CN114385829A

CN114385829A - Knowledge graph creating method, device, equipment and storage medium

Info

Publication number: CN114385829A
Application number: CN202210034006.5A
Authority: CN
Inventors: 金宁
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2022-01-12
Filing date: 2022-01-12
Publication date: 2022-04-22

Abstract

The disclosure provides a knowledge graph creating method, a knowledge graph creating device, knowledge graph creating equipment and a storage medium, and relates to the technical field of artificial intelligence such as natural language processing and knowledge graphs. The specific implementation scheme is as follows: selecting a target knowledge graph construction model from a preset knowledge graph construction model set; receiving target data input by a user; processing the target data by using a target knowledge graph construction model to construct a knowledge graph of the target data; and outputting the knowledge graph. The realization mode can improve the accuracy of knowledge graph establishment.

Description

Knowledge graph creating method, device, equipment and storage medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to the field of artificial intelligence technologies such as natural language processing and knowledge graph creation, and more particularly, to a method, an apparatus, a device, and a storage medium for creating a knowledge graph.

Background

Knowledge is an important foundation for intelligent upgrading of enterprises in social work and industrial production at present. Under the assistance of artificial intelligence, all walks of life are promoting the collection, organization, retrieval and application of knowledge by a brand-new method. Knowledge is an important basis for intelligent upgrading of enterprises. Under the assistance of artificial intelligence, all walks of life are promoting the collection, organization, retrieval and application of knowledge by a brand-new method. When the traditional knowledge graph production tool relates to non-structural data extraction, an industry expert is required to be invested for data structure design, a large amount of marking algorithm manpower is invested for marking optimization, and technical operators are invested for data processing in design graph production, so that the construction efficiency of the knowledge graph is low.

Disclosure of Invention

The disclosure provides a knowledge graph creating method, a knowledge graph creating device, knowledge graph creating equipment and a storage medium.

According to a first aspect, there is provided a knowledge-graph creation method, comprising: selecting a target knowledge graph construction model from a preset knowledge graph construction model set; receiving target data input by a user; processing the target data by using a target knowledge graph construction model to construct a knowledge graph of the target data; and outputting the knowledge graph.

According to a second aspect, there is provided a knowledge-graph creating apparatus comprising: a model determination unit configured to select a target knowledge-graph construction model from a set of preset knowledge-graph construction models; a data receiving unit configured to receive target data input by a user; the map construction unit is configured to process the target data by using the target knowledge map construction model to construct a knowledge map of the target data; a map output unit configured to output a knowledge map.

According to a third aspect, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described in the first aspect.

According to a fourth aspect, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method as described in the first aspect.

According to a fifth aspect, a computer program product comprising a computer program which, when executed by a processor, implements the method as described in the first aspect.

The technology disclosed by the invention can improve the construction efficiency of the knowledge graph.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is an exemplary system architecture diagram in which one embodiment of the present disclosure may be applied;

FIG. 2 is a flow diagram of one embodiment of a knowledge-graph creation method according to the present disclosure;

FIG. 3 is a schematic diagram of one application scenario of a knowledge-graph creation method according to the present disclosure;

FIG. 4 is a flow diagram of another embodiment of a knowledge-graph creation method according to the present disclosure;

FIG. 5 is a schematic block diagram of one embodiment of a knowledge-graph creation apparatus according to the present disclosure;

FIG. 6 is a block diagram of an electronic device for implementing the knowledge-graph creation method of an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

FIG. 1 illustrates an exemplary system architecture 100 to which embodiments of the knowledge-graph creation method or apparatus of the present disclosure may be applied.

As shown in fig. 1, the system architecture 100 may include

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have installed thereon various communication client applications, such as a knowledge-graph creation-type application. The user may import the target data to the

terminal devices

101, 102, 103 and create a knowledge-graph of the target data using the above-described knowledge-graph creation class application.

The

terminal apparatuses

101, 102, and 103 may be hardware or software. When the

terminal devices

101, 102, 103 are hardware, they may be various electronic devices including, but not limited to, smart phones, laptop portable computers, desktop computers, and the like. When the

terminal apparatuses

101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.

The server 105 may be a server providing various services, such as a backend server providing knowledge-graph creation model support on the

terminal devices

101, 102, 103. The backend server may feed back the updated knowledge graph creation model to the

terminal devices

101, 102, 103.

The server 105 may be hardware or software. When the server 105 is hardware, it may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server 105 is software, it may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.

It should be noted that the knowledge graph creation method provided by the embodiments of the present disclosure is generally performed by the

terminal devices

101, 102, and 103. Accordingly, the knowledge-graph creating means is generally provided in the

terminal devices

101, 102, 103.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to FIG. 2, a flow 200 of one embodiment of a knowledge-graph creation method in accordance with the present disclosure is shown. The knowledge graph creating method of the embodiment comprises the following steps:

step 201, selecting a target knowledge graph building model from a preset knowledge graph building model set.

In this embodiment, a knowledge graph building model set may be preset in an execution main body of the knowledge graph creation method. The knowledge graph building model set can comprise a plurality of knowledge graph building models, and different knowledge graph building models correspond to different industries and fields. For example, the set includes models corresponding to the military domain, the communication domain, and the medical domain. It can be understood that models in different fields may have different understandings for the same entity word, so that the accuracy of knowledge graph creation can be improved by accurately applying knowledge graph creation models in corresponding fields. The knowledge map construction model may be any model capable of extracting knowledge and performing map construction based on the extracted knowledge, and may be, for example, a neural network.

The execution subject may select a target knowledge-graph building model from the set of knowledge-graph building models according to the input of the user. The input may include an application scenario or an application domain. Alternatively, the executing agent may select a target knowledge-graph building model from the knowledge-graph building model set according to the current time period. For example, the last week is the stage of deploying the medical domain knowledge-graph construction model, and thus the medical domain knowledge-graph construction model may be selected as the target knowledge-graph construction model.

Step 202, receiving target data input by a user.

The execution body may receive the target data input by the user in various ways. For example, the user may enter the target data by uploading a file. The target data may be a document. The documents may be structured data or unstructured data. Meanwhile, the execution subject may also limit the data amount of the target data. For example, the size of the document is limited to less than 50 Mb. For structured data, 10 thousands of documents can be supported, and for unstructured data, 1 ten thousand documents can be supported. In addition, when the execution main body detects that the access of the target data approaches the critical value, prompt information can be output.

And 203, processing the target data by using the target knowledge graph construction model to construct a knowledge graph of the target data.

The execution subject may process the target data using the target knowledge-graph build model. Processing here includes, but is not limited to: cleaning, desensitization, normalization and knowledge extraction. The executive body can use the processed knowledge to construct a knowledge graph. Specifically, the executive body may use the entities obtained by the knowledge extraction as nodes, and use the relationships between the entities as edges to construct the knowledge graph.

And step 204, outputting the knowledge graph.

After the executive body constructs the knowledge graph, the knowledge graph can be output for follow-up intelligent question answering, knowledge retrieval or special topic gathering.

With continued reference to FIG. 3, a schematic diagram of one application scenario of a knowledge-graph creation method according to the present disclosure is shown. In the application scenario of fig. 3, a technician utilizes specially developed hardware devices for implementing the construction of the knowledge-graph. The hardware device may be integrated with an SSD (Solid State Disk or Solid State Drive) card, an iNIC (i Network Interface Controller) card, a GPU (Graphics Processing Unit), cloud management software, and virtualization software. The hardware equipment can realize calculation, network connection and storage, and can realize the construction of the knowledge graph by combining with a distributed storage engine.

And different hardware may be provided for devices implementing different functions in order to fully utilize the performance of the hardware. In order to facilitate the expansion of hardware performance, different hardware interfaces can be reserved in the hardware, so that the hardware with better performance can be replaced later when needed, or additional hardware can be added. For example, an interface of another SSD card may be reserved in the hardware device for subsequent access to another SSD card.

Technical personnel can generate hardware equipment suitable for different functions in batches through factories, and the hardware equipment is provided for users according to the requirements of the users, so that the condition that the equipment purchase period is long or the equipment cannot be purchased due to independent purchase of the users is avoided. After the user takes the hardware equipment, the user can import the document through the hardware equipment and select a proper target knowledge graph to create the model. The knowledge graph is constructed after the document is processed by the model, and is output and displayed to a user.

For example, the hardware device 1 including the CPU, the memory, the mechanical hard disk, the solid state disk, the RAID card, and the network card retains a GPU card slot for facilitating subsequent expansion of the user, and may be loaded with a map construction model and a map-based knowledge question-answering model, and may also be loaded with an intelligent search model. The knowledge extraction model and the computer program for retraining the model may be loaded in the hardware device 2 in which a video card is additionally provided as compared with the hardware device 1. It is understood that the user can optimize or add various components in the hardware device according to the actual application requirements to load the model or application program of other functions.

The knowledge graph creating method provided by the embodiment of the disclosure can improve the creating efficiency of the knowledge graph.

With continued reference to FIG. 4, a flow 400 of another embodiment of a knowledge-graph creation method in accordance with the present disclosure is shown. As shown in fig. 4, the method of the present embodiment may include the following steps:

step 401, performing an activation operation.

In this embodiment, the execution subject needs to perform an activation operation before constructing the knowledge graph. Specifically, the execution subject may be activated by receiving an activation file input by the user, or by receiving an activation code input by the user. It will be appreciated that upon activation, the user may proceed with the creation of the knowledge-graph using the hardware device as normal. If not, the user cannot use the hardware device normally.

In some optional implementations of this embodiment, the execution subject may perform the activation operation by: outputting a preset guide page; acquiring configuration information input by a user through a guide page; and performing activation operation according to the configuration information.

In this implementation, after the execution main body is powered on, the preset guidance page may be output first. The user can operate the above-mentioned guide page. The guide page may include text, pictures, or animations that instruct the user to perform an operation. The user can input the configuration information through the above-mentioned guide page. The configuration information may be a document, or may be a sequence combination including configuration information of different aspects. If the document is the document, the execution main body can directly analyze the document, determine various required configuration information and activate the configuration information. In the case of sequence combination, the execution body may configure the input sequence in turn. The execution body may perform an activation operation according to the configuration information. Specifically, the execution body may compare the configuration information with a preset activation code, and if the configuration information is the same as the preset activation code, the activation is successful. Alternatively, the execution body may perform other calculations (e.g., hash) on the above configuration information, and if the calculation result satisfies the condition, the activation is successful.

Step 402, selecting a target knowledge graph building model from a preset knowledge graph building model set.

Step 403, receiving target data input by a user;

step 404, normalizing each entity word in the target data; determining the weight of each entity word after normalization according to the source of each entity word in the target data; determining attribute information of each entity word according to the weight of each entity word; and constructing a knowledge graph according to each entity word and the attribute information.

In this embodiment, the execution subject may normalize each entity word in the target data. Here, the normalization is performed to unify only the entity words indicating the same entity into a single entity word. Meanwhile, the execution main body can also determine the weight of each entity word according to the source of each entity word. Specifically, the execution subject may determine the weight of each entity word according to the reliability of the source of each entity word. The higher the reliability, the greater the weight. The lower the reliability, the smaller the weight. After determining the weight of each entity word, attribute information of each entity word can be further determined. Specifically, the execution principal may use the attribute information of the entity word with the largest weight as the final attribute information. And finally, the executive body can take each entity word as a node and each attribute information as a side, and finally a knowledge graph is constructed.

In determining edges, edges may be established according to different priorities. Specifically, if the type and the name are included in the attribute information, the execution subject may preferentially find entity words having the same name and the same type from the mapping dictionary to establish edges. If the attribute information only comprises names and does not comprise types, entity words with the same name are preferentially found from the mapping dictionary, and related entity words are selected according to a preference rule. If the name is not included in the attribute information, the edge relation processing is not performed.

Step 405, outputting the knowledge graph.

In some optional implementations of this embodiment, the method may further include: receiving user-defined incremental data input by a user; and training a target knowledge graph to create a model by using the custom incremental data.

In this implementation, the execution agent may also provide the user with a structured data specification. The user may enter custom delta data according to the above specifications. The custom incremental data may be data generated by a user in practical application, and in order to further improve the accuracy of the model for creating the knowledge graph, the execution subject may train the target knowledge graph to create the model by using the custom incremental data.

In some optional implementations of the present embodiment, the execution main body may be preset with the drilling data inside. The drilling data is used for performing knowledge graph creation drilling. In this way, the user can more quickly understand the working principle of the target knowledge graph creation model and how to input data meeting the specification.

The knowledge graph creating method provided by the embodiment of the disclosure can be more convenient for users to use, allows the users to continuously train the model in the using process, and improves the accuracy of the model.

With further reference to fig. 5, as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a knowledge-graph creating apparatus, which corresponds to the embodiment of the method shown in fig. 2, and which is particularly applicable to various electronic devices.

As shown in fig. 5, the knowledge-graph creating apparatus 500 of the present embodiment includes: a model determination unit 501, a data reception unit 502, a map construction unit 503, and a map output unit 504.

A model determining unit 501 configured to select a target knowledge-graph building model from a set of preset knowledge-graph building models.

A data receiving unit 502 configured to receive target data input by a user.

A map construction unit 503 configured to process the target data using the target knowledge map construction model to construct a knowledge map of the target data.

A map output unit 504 configured to output a knowledge map.

In some optional implementations of this embodiment, the apparatus 500 may further include an activation unit configured to: outputting a preset guide page; acquiring configuration information input by a user through a guide page; and performing activation operation according to the configuration information.

In some optional implementations of this embodiment, the apparatus 500 may further include a drilling unit configured to: and performing knowledge map creation drilling by using drilling data corresponding to the target knowledge map creation model.

In some optional implementations of this embodiment, the apparatus 500 may further include a training unit configured to: receiving user-defined incremental data input by a user; and training a target knowledge graph to create a model by using the custom incremental data.

In some optional implementations of this embodiment, the atlas creation unit 503 may be further configured to: normalizing each entity word in the target data; determining the weight of each entity word after normalization according to the source of each entity word in the target data; determining attribute information of each entity word according to the weight of each entity word; and constructing a knowledge graph according to each entity word and the attribute information.

It should be understood that the units 501 to 504 recited in the knowledge-graph creating apparatus 500 correspond to respective steps in the method described with reference to fig. 2, respectively. Thus, the operations and features described above with respect to the method of knowledge graph creation are equally applicable to the apparatus 500 and the units contained therein and will not be described in detail here.

In the technical scheme of the disclosure, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations and do not violate the good customs of the public order.

The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to an embodiment of the present disclosure.

FIG. 6 shows a block diagram of an electronic device 600 that performs a method of knowledge-graph creation according to an embodiment of the disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 6, the electronic device 600 includes a processor 601 that may perform various suitable actions and processes in accordance with a computer program stored in a Read Only Memory (ROM)602 or a computer program loaded from a memory 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the electronic apparatus 600 can also be stored. The processor 601, the ROM 602, and the RAM603 are connected to each other via a bus 604. An I/O interface (input/output interface) 605 is also connected to the bus 604.

Various components in the electronic device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a memory 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the electronic device 600 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.

Processor 601 may be a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of processor 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. Processor 601 performs the various methods and processes described above, such as the knowledge-graph creation method. For example, in some embodiments, the knowledge-graph creation method may be implemented as a computer software program tangibly embodied in a machine-readable storage medium, such as memory 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into RAM603 and executed by processor 601, one or more steps of the above-described method of knowledge-graph creation may be performed. Alternatively, in other embodiments, processor 601 may be configured to perform the knowledge-graph creation method by any other suitable means (e.g., by way of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. The program code described above may be packaged as a computer program product. These program code or computer program products may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor 601, causes the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable storage medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable storage medium may be a machine-readable signal storage medium or a machine-readable storage medium. A machine-readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions of the present disclosure can be achieved.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A knowledge graph construction method comprises the following steps:

selecting a target knowledge graph construction model from a preset knowledge graph construction model set;

receiving target data input by a user;

processing the target data by using the target knowledge graph construction model to construct a knowledge graph of the target data;

and outputting the knowledge graph.

2. The method of claim 1, wherein the method further comprises:

outputting a preset guide page;

acquiring configuration information input by a user through the guide page;

and performing activation operation according to the configuration information.

3. The method of claim 1, wherein the method further comprises:

and performing knowledge map creation drilling by using drilling data corresponding to the target knowledge map creation model.

4. The method of claim 1, wherein the method further comprises:

receiving user-defined incremental data input by a user;

and training the target knowledge graph to create a model by using the custom incremental data.

5. The method of claim 1, wherein said processing said target data using said target knowledgegraph building model to build a knowledgegraph of said target data comprises:

normalizing each entity word in the target data;

determining the weight of each entity word after normalization according to the source of each entity word in the target data;

determining attribute information of each entity word according to the weight of each entity word;

and constructing a knowledge graph according to each entity word and the attribute information.

6. A knowledge-graph building apparatus comprising:

a model determination unit configured to select a target knowledge-graph construction model from a set of preset knowledge-graph construction models;

a data receiving unit configured to receive target data input by a user;

a graph construction unit configured to process the target data using the target knowledge graph construction model, constructing a knowledge graph of the target data;

a map output unit configured to output the knowledge map.

7. The apparatus of claim 6, wherein the apparatus further comprises an activation unit configured to:

outputting a preset guide page;

acquiring configuration information input by a user through the guide page;

and performing activation operation according to the configuration information.

8. The apparatus of claim 6, wherein the apparatus further comprises a drilling unit configured to:

9. The apparatus of claim 6, wherein the apparatus further comprises a training unit configured to:

receiving user-defined incremental data input by a user;

10. The apparatus of claim 6, wherein the atlas creation unit is further configured to:

normalizing each entity word in the target data;

11. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

12. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-5.

13. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-5.