléré.
PCT/JP2013/067418 2013-06-25 2013-06-25 Dispositif d'analyse de données, procédé d'expansion de données rdf, et programme d'analyse de données WO2014207827A1 (fr)

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JP2015523704A JP6001173B2 (ja) 2013-06-25 2013-06-25 データ分析装置、ｒｄｆデータの拡張方法、およびデータ分析プログラム

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JP2018532208A (ja) * 2015-10-30 2018-11-01 コンヴィーダ ワイヤレス， エルエルシー セマンティックｉｏｔのためのｒｅｓｔｆｕｌ動作
JP2020060981A (ja) * 2018-10-10 2020-04-16 富士通株式会社 ノード探索方法及びノード探索プログラム

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