Triple-based method and apparatus for constructing patent data knowledge graph

Abstract

The present application relates to a triple-based method and apparatus for constructing a patent data knowledge graph. The method comprises: receiving retrieval information input from the outside; on the basis of the retrieval information and a preset triple model, displaying a target entity and an initial graph corresponding to the target entity, the initial graph comprising at least two classes, and the preset triple model comprising a triple, a first-level parameter and a second-level parameter; on the basis of the first-level parameter input from the outside and the initial graph, adjusting types and display formats of the classes displayed in the graph; on the basis of the first-level parameter input from the outside and the initial graph, adjusting relationship type displays displayed in the graph; and adjusting the number of displays of each class on the basis of the second-level parameter input from the outside. The present application solves the problem that existing knowledge graphs describing an inter-entity relationship lack the establishment of logical relationships between attributes of entities, so that the display of corresponding entities and related information cannot be directly controlled by means of the attributes.

Description

Patent data knowledge graph construction method and device based on triples

[0001] This application is filed with the Patent Office of China on December 29, 2023, with application number 2023118617490 and the name of the invention being “Method and device for constructing a patent data knowledge graph based on triples”, all contents of which are incorporated by reference in this application. Technical Field

[0002] The present application relates to the field of knowledge graph technology. More specifically, the present application relates to a method and apparatus for constructing a triple-based patent data knowledge graph. Background Art

[0003] Structuring patent data primarily involves cleaning and transforming unstructured or semi-structured patent data, such as information about applicants, inventors, and rights holders, to facilitate further storage, query, and analysis. Knowledge graphs are constructed based on this structured data, establishing relationships between entities to form a graphical structure that can support various intelligent queries and analyses.

[0004] Existing patent knowledge graphs, based on patent resource libraries, transform unstructured patent text data into a structured representation in the form of triples of "head entity, relationship, tail entity" or "entity, relationship, entity." This effectively organizes the high-density technical information in patents, enhances the mining of deep semantic relationships between patents, and identifies the technical characteristics and development patterns of patents. Patent knowledge graphs include entities such as applicant information, scientific and technological concepts, and application directions, as well as the interrelationships between entities. Interrelationships between entities typically include relationships between applicant entities, relationships between scientific and technological concept entities, and relationships between application direction entities.

[0005] However, existing knowledge graphs describe the relationships between entities, but lack the establishment of logical relationships between entity attributes, and cannot directly control the display of corresponding entities and related information through attributes. Summary of the Invention

[0006] In response to the above problems, the purpose of this application is to provide a method and device for constructing a patent data knowledge graph based on triples.

[0007] According to the first aspect of the embodiment of the present application, a method for constructing a patent data knowledge graph based on triples is provided, including: receiving externally input retrieval information; displaying a target entity and an initial spectrum corresponding to the target entity based on the retrieval information and a preset triple model, wherein the initial spectrum includes at least two classes, and the preset triple model includes: triples, primary parameters, and secondary parameters; adjusting the types of classes displayed in the spectrum and the display form of the classes based on the externally input primary parameters and the initial spectrum; adjusting the display of the types of relationships displayed in the spectrum based on the externally input primary parameters and the initial spectrum; and adjusting the display quantity of each class based on the externally input secondary parameters.

[0008] Furthermore, the method of adjusting the types of classes and display forms of classes displayed in the atlas according to the externally input primary parameters and the initial atlas includes: the primary parameters include: entity types and entity labels; determining the types to be displayed according to the entity types; adjusting the types of classes displayed in the atlas based on the initial atlas according to the types to be displayed; determining the display forms of the classes to be displayed according to the entity labels; and adjusting the display forms of the classes displayed in the atlas based on the initial atlas according to the display forms of the classes to be displayed.

[0009] Furthermore, the method of adjusting the relationship types displayed in the graph based on the externally input primary parameters and the initial graph includes: the primary parameters include: entity data relationships; determining the types of relationships to be displayed based on the entity data relationships; and adjusting the types of relationships displayed in the graph based on the initial graph based on the types of relationships to be displayed.

[0010] Furthermore, the display quantity of each class is adjusted according to the secondary parameters input externally, including: the secondary parameters include: entity types and corresponding quantities of entity types; determining the types to be displayed according to the entity types; and adjusting the display quantity of the classes displayed in the atlas based on the initial atlas according to the types to be displayed and the corresponding quantities of the entity types.

[0011] According to the second aspect of the embodiment of the present application, a patent data knowledge graph construction device based on triples is provided, which is characterized by including: a data receiving module for receiving externally input retrieval information; a data display module for displaying a target entity and an initial spectrum corresponding to the target entity based on the retrieval information and a preset triple model, wherein the initial spectrum includes at least two classes, and the preset triple model includes: triples, primary parameters and secondary parameters; a primary parameter type adjustment module for adjusting the type of class displayed in the spectrum and the display form of the class according to the externally input primary parameters and the initial spectrum; a primary parameter relationship adjustment module for adjusting the relationship type display of the spectrum according to the externally input primary parameters and the initial spectrum; and a secondary parameter adjustment module for adjusting the display quantity of each class according to the externally input secondary parameters.

[0012] Furthermore, the first-level parameter type adjustment module is specifically used to: the first-level parameters include: entity type and entity label; determine the type to be displayed based on the entity type; adjust the type of class displayed in the graph based on the type to be displayed; determine the display form of the class to be displayed based on the entity label; adjust the display form of the class displayed in the graph based on the initial graph based on the display form of the class to be displayed.

[0013] Furthermore, the first-level parameter relationship adjustment module is specifically used for: the first-level parameters include: entity data relationship; based on the entity data relationship, determining the type of relationship to be displayed; based on the type of relationship to be displayed, adjusting the type of relationship displayed in the graph on the basis of the initial graph.

[0014] Furthermore, the secondary parameter adjustment module is specifically used to: the secondary parameters include: entity types and the corresponding number of entity types; determine the types to be displayed based on the entity types; and adjust the display quantity of the classes displayed in the atlas based on the initial atlas according to the types to be displayed and the corresponding number of entity types.

[0015] According to the third aspect of the embodiment of the present application, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; a processor for reading executable instructions from the memory and executing the instructions to implement a triple-based patent data knowledge graph construction method provided in the first aspect of the present application.

[0016] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which computer program instructions are stored. When the program instructions are executed by a processor, the steps of a method for constructing a patent data knowledge graph based on triples provided in the first aspect of the present application are implemented.

[0017] The technical solutions provided by the embodiments of the present application may have the following beneficial effects:

[0018] This application establishes a logical relationship between entity attributes through a preset triple model, and through the triples, primary parameters and secondary parameters included in the preset triple model, and directly controls the display of the knowledge graph through the entity attributes. Among them, the primary parameters can adjust the type of class displayed in the graph and the display form of the class. First, a class selection adjustment is made to the graph display to obtain the basic framework of the graph display; the type of relationship displayed in the graph display is adjusted, and the graph display relationship is further adjusted to make the graph display more accurate. The secondary parameters can adjust the display quantity of each class and control the amount of graph display, and finally obtain a knowledge graph that meets the requirements, is accurately displayed and has an appropriate display quantity. This application directly controls the entity attributes through a triple model composed of triples, primary parameters and secondary parameters, so as to simply and accurately generate the required knowledge graph. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] FIG1 is a flowchart showing a method for constructing a patent data knowledge graph based on triples according to an exemplary embodiment;

[0021] FIG2 is a retrieval prompt diagram illustrating a method for constructing a patent data knowledge graph based on triples according to an exemplary embodiment;

[0022] FIG3 is a schematic diagram of an initial graph of a method for constructing a patent data knowledge graph based on triples according to an exemplary embodiment;

[0023] FIG4 is a schematic diagram of types and states of graphs to be displayed in a method for constructing a patent data knowledge graph based on triples according to an exemplary embodiment;

[0024] FIG5 is a classification control diagram illustrating a method for constructing a patent data knowledge graph based on triples according to an exemplary embodiment;

[0025] FIG6 is a schematic diagram of a patent control diagram illustrating a method for constructing a patent data knowledge graph based on triples according to an exemplary embodiment;

[0026] FIG7 is a schematic diagram of a classification control diagram of a method for constructing a patent data knowledge graph based on triples according to an exemplary embodiment;

[0027] FIG8 is a schematic diagram of display form control of a method for constructing a patent data knowledge graph based on triples according to an exemplary embodiment;

[0028] FIG9 is a schematic diagram showing basic relationships of a method for constructing a patent data knowledge graph based on triples according to an exemplary embodiment;

[0029] FIG10 is a schematic diagram showing technical relationships of a method for constructing a patent data knowledge graph based on triples according to an exemplary embodiment;

[0030] FIG11 is a schematic diagram of node control of a method for constructing a patent data knowledge graph based on triples according to an exemplary embodiment;

[0031] FIG12 is a schematic diagram of node control of a method for constructing a patent data knowledge graph based on triples according to an exemplary embodiment;

[0032] FIG13 is a schematic diagram showing initial graph type control of a method for constructing a patent data knowledge graph based on triples according to an exemplary embodiment;

[0033] FIG14 is a schematic diagram of a basic framework of graph display control and adjustment of a method for constructing a patent data knowledge graph based on triples according to an exemplary embodiment;

[0034] FIG15 is a schematic diagram showing control and adjustment of a graph display relationship in a method for constructing a patent data knowledge graph based on triples according to an exemplary embodiment;

[0035] FIG16 is a graph showing the final result of a method for constructing a patent data knowledge graph based on triples according to an exemplary embodiment;

[0036] FIG17 is a device diagram showing a method for constructing a patent data knowledge graph based on triples according to an exemplary embodiment. DETAILED DESCRIPTION

[0037] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below in conjunction with the drawings in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this document.

[0038] Knowledge graphs are often represented using the Resource Description Framework (RDF) data model, specifically in the form of SPO triples. Entities represent natural objects or abstract concepts, and relationships model the interactions between entities. The basic storage format is a triple consisting of (head entity h, relationship r, tail entity t). In actual use, once a triple is created, the displayed content can only be changed by modifying the parameters in the triple, and cannot be flexibly adjusted based on customer needs.

[0039] In the embodiment of the present application, on the basis of establishing "entity-relationship-entity" and "entity-attribute-attribute value", logical relationships are established between control entities and attributes through classification, and entities and attributes in triples are uniformly and directly controlled through "category" and "relationship", which is conducive to the control and display of knowledge graphs. Specifically, the embodiment of the present application provides a method for constructing a patent data knowledge graph based on triples, such as exemplary method 1, which includes the following steps, as shown in Figure 1:

[0040] In step S101 , externally input search information is received.

[0041] The search information entered should be entity data, that is, it should be one or more of patents, persons, organizations, dates, addresses and classification numbers, as shown in Figure 2.

[0042] In step S102, the target entity and the initial spectrum corresponding to the target entity are displayed according to the search information and the preset triple model.

[0043] The initial spectrum includes at least two classes, and the preset triple model includes triples, primary parameters, and secondary parameters. For example, if the search input is Patent 1, an initial spectrum consisting of Patent 1 and at least two classes corresponding to Patent 1 is displayed. A basic graph display is obtained, as shown in Figure 3. The entities "smart car key device, smart car, and smart car interaction system" form the initial graph along with the patent, person, organization, and classification number. This displays basic entity information and prepares for subsequent graph display adjustments.

[0044] The initial spectrum graph consists of triplets. The primary and secondary parameters control the correspondence between entity attributes and entities in the triples, establish logical relationships between entity attributes, and directly control the display of the knowledge graph through entity attributes.

[0045] In step S103, the type of classes and the display form of the classes displayed in the atlas are adjusted according to the externally input primary parameters and the initial atlas.

[0046] The first-level parameters include: entity type and entity label.

[0047] The category to be displayed is determined according to the entity category.

[0048] According to the types to be displayed, on the basis of the initial graph, the types of classes displayed in the graph are adjusted.

[0049] The categories to be displayed in the atlas include patents, people, organizations, dates, addresses, and classifications, as shown in Figure 4. The "on" or "off" state of the class is controlled by the first-level parameters input externally, which controls whether the current corresponding class is displayed. Among them, since the classification includes two parts, IPC classification and technical features, it is necessary to control whether these two parts are displayed separately, as shown in Figure 5. For example, when the patent is "off", all the contents in the patent are not displayed, and the image changes from the initial image Figure 3 to Figure 6, that is, the technical problems, technical fields, and technical effects in Figure 3 are not displayed; when the IPC classification is turned on, all the contents in the IPC classification are displayed, and the image changes from the initial image Figure 3 to Figure 7, that is, on the basis of Figure 3, the IPC classification section, major category, minor category, major group, and minor group are added. In this example, the minor group data is missing, so the minor group is not displayed.

[0050] A display format of the class to be displayed is determined according to the entity tag.

[0051] According to the display form of the class to be displayed, on the basis of the initial atlas, the display form of the class displayed in the atlas is adjusted.

[0052] After controlling whether the current corresponding class is displayed, if the current corresponding class status is set to "On", select the display format of the class displayed in the atlas. The display formats of the class displayed in the atlas include:

[0053] The display format of patents: patent, field, problem and effect. Among them, patent represents the name of the patent; field represents the field involved in the patent; problem represents the problem solved by the patent; effect represents the effect achieved by the patent. The display format of organizations: enterprises, schools, hospitals, research institutions, agencies and others. The display format of organizations represents the type of institutions related to the patent. The display format of dates: year, year-month and year-month-day. The display format of dates represents the display format of dates related to patents. The display format of addresses: country, province, city and province-district-city / county. The display format of addresses represents the display format of addresses related to patents. The display format of IPC classifications in classifications: department, major category, minor category, major group and minor group. The display format of IPC classifications in classifications represents the display format of IPC classifications related to patents.

[0054] The display format is selected based on externally input primary parameters. For technical features within characters and categories, there's no specific display format, so only whether to display them is controlled. For example, when a patent is "opened," the effects are turned off, and the image changes from the initial image (Figure 3) to Figure 8. This means the technical effects in Figure 3 are not displayed, while the rest of the patent content is displayed normally.

[0055] In step S104, the relationship type display of the graph is adjusted according to the externally input primary parameters and the initial graph.

[0056] The first-level parameters include: entity data relationship.

[0057] According to the entity data relationship, the type of the relationship to be displayed is determined.

[0058] There are two types of relationships: basic relationships and technical relationships.

[0059] Among them, the basic relationships include: related persons, time relationships, change relationships, address relationships, employee organizations, problem effects and field methods.

[0060] Based on the externally input primary parameters and initial map, select the basic relationships to be displayed.

[0061] Specifically, as shown in Figure 9, related persons include: applicant, inventor, examiner, and agent. Time relations include: application date, authorization date, and publication date. Change relations include: previous right holder, post-change right holder, and current right holder. Address relations include: applicant's address and affiliation address. Employee and organization relations include: company employees and affiliations. Problem and effect relations include: technical problems and technical effects. Field and method relations include: technical fields.

[0062] According to the externally input primary parameters, the initial map and the selected basic relationships to be displayed, the type of content displayed in the corresponding basic relationships is controlled.

[0063] Among them, technical relationships include: reference relationships, group relationships, and feature relationships, as shown in Figure 10.

[0064] Based on the externally input primary parameters and initial map, select the technical relationships that need to be displayed.

[0065] Among them, the reference relationship includes: reference and referenced. The family relationship includes: simple same family and extended same family. The feature relationship includes: technical features.

[0066] In step S105, the display quantity of each category is adjusted according to the secondary parameters input externally.

[0067] Secondary parameters include: entity type and corresponding quantity of entity type.

[0068] The category to be displayed is determined according to the entity category.

[0069] The categories to be displayed include: organization screening, person screening, date, classification number and patent characteristics.

[0070] Based on the corresponding number of categories to be displayed and entity categories, the number of categories displayed in the graph is adjusted based on the initial graph. For example, when the input secondary parameter is organization screening, 5, the number of organization nodes displayed in the patent is adjusted to 5, as shown in Figure 11. The image is changed from the initial image Figure 3 to Figure 12, and the number of nodes in Figure 3 is adjusted based on the number of nodes 5.

[0071] Exemplary Method 2

[0072] In the embodiment of the present application, an initial map is obtained, FIG3 , external input data, and the initial map is adjusted according to the input data.

[0073] The entity types of the external input first-level parameters are date, person, patent and classification, and the entity labels of the external input first-level parameters are year, month, subcategory and major category.

[0074] The entity data relationship of the first-level parameter of external input is inventor and agent.

[0075] The secondary parameter of external input is the number of technical feature nodes, which is 2 nodes.

[0076] According to the entity types of the external input first-level parameters, which are date, person, patent and classification, the display of date, person, patent and classification is controlled, and the rest are adjusted to the "off" state and not displayed, resulting in Figure 13.

[0077] According to the entity labels of the external input first-level parameters as year, month, subcategory and major category, the date is controlled to display the subordinate year and month, and the rest are not displayed. The IPC category in the classification is controlled to display the subordinate subcategory and major category, and the rest are not displayed. Figure 14 is obtained, forming the basic framework of the atlas display.

[0078] According to the entity data relationship of the external input first-level parameter, the inventor and the agent are controlled. The related persons in the basic relationship are displayed as the inventor and the agent, and the others are not displayed, so as to obtain Figure 15, which makes the map display more accurate.

[0079] According to the external input secondary parameter, the number of technical feature nodes is 2 nodes, and the maximum number of nodes is adjusted to obtain the final result image generated in Figure 16.

[0080] Exemplary devices

[0081] In the embodiment of the present application, as shown in FIG17 , it includes a data receiving module 1701 , a data display module 1702 , a first-level parameter adjustment type adjustment module 1703 , a first-level parameter adjustment relationship adjustment module 1704 and a second-level parameter adjustment module 1705 .

[0082] The data receiving module 1701 is used to receive search information input from the outside;

[0083] A data display module 1702 is configured to display a target entity and an initial spectrum corresponding to the target entity based on the search information and a preset triple model, wherein the initial spectrum includes at least two classes, and the preset triple model includes: a triple, a primary parameter, and a secondary parameter;

[0084] The first-level parameter adjustment type adjustment module 1703 is used to adjust the types of classes displayed in the atlas and the display form of the classes according to the first-level parameters input externally and the initial atlas; the first-level parameters include: entity types and entity labels; according to the entity types, the types to be displayed are determined; according to the types to be displayed, the types of classes displayed in the atlas are adjusted on the basis of the initial atlas; according to the entity labels, the display form of the classes to be displayed is determined; according to the display form of the classes to be displayed, the display form of the classes displayed in the atlas is adjusted on the basis of the initial atlas.

[0085] The first-level parameter adjustment relationship adjustment module 1704 is used to adjust the relationship types displayed in the graph according to the first-level parameters input externally and the initial graph; the first-level parameters include: entity data relationship; based on the entity data relationship, determine the type of relationship to be displayed; based on the type of relationship to be displayed, adjust the type of relationship displayed in the graph on the basis of the initial graph.

[0086] Secondary parameter adjustment module 1705 is configured to adjust the display quantity of each class based on externally input secondary parameters. Secondary parameters include: entity types and corresponding quantities of entity types; determining the class to be displayed based on the entity types; and adjusting the display quantity of the class displayed in the graph based on the initial graph based on the class to be displayed and the corresponding quantity of the entity types.

[0087] Exemplary electronic devices

[0088] This embodiment proposes an electronic device, comprising: one or more processors, and an internal memory and an external memory, wherein the internal memory stores instructions. When the instructions are executed by the one or more processors, the one or more processors execute a method for constructing a patent data knowledge graph based on triples as described in any of the aforementioned embodiments.

[0089] The processor is configured to execute all or part of the steps of the triple-based patent data knowledge graph construction method described in the embodiment. The memory is configured to store various types of data, such as instructions for any application or method in the electronic device, as well as application-related data.

[0090] The processor can be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and is used to execute a patent data knowledge graph construction method based on triples described in the embodiment.

[0091] Computer storage media

[0092] The computer-readable storage medium stores a computer program, and when the computer program is executed by one or more processors, it implements a method for constructing a patent data knowledge graph based on triples as described in any of the aforementioned embodiments.

[0093] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0094] In the 1930s, technological improvements could be clearly distinguished as either hardware improvements (for example, improvements to circuit structures like diodes, transistors, and switches) or software improvements (improvements to process flows). However, with the advancement of technology, many process flow improvements today can now be considered direct improvements to hardware circuit structures. Designers almost always create the corresponding hardware circuit structure by programming the improved process flow into the hardware circuit. Therefore, it cannot be said that a process flow improvement cannot be implemented using hardware modules. For example, a programmable logic device (PLD), such as a field programmable gate array (FPGA), is an integrated circuit whose logical function is determined by user programming. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to hire a chip manufacturer to design and manufacture a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly done using "logic compiler" software. This is similar to the software compiler used when developing programs. Before compilation, the original code must also be written in a specific programming language, called a hardware description language (HDL). There is not just one HDL, but many, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art will also understand that by simply programming the method flow in one of these hardware description languages ​​and then programming it into an integrated circuit, a hardware circuit that implements the logic method flow can be easily obtained.

[0095] The controller can be implemented in any suitable manner. For example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also know that in addition to implementing the controller in a purely computer-readable program code format, the controller can be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules that implement the method and structures within the hardware component.

[0096] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0097] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0098] Those skilled in the art will appreciate that one or more embodiments of this specification may be provided as a method, system, or computer program product. Thus, one or more embodiments of this specification may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0099] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0100] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0102] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0103] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0104] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0105] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0106] One or more embodiments of this specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. One or more embodiments of this specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0107] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0108] The foregoing description is merely an example of the present invention and is not intended to limit the present invention. Persons skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be included within the scope of the claims herein.

Claims

1. A method for constructing a patent data knowledge graph based on triples, characterized in that Including: Receiving the retrieved information input externally; According to the retrieved information and a preset triple model, presenting a target entity and an initial spectrum diagram corresponding to the target entity, the initial spectrum diagram including at least two categories, and the preset triple model including: triples, primary parameters, and secondary parameters; According to the primary parameters input externally and the initial spectrum diagram, adjusting the types of categories presented in the spectrum diagram and the presentation forms of the categories; According to the primary parameters input externally and the initial spectrum diagram, adjusting the presentation of the types of relationships presented in the spectrum diagram; According to the secondary parameters input externally, adjusting the number of presentations of each of the categories.

2. The method according to claim 1, wherein The adjusting the types of categories presented in the spectrum diagram and the presentation forms of the categories according to the primary parameters input externally and the initial spectrum diagram includes: The primary parameters include: entity types and entity labels; Determining the types to be presented according to the entity types; Based on the types to be presented, adjusting the types of categories presented in the spectrum diagram on the basis of the initial spectrum diagram; Determining the presentation forms of the categories to be presented according to the entity labels; Based on the presentation forms of the categories to be presented, adjusting the presentation forms of the categories presented in the spectrum diagram on the basis of the initial spectrum diagram.

3. The method according to claim 1, wherein The adjusting the presentation of the types of relationships presented in the spectrum diagram according to the primary parameters input externally and the initial spectrum diagram includes: The primary parameters include: entity data relationships; Determining the types of relationships to be presented according to the entity data relationships; Based on the types of relationships to be presented, adjusting the types of relationships presented in the spectrum diagram on the basis of the initial spectrum diagram.

4. The method according to claim 1, wherein The adjusting the number of presentations of each of the categories according to the secondary parameters input externally includes: The secondary parameters include: entity types and the corresponding quantities of the entity types; Determining the types to be presented according to the entity types; Based on the types to be presented and the corresponding quantities of the entity types, adjusting the number of presentations of the categories presented in the spectrum diagram on the basis of the initial spectrum diagram.

5. A device for constructing a knowledge graph of patent data based on triples, characterized in that, Including: A data receiving module, configured to receive the retrieved information input externally; A data presentation module, configured to present a target entity and an initial spectrum diagram corresponding to the target entity according to the retrieved information and a preset triple model, the initial spectrum diagram including at least two categories, and the preset triple model including: triples, primary parameters, and secondary parameters; A primary parameter category adjustment module, configured to adjust the types of categories presented in the spectrum diagram and the presentation forms of the categories according to the primary parameters input externally and the initial spectrum diagram; A primary parameter relationship adjustment module, configured to adjust the presentation of the types of relationships presented in the spectrum diagram according to the primary parameters input externally and the initial spectrum diagram; A secondary parameter adjustment module, configured to adjust the number of presentations of each of the categories according to the secondary parameters input externally.

6. The device according to claim 5, characterized in that, The primary parameter category adjustment module is specifically configured to: The primary parameters include: entity types and entity labels; Determining the types to be presented according to the entity types; Based on the types to be presented, adjusting the types of categories presented in the spectrum diagram on the basis of the initial spectrum diagram; Determining the presentation forms of the categories to be presented according to the entity labels; Based on the display form of the class to be displayed, adjust the display form of the classes shown in the initial graph.

7. The device according to claim 5, characterized in that, The first-level parameter relationship adjustment module is specifically configured to: The first-level parameters include: entity data relationships; Determine the types of relationships to be displayed according to the entity data relationships; Based on the types of relationships to be displayed, adjust the types of relationships shown in the initial graph.

8. The device according to claim 5, wherein The second-level parameter adjustment module is specifically configured to: The second-level parameters include: entity types and the corresponding quantities of entity types; Determine the types to be displayed according to the entity types; Based on the types to be displayed and the corresponding quantities of the entity types, adjust the display quantities of the classes shown in the initial graph.

9. An electronic device, characterized in that, It includes: A memory for storing computer programs; A processor for implementing the steps of a method for constructing a patent data knowledge graph based on triples as described in any one of claims 1 to 4 when executing the computer program.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of a method for constructing a patent data knowledge graph based on triples as described in any one of claims 1 to 4 are implemented.

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