CN110750654A - Knowledge graph acquisition method, device, equipment and medium - Google Patents
Knowledge graph acquisition method, device, equipment and medium Download PDFInfo
- Publication number
- CN110750654A CN110750654A CN201911033354.5A CN201911033354A CN110750654A CN 110750654 A CN110750654 A CN 110750654A CN 201911033354 A CN201911033354 A CN 201911033354A CN 110750654 A CN110750654 A CN 110750654A
- Authority
- CN
- China
- Prior art keywords
- target
- map
- query
- configuration information
- graph
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- 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
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/901—Indexing; Data structures therefor; Storage structures
- G06F16/9024—Graphs; Linked lists
Abstract
The embodiment of the invention discloses a knowledge graph query method, a knowledge graph query device, knowledge graph query equipment and a knowledge graph query medium. The method comprises the following steps: determining a target configuration information table from the candidate configuration information table according to the map scene request information input by the user; determining a map query algorithm and a target database according to the target configuration information table; and inquiring the atlas in the target database according to the atlas inquiry parameters input by the user and the atlas inquiry algorithm to obtain the knowledge atlas. According to the embodiment of the invention, the target configuration information table is determined from the candidate configuration information table according to the map scene request information, the knowledge map query is realized according to the map query parameters input by the user and the map query algorithm and the target database determined from the target configuration information table, so that the independent development of a knowledge map query system for each service is avoided, and the cost and the implementation period for constructing the knowledge map query system are reduced.
Description
Technical Field
The embodiment of the invention relates to the technical field of knowledge graphs, in particular to a knowledge graph obtaining method, a knowledge graph obtaining device, knowledge graph obtaining equipment and a knowledge graph obtaining medium.
Background
In mass atlas data storage, business departments usually require that the knowledge atlas is displayed in a visual manner, but different business requirements usually require different display contents or display rules, and the related contents and styles are complicated and changeable, for example, in the visual display of the guaranty relationship atlas, a guaranty party, a guaranty method, a guaranty amount and the like are required to be displayed, and in the visual display of the customer relationship atlas, a customer type, a customer name, a relationship type and the like are required to be displayed.
In traditional application development, separate development is often needed according to different requirements, which not only needs to spend large cost, but also has long project implementation period and low code reuse rate.
Disclosure of Invention
The embodiment of the invention provides a knowledge graph query method, a knowledge graph query device, knowledge graph query equipment and a knowledge graph query medium, and aims to solve the problems of high development cost and long implementation period caused by the fact that a knowledge graph query system is separately developed for each service in the prior art.
In a first aspect, an embodiment of the present invention provides a method for querying a knowledge graph, where the method includes:
determining a target configuration information table from the candidate configuration information table according to the map scene request information input by the user;
determining a map query algorithm and a target database according to the target configuration information table;
and inquiring the atlas in the target database according to the atlas inquiry parameters input by the user and the atlas inquiry algorithm to obtain the knowledge atlas.
In a second aspect, an embodiment of the present invention provides a knowledge-graph query apparatus, where the apparatus includes:
the target configuration information table determining module is used for determining a target configuration information table from the candidate configuration information tables according to the map scene request information input by the user;
the algorithm and database acquisition module is used for determining a map query algorithm and a target database according to the target configuration information table;
and the map query module is used for performing map query in the target database according to a map query parameter input by a user and the map query algorithm to obtain a knowledge map.
In a third aspect, an embodiment of the present invention provides an apparatus, where the apparatus includes:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a method of knowledge-graph querying as described in any of the embodiments of the invention.
In a fourth aspect, an embodiment of the present invention provides a computer-readable medium, on which a computer program is stored, where the computer program is used to implement a method for querying a knowledge-graph according to any one of the embodiments of the present invention when executed by a processor.
According to the embodiment of the invention, the target configuration information table is determined from the candidate configuration information table according to the map scene request information, the knowledge map query is realized according to the map query parameters input by the user and the map query algorithm and the target database determined from the target configuration information table, so that the independent development of a knowledge map query system for each service is avoided, and the cost and the implementation period for constructing the knowledge map query system are reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart of a method for querying a knowledge-graph according to an embodiment of the present invention;
FIG. 2 is a flowchart of a knowledge-graph query method according to a second embodiment of the present invention;
FIG. 3 is a flowchart of a knowledge-graph query method according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a knowledge-graph query apparatus according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an apparatus according to a fifth embodiment of the present invention.
Detailed Description
The embodiments of the present invention will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the embodiments of the invention and that no limitation of the invention is intended. It should be further noted that, for convenience of description, only the structures related to the embodiments of the present invention are shown in the drawings, not all the structures.
Example one
Fig. 1 is a flowchart of a knowledge graph query method according to an embodiment of the present invention. The embodiment is suitable for querying the knowledge graph, and can be executed by the knowledge graph querying device provided by the embodiment of the invention, and the knowledge graph querying device can be implemented by software and/or hardware. Referring to fig. 1, the method specifically includes:
step 101, according to the map scene request information input by the user, determining a target configuration information table from the candidate configuration information tables.
The configuration information table is obtained by a developer configuring information for each service requirement in a configuration library by including a Cypher language according to each service requirement, especially a popular service requirement, such as a customer relationship, an account fund transaction relationship, a loan relationship, an investment relationship and the like in the financial field, and finally obtaining a configuration information table corresponding to each service requirement, wherein information included in the configuration information table is used for constructing a knowledge graph corresponding to the service requirement, and exemplarily, the configuration information table includes but is not limited to the following information: the method comprises the steps of map name, map identification, a target database, a map query algorithm, an extended map algorithm, a node detail algorithm, a relation detail algorithm, candidate relation labels, candidate node labels, search condition configuration information, detail event query logic information, a Chinese and English comparison table and the like.
Specifically, the user inputs map scene request information in a search condition input area of the established knowledge map query system, and the input mode may include selecting one of the map scenes recommended by the system, or manually inputting the map scene request information, and after the knowledge map query system acquires the map scene request information input by the user, determining a target configuration information table from a candidate configuration information table in the configuration library according to the information.
Optionally, step 101 includes:
A. acquiring map scene request information input by a user, and matching the map scene request information with map identifications in a candidate configuration information table.
The map identification exists uniquely, namely each configuration information table corresponds to a unique map identification, and the corresponding configuration information table can be determined according to the map identification. For example, a configuration information table of customer relations, whose atlas is identified as custom relationship.
Optionally, the matching method includes, but is not limited to, keyword matching or word vector matching, and the like, and the embodiment of the present invention is not particularly limited.
B. And taking the candidate configuration information table with the same map mark as the map scene request information as a target configuration information table.
Specifically, the candidate configuration information table corresponding to the map identifier having the same matching result as the map scene request information is used as the target configuration information table, and it can be determined that if there is a map identifier having the same matching result as the map scene request information, there is only one map identifier, so that after the map identifier having the same matching result as the map scene request information is determined, the matching is immediately stopped, so as to accelerate the speed of querying the knowledge map.
The target configuration information table is determined from the candidate configuration information table according to the map scene request information input by the user, and a foundation is laid for subsequently determining a map query algorithm and a target database corresponding to the map scene request information.
And step 102, determining a map query algorithm and a target database according to the target configuration information table.
The map query algorithm is the core of knowledge map construction, and is used for querying source data in a database according to a certain parameter to determine nodes related to the parameter and the relationship between each node and the parameter, namely to determine points and edges in the knowledge map, and because the map query algorithms used by different service requirements are different, the associated map query algorithms in different configuration information tables are also different. Most of the existing databases are in distributed storage, and source data with different service requirements are stored in different databases under normal conditions, so that a target database is determined according to a target configuration information table corresponding to map scene request information.
Specifically, the associated map query algorithm and the target database are called in the target configuration information table as the map query algorithm and the target database corresponding to the map scene request information.
By determining the map query algorithm and the target database according to the target configuration information table, a foundation is laid for obtaining the knowledge map subsequently.
And 103, carrying out atlas query in the target database according to the atlas query parameters input by the user and the atlas query algorithm to obtain a knowledge atlas.
Specifically, search condition configuration information is obtained from a target configuration information table, a search condition input area of the knowledge graph query system is rendered according to the search condition configuration information, the rendering is used for identifying graph query parameters which are input by a user and matched with the search condition configuration information, and error reporting is carried out on unmatched graph query parameters, for example, the search condition configuration information comprises a client name and a client number, and when the graph query parameters input by the user are the client name or the client number, the graph query parameters are obtained and the next operation is executed; when the map query parameter input by the user is not the customer name or the customer number, an error is reported to remind the user that the system does not support querying the currently input map query parameter. After the map query parameters input by the user are obtained, the map query parameters are brought into the map query algorithm determined in S102, and map query is performed in the target database determined in S102 to obtain the knowledge map.
Optionally, S103 includes:
A. and carrying out format verification on the map query parameters.
Specifically, the format of the map query parameter is required to correspond to the format that can be identified by the map query algorithm, and if the map query parameter is not verified, the knowledge map query system reports an error to the user to remind the user to input the map query parameter with the correct format.
B. And determining parameter filling areas in the map query algorithm.
Optionally, the target parameter names in the graph query algorithm are determined through the regular expression, and the areas occupied by the target parameter names are used as parameter filling areas in the graph query algorithm.
C. And filling the map query parameters passing the format verification into the parameter filling area, and performing map query in the target database according to the filled map query algorithm.
Specifically, the target parameter name in the graph query algorithm is replaced by the graph query parameter after the format verification, and the graph query parameter is correspondingly filled in a parameter filling area, for example, the target parameter name in the graph query algorithm is the user name, the occupied parameter filling area is an area a, and the graph query parameter is Zhang III, then the user name is replaced by the Zhang III, and the user name is written in the area a. According to the filled-in atlas query algorithm, atlas query is carried out in a target database to obtain a knowledge atlas, wherein the knowledge atlas consists of entity nodes associated with atlas query parameters and the relationship between each entity node and the atlas query parameters, namely points and edges of the knowledge atlas, for example, the atlas query parameters are 'Zhang III', the 'Li IV', the 'Wan V' and the 'Zhao VI' are entity nodes associated with 'Zhang III', the relationship between the 'Li IV' and the 'Zhang III' is 'loan relationship', the relationship between the 'Wan V' and the 'Zhang III' is 'guarantee relationship' and the relationship between the 'Zhang III' and the 'investment relationship'. Optionally, the entity nodes in the knowledge graph and the relationship between each entity node and the graph query parameter are stored in a form of a list.
And inquiring the atlas in the target database according to the atlas inquiry parameters input by the user and the atlas inquiry algorithm to obtain the knowledge atlas, thereby realizing the knowledge atlas inquiry of different service requirements.
According to the technical scheme provided by the embodiment of the invention, the target configuration information table is determined from the candidate configuration information table according to the map scene request information, the knowledge map query is realized according to the map query parameters input by the user, the map query algorithm and the target database determined from the target configuration information table, and the target configuration information table is determined from the candidate configuration information table to determine the relevant information for map query, so that the knowledge map query of different services can be realized based on one system, the independent development of a knowledge map query system for each service is avoided, and the cost and the implementation period for constructing the knowledge map query system are reduced.
On the basis of the above embodiment, after S103, the method further includes:
rendering the knowledge graph to visualize the knowledge graph.
Specifically, the visualized knowledge graph is displayed in a graph display area of the knowledge graph query system.
Through visualization of the knowledge graph, a user can observe the framework of the knowledge graph more visually, and user experience is enhanced.
On the basis of the above embodiment, after S103, the method further includes:
responding to a graph expansion request of a user acting on a target node in a knowledge graph, and performing graph expansion on the target node in the target database according to the identification of the target node and an expanded graph algorithm in the target configuration information table.
The graph expansion refers to the construction of a graph for one or more nodes in the graph on the basis of a knowledge graph obtained through a graph query algorithm so as to obtain a double-layer or even multi-layer knowledge graph.
Specifically, a user selects a certain target node in a visualized knowledge graph, an interactive interface of a knowledge graph query system displays an option of an 'expansion graph', when the user clicks the 'expansion graph', a graph expansion request is generated, the knowledge graph query system responds to the graph expansion request of the user acting on the target node in the knowledge graph, an expansion graph algorithm is determined from a target configuration information table, an ID corresponding to the target node is filled in a parameter filling area of the expansion graph algorithm, graph expansion is performed in a target database according to the filled expansion graph algorithm, and then entity nodes associated with the ID of the target node and the relation between each entity node and the ID of the target node are obtained.
Optionally, after the extended map is obtained, rendering the knowledge map to visualize the extended map, and displaying the visualized extended map in a map display area of the knowledge map query system.
By carrying out map expansion on target nodes in the knowledge map, the expanded knowledge map contains more map information, and the requirement of a user for obtaining more map information can be further met.
Example two
Fig. 2 is a flowchart of a knowledge-graph query method according to a second embodiment of the present invention. The present embodiment provides a specific implementation manner for the above-described embodiments. The method specifically comprises the following steps:
s201, according to the map scene request information input by the user, determining a target configuration information table from the candidate configuration information tables.
S202, determining a map query algorithm and a target database according to the target configuration information table.
S203, according to the atlas query parameters input by the user and the atlas query algorithm, carrying out atlas query in the target database to obtain a knowledge atlas.
S204, acquiring node information of each node in the knowledge graph, and matching the node information with candidate node labels in the target configuration information table.
Each configuration information table is associated with a plurality of candidate node tags for summarizing and summarizing nodes in the knowledge graph, so that a user can better acquire desired information from the knowledge graph, the candidate node tags are determined by relevant technicians according to service requirements corresponding to the configuration information tables, for example, the service requirements are customer relationships, and the associated candidate node tags in the configuration information tables corresponding to the customer relationships may include: customer number, customer name, certificate number, customer address and account number, etc.
Specifically, the node information of each node and the candidate node labels are matched according to the arrangement sequence of the candidate node labels, and the matching method may include a modeling method, for example, a label model is established in advance, the label model is obtained by training sample node information and artificially labeled node labels, the node information of each node is input into the label model, a corresponding node label is output, and then the output node label is matched with the candidate node label.
S205, adding the matched candidate node labels to the nodes in the knowledge graph.
Illustratively, for example, if the node information "zhang san" of the node a matches the candidate label "customer name", the "customer name" is taken as the node label of the node a; for another example, if the node information of node B is "0001" and the candidate label "client number" matches, the "client number" is used as the node label of node B.
Optionally, after adding the node labels to the nodes, rendering the knowledge graph with the node labels to obtain a visualized knowledge graph, and displaying the visualized knowledge graph in the graph display area.
S206, obtaining candidate attribute values associated with the relations according to the relations between the nodes in the knowledge graph and the graph query parameters.
The relationship between each node and the map query parameter is associated with a plurality of candidate attribute values, for example, the map query parameter is Zhangthree, the relationship between the node Liquan and Zhangthree is a guarantee relationship, and under the guarantee relationship, fifty-ten-thousand yuan of guarantee amount, two years of guarantee period, one-ten-thousand yuan of guarantee deposit and a waiting and selecting attribute value of a guarantee agency company A are associated; for another example, if the map query parameter is "wang wu", and the relationship between the nodes "zhao xi" and "wang wu" is a loan relationship, then the loan relationship is associated with "loan amount ten thousand yuan", "loan currency rmb", "loan term one year", "interest annual rate 20%", and "default gold five thousand yuan" to wait for selection of attribute values.
S207, taking the candidate attribute value matched with the candidate relation label in the target configuration information table as the relation label of the relation.
Wherein, the candidate relation label is determined by the related technicians according to the attribute concerned by the user in different relations, for example, in the guarantee relation, if the user cares about the guarantee amount, the 'guarantee amount' is taken as the candidate label; for another example, in the investment relationship, if the user cares about the investment amount, "investment amount" is used as the candidate tag.
Specifically, the candidate relationship labels in the target configuration information table are matched with the candidate attribute values of the relationships in the knowledge graph, and the candidate attribute values successfully matched are used as the relationship labels of the corresponding relationships of the candidate attribute values.
Illustratively, for example, if the candidate attribute value "guarantee amount 50 ten thousand" in the relationship a matches the candidate relationship label "guarantee amount", then "guarantee amount 50 ten thousand" is taken as the relationship label of the relationship a; for another example, if the candidate attribute value "annual rate 5%" in the relationship B matches the candidate relationship label "annual rate", the "annual rate 5%" is used as the relationship label of the relationship B.
Optionally, after the relation is added with the relation label, rendering the knowledge graph with the relation label to obtain a visualized knowledge graph, and displaying the visualized knowledge graph in the graph display area.
According to the technical scheme provided by the embodiment of the invention, the node information of each node in the knowledge graph is obtained, and the node information is matched with the candidate node label in the target configuration information table, so that the node label is added to each node; the relation labels are added to the relations by acquiring the candidate attribute values associated with the relations according to the relations between the nodes in the knowledge graph and the graph query parameters and using the candidate attribute values matched with the candidate relation labels in the target configuration information table as the relation labels of the relations, so that the technical effect of conveniently acquiring the required information from the knowledge graph by a user is achieved.
EXAMPLE III
Fig. 3 is a flowchart of a knowledge-graph query method according to a third embodiment of the present invention. The present embodiment provides a specific implementation manner for the above-described embodiments. The method specifically comprises the following steps:
s301, according to the map scene request information input by the user, determining a target configuration information table from the candidate configuration information tables.
S302, determining a map query algorithm and a target database according to the target configuration information table.
S303, according to the map query parameters input by the user and the map query algorithm, map query is carried out in the target database to obtain a knowledge map.
S304, responding to a detail query request of a user acting on a target node in a knowledge graph, and acquiring the detail information of the target node in the target database according to the identification of the target node and a node detail algorithm in the target configuration information table.
The node details include all information that can be obtained from the target database for a node, for example, the node details corresponding to node "zhang san" may include "age", "height", "weight", "academic calendar", "marital status", "income status", and "number of properties", and so on.
Specifically, a user selects a certain target node in a visualized knowledge graph, a detail query request option is displayed in an interactive interface of a knowledge graph query system, the detail query request of the target node is generated after the user clicks the detail query request, the knowledge graph query system responds to the detail query request of the target node in the knowledge graph, a node detail algorithm is determined from a target configuration information table, an ID corresponding to the target node is filled in a parameter filling area of the node detail algorithm, and the detail information of the target node is acquired in a target database according to the filled node detail algorithm.
Optionally, after the detail information of the target node is acquired, performing chinese-english conversion on the acquired detail information of the target node according to a chinese-english comparison table in a target configuration information table, rendering the converted detail information of the target node to obtain visualized detail information, and displaying the visualized detail information in a detail display area.
S305, responding to a detail query request of a user acting on a target relation in a knowledge graph, and acquiring the detail information of the target relation in the target database according to the identification of the target relation and a relation detail algorithm in the target configuration information table.
The relationship details include information such as start point information, end point information, relationship type, and all attribute values of the relationship, for example, the start point is node a, the end point is node B, the relationship type is "lending relationship", and all attribute values of the relationship are "lending amount ten thousand yuan", "lending currency type rmb", "lending term one year", "interest annual rate 20%", and "default gold five thousand yuan".
Specifically, a user selects a certain target relationship in a visualized knowledge graph, a detail query request option is displayed in an interactive interface of a knowledge graph query system, the detail query request of the target relationship is generated after the user clicks the detail query request, the knowledge graph query system responds to the detail query request of the target relationship in the knowledge graph, a relationship detail algorithm is determined from a target configuration information table, a relationship ID corresponding to the target relationship is filled in a parameter filling area of the relationship detail algorithm, and the detail information of the target relationship is acquired in a target database according to the filled relationship detail algorithm.
Optionally, after the detail information of the target relationship is obtained, the detail information of the target relationship is rendered to obtain visualized detail information, and the visualized detail information is displayed in the detail display area.
According to the technical scheme provided by the embodiment of the invention, the detail information of the target node is obtained in the target database by responding to the detail query request of the user acting on the target node in the knowledge graph, and the detail query request of the target relation in the knowledge graph is responded by the user, so that the user can accurately know the information related to each node and relation in the knowledge graph, and the comprehensiveness of the knowledge graph is increased.
On the basis of the above embodiment, after S305, the method further includes:
determining a detail data query algorithm and a detail database from the target configuration information table; and according to the detail information of the target relation and the detail data query algorithm, performing detail data query in the detail database.
The detail data means that all attribute values of the relationship in the relationship detail information are further expanded, and for example, for the relationship a which represents the guarantee relationship, the detail data is inquired in the detail database by the detail data inquiry algorithm to obtain the detail data such as the "guarantee date" and the "guarantee place".
By inquiring detail data in the detail database, the integrity and the reliability of the knowledge graph are improved.
Example four
Fig. 4 is a schematic structural diagram of a knowledge graph query apparatus according to a fourth embodiment of the present invention, which is capable of executing a knowledge graph query method according to any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method. As shown in fig. 4, the apparatus may include:
a target configuration information table determining module 41, configured to determine a target configuration information table from the candidate configuration information tables according to the map scene request information input by the user;
an algorithm and database acquisition module 42, configured to determine a map query algorithm and a target database according to the target configuration information table;
and the map query module 43 is configured to perform map query in the target database according to a map query parameter input by a user and the map query algorithm to obtain a knowledge map.
On the basis of the foregoing embodiment, the target configuration information table determining module 41 is specifically configured to:
acquiring map scene request information input by a user, and matching the map scene request information with map identifications in a candidate configuration information table;
and taking the candidate configuration information table with the same map mark as the map scene request information as a target configuration information table.
On the basis of the foregoing embodiment, the map query module 43 is specifically configured to:
carrying out format verification on the map query parameters;
determining parameter filling areas in the map query algorithm;
and filling the map query parameters passing the format verification into the parameter filling area, and performing map query in the target database according to the filled map query algorithm.
On the basis of the above embodiment, the apparatus further includes a node tag adding module, specifically configured to:
acquiring node information of each node in a knowledge graph, and matching the node information with candidate node labels in the target configuration information table;
and adding the matched candidate node labels to the nodes in the knowledge graph.
On the basis of the above embodiment, the apparatus further includes a relationship tag adding module, specifically configured to:
acquiring candidate attribute values associated with the relations according to the relations between the nodes in the knowledge graph and the graph query parameters;
and taking the candidate attribute value matched with the candidate relation label in the target configuration information table as the relation label of the relation.
On the basis of the above embodiment, the apparatus further includes a spectrum expansion module, specifically configured to:
responding to a graph expansion request of a user acting on a target node in a knowledge graph, and performing graph expansion on the target node in the target database according to the identification of the target node and an expanded graph algorithm in the target configuration information table.
On the basis of the above embodiment, the apparatus further includes a node detail module, specifically configured to:
responding to a detail query request of a user acting on a target node in a knowledge graph, and acquiring the detail information of the target node in the target database according to the identification of the target node and a node detail algorithm in the target configuration information table.
On the basis of the above embodiment, the apparatus further includes a relationship detail module, specifically configured to:
responding to a detail query request of a user acting on a target relation in a knowledge graph, and acquiring the detail information of the target relation in the target database according to the identification of the target relation and a relation detail algorithm in the target configuration information table.
The knowledge graph query device provided by the embodiment of the invention can execute the knowledge graph query method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to a method for querying a knowledge graph according to any embodiment of the present invention.
EXAMPLE five
Fig. 5 is a schematic structural diagram of an apparatus according to a fifth embodiment of the present invention. Fig. 5 illustrates a block diagram of an exemplary device 500 suitable for use in implementing embodiments of the present invention. The device 500 shown in fig. 5 is only an example and should not bring any limitations to the functionality and scope of use of the embodiments of the present invention.
As shown in fig. 5, device 500 is in the form of a general purpose computing device. The components of device 500 may include, but are not limited to: one or more processors or processing units 501, a system memory 502, and a bus 503 that couples the various system components (including the system memory 502 and the processing unit 501).
The system memory 502 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)504 and/or cache memory 505. The device 500 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 506 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard drive"). Although not shown in FIG. 5, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 503 by one or more data media interfaces. Memory 502 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 508 having a set (at least one) of program modules 507 may be stored, for instance, in memory 502, such program modules 507 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 507 generally perform the functions and/or methodologies of embodiments of the invention as described herein.
The device 500 may also communicate with one or more external devices 509 (e.g., keyboard, pointing device, display 510, etc.), with one or more devices that enable a user to interact with the device 500, and/or with any devices (e.g., network card, modem, etc.) that enable the device 500 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 511. Also, device 500 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet) via network adapter 512. As shown, the network adapter 512 communicates with the other modules of the device 500 over a bus 503. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 501 executes various functional applications and data processing by running a program stored in the system memory 502, for example, implementing a method for querying a knowledge graph provided by an embodiment of the present invention, including:
determining a target configuration information table from the candidate configuration information table according to the map scene request information input by the user;
determining a map query algorithm and a target database according to the target configuration information table;
and inquiring the atlas in the target database according to the atlas inquiry parameters input by the user and the atlas inquiry algorithm to obtain the knowledge atlas.
EXAMPLE six
An embodiment of the present invention further provides a computer-readable storage medium, where the computer-executable instructions, when executed by a computer processor, are configured to perform a method for knowledge-graph query, the method including:
determining a target configuration information table from the candidate configuration information table according to the map scene request information input by the user;
determining a map query algorithm and a target database according to the target configuration information table;
and inquiring the atlas in the target database according to the atlas inquiry parameters input by the user and the atlas inquiry algorithm to obtain the knowledge atlas.
Of course, the storage medium containing the computer-executable instructions provided by the embodiments of the present invention is not limited to the method operations described above, and may also perform related operations in a knowledge-graph query method provided by any embodiments of the present invention. The computer-readable storage media of embodiments of the invention may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (11)
1. A method of knowledge-graph query, the method comprising:
determining a target configuration information table from the candidate configuration information table according to the map scene request information input by the user;
determining a map query algorithm and a target database according to the target configuration information table;
and inquiring the atlas in the target database according to the atlas inquiry parameters input by the user and the atlas inquiry algorithm to obtain the knowledge atlas.
2. The method of claim 1, wherein determining a target configuration information table from the candidate configuration information tables according to the atlas scene request information input by the user comprises:
acquiring map scene request information input by a user, and matching the map scene request information with map identifications in a candidate configuration information table;
and taking the candidate configuration information table with the same map mark as the map scene request information as a target configuration information table.
3. The method of claim 1, wherein performing a graph query in the target database according to a graph query parameter input by a user and the graph query algorithm comprises:
carrying out format verification on the map query parameters;
determining parameter filling areas in the map query algorithm;
and filling the map query parameters passing the format verification into the parameter filling area, and performing map query in the target database according to the filled map query algorithm.
4. The method of claim 1, wherein after obtaining the knowledge-graph, further comprising:
acquiring node information of each node in a knowledge graph, and matching the node information with candidate node labels in the target configuration information table;
and adding the matched candidate node labels to the nodes in the knowledge graph.
5. The method of claim 1, wherein after obtaining the knowledge-graph, further comprising:
acquiring candidate attribute values associated with the relations according to the relations between the nodes in the knowledge graph and the graph query parameters;
and taking the candidate attribute value matched with the candidate relation label in the target configuration information table as the relation label of the relation.
6. The method of claim 1, wherein after obtaining the knowledge-graph, further comprising:
responding to a graph expansion request of a user acting on a target node in a knowledge graph, and performing graph expansion on the target node in the target database according to the identification of the target node and an expanded graph algorithm in the target configuration information table.
7. The method of claim 1, wherein after obtaining the knowledge-graph, further comprising:
responding to a detail query request of a user acting on a target node in a knowledge graph, and acquiring the detail information of the target node in the target database according to the identification of the target node and a node detail algorithm in the target configuration information table.
8. The method of claim 1, wherein after obtaining the knowledge-graph, further comprising:
responding to a detail query request of a user acting on a target relation in a knowledge graph, and acquiring the detail information of the target relation in the target database according to the identification of the target relation and a relation detail algorithm in the target configuration information table.
9. A knowledge-graph query apparatus, the apparatus comprising:
the target configuration information table determining module is used for determining a target configuration information table from the candidate configuration information tables according to the map scene request information input by the user;
the algorithm and database acquisition module is used for determining a map query algorithm and a target database according to the target configuration information table;
and the map query module is used for performing map query in the target database according to a map query parameter input by a user and the map query algorithm to obtain a knowledge map.
10. An apparatus, characterized in that the apparatus further comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of knowledge-graph querying as claimed in any one of claims 1-8.
11. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method of knowledge-graph querying according to any one of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911033354.5A CN110750654A (en) | 2019-10-28 | 2019-10-28 | Knowledge graph acquisition method, device, equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911033354.5A CN110750654A (en) | 2019-10-28 | 2019-10-28 | Knowledge graph acquisition method, device, equipment and medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110750654A true CN110750654A (en) | 2020-02-04 |
Family
ID=69280529
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911033354.5A Pending CN110750654A (en) | 2019-10-28 | 2019-10-28 | Knowledge graph acquisition method, device, equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110750654A (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111339373A (en) * | 2020-02-22 | 2020-06-26 | 苏宁金融科技(南京)有限公司 | Method and system for extracting map features, computer equipment and storage medium |
| CN111383097A (en) * | 2020-03-24 | 2020-07-07 | 中国建设银行股份有限公司 | Method and device for mining suspected personal account |
| CN111666426A (en) * | 2020-06-10 | 2020-09-15 | 北京海致星图科技有限公司 | Method, system and equipment for acquiring knowledge graph multi-scene graph data |
| CN111680150A (en) * | 2020-06-05 | 2020-09-18 | 深圳市铭数信息有限公司 | Information processing method, device, equipment and storage medium |
| CN111782820A (en) * | 2020-06-30 | 2020-10-16 | 京东数字科技控股有限公司 | Knowledge graph creating method and device, readable storage medium and electronic equipment |
| CN111880989A (en) * | 2020-07-14 | 2020-11-03 | 中国银联股份有限公司 | Configuration item management method and device |
| CN111966835A (en) * | 2020-08-26 | 2020-11-20 | 中国银行股份有限公司 | Device and method for analyzing functional service required by scene based on knowledge graph |
| CN112035681A (en) * | 2020-09-17 | 2020-12-04 | 中国银行股份有限公司 | Credit card rate information determination method and device based on knowledge graph |
| CN112380297A (en) * | 2020-12-02 | 2021-02-19 | 福建天创信息科技有限公司 | Method and terminal for generating relation map |
| CN112650858A (en) * | 2020-12-29 | 2021-04-13 | 中国平安人寿保险股份有限公司 | Method and device for acquiring emergency assistance information, computer equipment and medium |
| CN112948593A (en) * | 2021-03-18 | 2021-06-11 | 北京中经惠众科技有限公司 | Knowledge graph generation method, device, equipment and medium |
| CN113806556A (en) * | 2021-09-14 | 2021-12-17 | 广东电网有限责任公司 | Method, device, equipment and medium for constructing knowledge graph based on power grid data |
| CN113901196A (en) * | 2021-09-22 | 2022-01-07 | 南京复保科技有限公司 | Insurance relation map display method and system, computer equipment and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7475018B1 (en) * | 2000-03-16 | 2009-01-06 | Swiss Reinsurance Company | Method for structuring unstructured domains to create value |
| CN103425741A (en) * | 2013-07-16 | 2013-12-04 | 北京中科汇联信息技术有限公司 | Information exhibiting method and device |
| CN108268582A (en) * | 2017-07-14 | 2018-07-10 | 广东神马搜索科技有限公司 | Information query method and device |
| CN109271525A (en) * | 2018-08-08 | 2019-01-25 | 北京百度网讯科技有限公司 | For generating the method, apparatus, equipment and computer readable storage medium of knowledge mapping |
| CN110008355A (en) * | 2019-04-11 | 2019-07-12 | 华北科技学院 | The disaster scene information fusion method and device of knowledge based map |
| CN110263083A (en) * | 2019-06-20 | 2019-09-20 | 北京百度网讯科技有限公司 | Processing method, device, equipment and the medium of knowledge mapping |
-
2019
- 2019-10-28 CN CN201911033354.5A patent/CN110750654A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7475018B1 (en) * | 2000-03-16 | 2009-01-06 | Swiss Reinsurance Company | Method for structuring unstructured domains to create value |
| CN103425741A (en) * | 2013-07-16 | 2013-12-04 | 北京中科汇联信息技术有限公司 | Information exhibiting method and device |
| CN108268582A (en) * | 2017-07-14 | 2018-07-10 | 广东神马搜索科技有限公司 | Information query method and device |
| CN109271525A (en) * | 2018-08-08 | 2019-01-25 | 北京百度网讯科技有限公司 | For generating the method, apparatus, equipment and computer readable storage medium of knowledge mapping |
| CN110008355A (en) * | 2019-04-11 | 2019-07-12 | 华北科技学院 | The disaster scene information fusion method and device of knowledge based map |
| CN110263083A (en) * | 2019-06-20 | 2019-09-20 | 北京百度网讯科技有限公司 | Processing method, device, equipment and the medium of knowledge mapping |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111339373B (en) * | 2020-02-22 | 2023-06-30 | 苏宁金融科技(南京)有限公司 | Atlas feature extraction method, atlas feature extraction system, computer equipment and storage medium |
| CN111339373A (en) * | 2020-02-22 | 2020-06-26 | 苏宁金融科技(南京)有限公司 | Method and system for extracting map features, computer equipment and storage medium |
| CN111383097A (en) * | 2020-03-24 | 2020-07-07 | 中国建设银行股份有限公司 | Method and device for mining suspected personal account |
| CN111383097B (en) * | 2020-03-24 | 2023-08-29 | 中国建设银行股份有限公司 | Method and device for mining personal suspected account |
| CN111680150A (en) * | 2020-06-05 | 2020-09-18 | 深圳市铭数信息有限公司 | Information processing method, device, equipment and storage medium |
| CN111666426A (en) * | 2020-06-10 | 2020-09-15 | 北京海致星图科技有限公司 | Method, system and equipment for acquiring knowledge graph multi-scene graph data |
| CN111782820A (en) * | 2020-06-30 | 2020-10-16 | 京东数字科技控股有限公司 | Knowledge graph creating method and device, readable storage medium and electronic equipment |
| CN111880989A (en) * | 2020-07-14 | 2020-11-03 | 中国银联股份有限公司 | Configuration item management method and device |
| CN111966835A (en) * | 2020-08-26 | 2020-11-20 | 中国银行股份有限公司 | Device and method for analyzing functional service required by scene based on knowledge graph |
| CN112035681A (en) * | 2020-09-17 | 2020-12-04 | 中国银行股份有限公司 | Credit card rate information determination method and device based on knowledge graph |
| CN112035681B (en) * | 2020-09-17 | 2023-10-24 | 中国银行股份有限公司 | Method and device for determining credit card rate information based on knowledge graph |
| CN112380297A (en) * | 2020-12-02 | 2021-02-19 | 福建天创信息科技有限公司 | Method and terminal for generating relation map |
| CN112650858B (en) * | 2020-12-29 | 2023-09-26 | 中国平安人寿保险股份有限公司 | Emergency assistance information acquisition method and device, computer equipment and medium |
| CN112650858A (en) * | 2020-12-29 | 2021-04-13 | 中国平安人寿保险股份有限公司 | Method and device for acquiring emergency assistance information, computer equipment and medium |
| CN112948593A (en) * | 2021-03-18 | 2021-06-11 | 北京中经惠众科技有限公司 | Knowledge graph generation method, device, equipment and medium |
| CN113806556A (en) * | 2021-09-14 | 2021-12-17 | 广东电网有限责任公司 | Method, device, equipment and medium for constructing knowledge graph based on power grid data |
| CN113901196A (en) * | 2021-09-22 | 2022-01-07 | 南京复保科技有限公司 | Insurance relation map display method and system, computer equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110750654A (en) | Knowledge graph acquisition method, device, equipment and medium | |
| US11663375B2 (en) | Configuration of a digital twin for a building or other facility via BIM data extraction and asset register mapping | |
| JP6736173B2 (en) | Method, system, recording medium and computer program for natural language interface to a database | |
| CN111177231A (en) | Report generation method and report generation device | |
| CN104750771B (en) | The method and system of context data analysis is carried out using domain information | |
| CN113946690A (en) | Potential customer mining method and device, electronic equipment and storage medium | |
| CN111813804A (en) | Data query method and device, electronic equipment and storage medium | |
| CN111553556A (en) | Business data analysis method and device, computer equipment and storage medium | |
| CN110941488A (en) | Task processing method, device, equipment and storage medium | |
| US20200175032A1 (en) | Dynamic data visualization from factual statements in text | |
| CN112948396A (en) | Data storage method and device, electronic equipment and storage medium | |
| CN110928928B (en) | Data statistics method and device for investment subject, electronic equipment and storage medium | |
| CN112039975A (en) | Method, device, equipment and storage medium for processing message field | |
| CN112784588A (en) | Method, device, equipment and storage medium for marking text | |
| CN116594683A (en) | Code annotation information generation method, device, equipment and storage medium | |
| CN108304291B (en) | Test input information retrieval apparatus and method | |
| CN111859985B (en) | AI customer service model test method and device, electronic equipment and storage medium | |
| CN112527609B (en) | Early warning information pushing method and device, electronic equipment and storage medium | |
| CN110457705B (en) | Method, device, equipment and storage medium for processing point of interest data | |
| CN114925050A (en) | Data verification method and device based on knowledge base, electronic equipment and storage medium | |
| CN113722550A (en) | Method and device for realizing relation map, electronic equipment and storage medium | |
| CN113127574A (en) | Service data display method, system, equipment and medium based on knowledge graph | |
| CN110750569A (en) | Data extraction method, device, equipment and storage medium | |
| CN111191057A (en) | User-defined retrieval method and device, electronic equipment and storage medium thereof | |
| CN114266170B (en) | Gas supply range identification method and device in gas distribution pipe network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20220922 Address after: 25 Financial Street, Xicheng District, Beijing 100033 Applicant after: CHINA CONSTRUCTION BANK Corp. Address before: 25 Financial Street, Xicheng District, Beijing 100033 Applicant before: CHINA CONSTRUCTION BANK Corp. Applicant before: Jianxin Financial Science and Technology Co.,Ltd. |
|
| TA01 | Transfer of patent application right | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200204 |
|
| RJ01 | Rejection of invention patent application after publication |