CN113836314B - Knowledge graph construction method, device, equipment and storage medium - Google Patents
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- 238000013473 artificial intelligence Methods 0.000 abstract description 3
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
- G06F40/295—Named entity recognition
Abstract
The disclosure provides a knowledge graph construction method, a device, equipment and a storage medium, and relates to the technical fields of artificial intelligence such as natural language processing, knowledge graph field and the like. The specific implementation scheme is as follows: acquiring a plurality of specification documents of a target object, wherein the plurality of specification documents are used for representing application specifications aiming at the target object; performing entity identification and structure analysis on characters in each standard document, and determining entities in each standard document, relations among the entities and attribute information of the entities; and constructing a knowledge graph of the target object according to the entity, the relation and the attribute information. The implementation mode can automatically construct the knowledge graph for the standard document, and the construction efficiency of the knowledge graph is improved.
Description
Technical Field
The disclosure relates to the technical field of computers, in particular to the technical field of artificial intelligence such as natural language processing and knowledge graph field, and especially relates to a knowledge graph construction method, a device, equipment and a storage medium.
Background
In the industrial application scene of the knowledge graph, a huge number of documents and standard class specification documents exist, and how to sort the specification documents to generate the knowledge graph for further application is a problem to be solved at present.
Disclosure of Invention
The disclosure provides a knowledge graph construction method, a knowledge graph construction device, knowledge graph construction equipment and a storage medium.
According to a first aspect, there is provided a knowledge graph construction method, including: acquiring a plurality of specification documents of a target object, wherein the plurality of specification documents are used for representing application specifications aiming at the target object; performing entity recognition and sentence pattern analysis on sentences in each standard document, and determining entities in each standard document, relations among the entities and attribute information of the entities; and constructing a knowledge graph of the target object according to the entity, the relation and the attribute information.
According to a second aspect, there is provided a knowledge graph construction apparatus including: a document acquisition unit configured to acquire a plurality of specification documents of a target object, the plurality of specification documents representing application specifications for the target object; the information extraction unit is configured to perform entity recognition and sentence pattern analysis on sentences in each standard document, and determine entities in each standard document, relations among the entities and attribute information of the entities; and the knowledge graph construction unit is configured to construct a knowledge graph of the target object according to the entity, the relation and the attribute information.
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 storing 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.
According to the technology disclosed by the invention, the knowledge graph can be automatically constructed for the standard document, and the construction efficiency of the knowledge graph is improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will be readily appreciated from the following description.
Drawings
The drawings are for 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 an embodiment of the present disclosure may be applied;
FIG. 2 is a flow chart of one embodiment of a knowledge graph construction method in accordance with the present disclosure;
FIG. 3 is a schematic illustration of one application scenario of a knowledge graph construction method according to the present disclosure;
FIG. 4 is a flow chart of another embodiment of a knowledge graph construction method in accordance with the present disclosure;
FIG. 5 is a schematic diagram of a structure of one embodiment of a knowledge graph construction apparatus in accordance with the disclosure;
fig. 6 is a block diagram of an electronic device used to implement a knowledge graph construction method of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one 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, without conflict, the embodiments of the present disclosure and features of the embodiments may be combined with each other. 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 construction method or knowledge graph construction apparatus of the present disclosure may be applied.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a search class application, a browser class application, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 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, smartphones, tablet computers, electronic book readers, car-mounted computers, laptop and desktop computers, and the like. When the terminal devices 101, 102, 103 are software, they can be installed in the above-listed electronic devices. Which may be implemented as multiple software or software modules (e.g., to provide distributed services), or as a single software or software module. The present invention is not particularly limited herein.
The server 105 may be a server providing various services, such as a background server responding to requests sent by the terminal devices 101, 102, 103. The background server may construct a knowledge graph in advance using a plurality of specification documents, respond to a request of the terminal devices 101, 102, 103 using the knowledge graph, and feed back response information 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 formed by a plurality of servers, or as a single server. When server 105 is software, it may be implemented as a plurality of software or software modules (e.g., to provide distributed services), or as a single software or software module. The present invention is not particularly limited herein.
It should be noted that, the knowledge graph construction method provided in the embodiment of the disclosure is generally executed by the server 105. Accordingly, the knowledge graph construction apparatus is generally provided in the server 105.
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 construction method in accordance with the present disclosure is shown. The knowledge graph construction method of the embodiment comprises the following steps:
In step 201, a plurality of canonical documents of the target object are obtained.
In this embodiment, the execution body of the knowledge graph construction method may first acquire a plurality of specification documents of the target object. Here, the target object may be an object applied in various daily lives, such as a transformer, a switch, and the like. The plurality of specification documents may be documents written in accordance with a specification such as an execution standard of a transformer, a revised record of law and regulation, a management regulation of civil aviation, and the like. These documents all have certain writing criteria, for example, for the implementation criteria of transformers, the introductory part of which is usually written with the reasons for making the criteria, standard drafting units, drafting persons, etc. A plurality of specification documents are used to represent application specifications for the target object.
Step 202, entity recognition and structure analysis are carried out on characters in each standard document, and the entity, the relation among the entities and the attribute information of the entity in each standard document are determined.
In this embodiment, since each specification document has a certain composition specification, the execution body may determine the entities in each specification document by extracting the rules for the entities corresponding to these composition specifications. Meanwhile, in order to improve the accuracy of entity extraction, the execution body can also perform entity recognition on the characters in each standard document so as to determine more entities. After determining the entities, the execution body can also analyze the dependency relationship of the characters in each canonical document to determine the relationship among the entities. The execution subject may also determine attribute information of the entity. The attribute information may include, for example, standard validation time, expiration date, and the like.
And 203, constructing a knowledge graph of the target object according to the entity, the relation and the attribute information.
After determining the entity, the relationship among the entities and the attribute information of the entities, the execution subject can construct a knowledge graph of the target object. Specifically, the execution body may use the entities as nodes and the relationships between the entities as edges between the nodes to construct a knowledge graph of the target object. And meanwhile, the attribute information of the entity is used as the attribute value of the node.
With continued reference to fig. 3, a schematic diagram of one application scenario of the knowledge graph construction method according to the present disclosure is shown. In the application scenario of fig. 3, the server 301 acquires a plurality of standard documents of the transformer, determines entities in each standard document, relationships among the entities, and attribute information of the entities, and constructs a knowledge graph of the transformer.
The knowledge graph construction method provided by the embodiment of the disclosure can automatically construct the knowledge graph for the standard document, and improves the construction efficiency of the knowledge graph.
With continued reference to fig. 4, a flow 400 of another embodiment of a knowledge graph construction method in accordance with the present disclosure is shown. As shown in fig. 4, the method of the present embodiment may include the steps of:
In step 401, a plurality of canonical documents of the target object are obtained.
Step 402, keyword matching is carried out on each standard document, and target sentences in each standard document are determined; and determining the paragraph in which the target sentence is positioned as a target paragraph.
In this embodiment, after the execution body obtains each canonical document, the execution body may first perform keyword matching on each canonical document, and determine a sentence in which the keyword in each canonical document is located, which is called a target sentence. The execution body may then take the paragraph in which the target statement is located as the target paragraph.
After the target paragraph is determined, entity recognition and other processing can be performed on the characters in the target paragraph, so as to determine the entity in the target paragraph. And performing structural analysis and the like on the target paragraphs to determine the relationship between the entities and the attribute information of the entities.
Step 403, searching preset language description in each specification document, and determining a history entity, a replacement entity of the history entity and a relation between the history entity and the replacement entity; performing entity identification on each specification document, and determining a preset type entity in each specification document; determining an associated entity of the preset type and a relation between the entity of the preset type and the associated entity according to punctuation marks in sentences in which the entity of the preset type is located; and extracting the determined attribute information of each entity.
In this embodiment, when determining the entity in each canonical document, the executing body may first search a preset language description in each canonical document, and determine the historical entity, the replacement entity of the historical entity, and the relationship between the historical entity and the replacement entity. For example, the preset language description may be "the present standard replaces XXX". After the language description is found, the replacement entity can be determined to be the "present standard", the history entity is "XXX", and the relationship between the replacement entity and the history entity is a replacement relationship. Meanwhile, the execution body can also perform entity identification on each specification document to determine the preset type of entity in each specification document. For example, entities such as drafting units, drafting persons, etc. in the standard document can be identified. Or the executing entity may identify entities of name categories, such as Zhang three, lifour, etc. The execution body can also determine the associated entity with the entity of the preset type according to the punctuation mark of the entity of the preset type in the sentence. For example, the sentence is "drafting unit: a power company, a design institute B and a research institute C. The execution subject may determine the entity "drafting unit" using entity identification, and then determine the entity "drafting unit" according to ": and determines that the associated entity is "A power company", "B design institute", "C research institute". The executing body may then also extract attribute information in each canonical document, such as drafting time, validation time, etc.
It will be appreciated that the entity identification and structure analysis method described in step 403 may be applied in the target paragraph determined in step 402.
And step 404, constructing a knowledge graph of the target object according to the entity, the relation and the attribute information.
After determining each entity, the relationship between each entity, and attribute information of each entity, the execution subject may construct a knowledge graph of the target object.
In some alternative implementations of the present embodiment, the plurality of specification documents may include at least one revision document of at least one specification document. The above method may further comprise the following steps, not shown in fig. 4: in response to receiving the historical search information for the target object, a revised history of the application specification of the target object is determined from the knowledge-graph.
In this implementation, if the plurality of specification documents may include at least one revision document of the at least one specification document, there is at least one revision of the application specification specifying the target object. The knowledge-graph includes information of multiple revisions of the target object. After receiving the historical search information for the target object, the execution body can determine the revision history of the application specification of the target object according to the knowledge graph. The revision history may include revision time, validation time, drafting units, etc. of each version.
In some optional implementations of the present embodiment, the method may further include the following steps, not shown in fig. 4: responding to the received query information aiming at the target object, analyzing the query information and determining the query edge or the query attribute information of the target object; and determining answers of the query information according to the knowledge graph and the query edge or according to the knowledge graph and the query attribute information.
In this implementation manner, if the execution body receives the query information for the target object, the query information is parsed, and the query edge of the target object is determined. The query information may be "what B of a" is, where a may represent an entity, and B may be relationship or attribute information. For example, the query information is "when the revision time of criterion a is". The execution body determines answers of the query information according to the knowledge graph and the query edge or according to the knowledge graph and the query attribute information. The execution body may output the answer.
In some optional implementations of the present embodiment, the method may further include the following steps, not shown in fig. 4: searching attribute information and related entities of the target entity in the knowledge graph in response to receiving query information of the target entity; and summarizing the searched attribute information and the related entity, and outputting the summarized information.
In this implementation manner, if the executing body receives the query information for the target entity, the target entity may be any entity in the knowledge graph. The execution subject can query the information of the target entity in the knowledge graph to determine the attribute information of the target entity and the associated entity. The execution body can collect the searched attribute information and the related entity and output the collected information.
The knowledge graph construction method provided by the embodiment of the disclosure can construct the knowledge graph by self and improve the application range of the knowledge graph.
With further reference to fig. 5, as an implementation of the method shown in the foregoing drawings, the disclosure provides an embodiment of a knowledge-graph construction apparatus, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.
As shown in fig. 5, the knowledge graph construction apparatus 500 of the present embodiment includes: a document acquisition unit 501, an information extraction unit 502, and a knowledge graph construction unit 503.
The document acquisition unit 501 is configured to acquire a plurality of specification documents of the target object, the plurality of specification documents representing application specifications for the target object.
The information extraction unit 502 is configured to perform entity recognition and structure analysis on the text in each canonical document, and determine the entity in each canonical document, the relationship between the entities, and the attribute information of the entities.
The knowledge graph construction unit 503 is configured to construct a knowledge graph of the target object according to the entity, the relationship, and the attribute information.
In some optional implementations of the present embodiment, the information extraction unit 502 may be further configured to: keyword matching is carried out on each specification document, and target sentences in each specification document are determined; determining the paragraph in which the target sentence is located as a target paragraph; entity recognition and structure analysis are carried out on the characters in the target paragraph, and the entities in each canonical document, the relation among the entities and the attribute information of the entities are determined.
In some optional implementations of the present embodiment, the information extraction unit 502 may be further configured to: searching preset language description in each specification document, and determining a history entity, a replacement entity of the history entity and a relation between the history entity and the replacement entity; performing entity identification on each specification document, and determining a preset type entity in each specification document; determining an associated entity of the preset type and a relation between the entity of the preset type and the associated entity according to punctuation marks in sentences in which the entity of the preset type is located; and extracting the determined attribute information of each entity.
In some alternative implementations of the present embodiment, the plurality of specification documents includes at least one revision document of the at least one specification document. The apparatus 500 may further comprise a first querying element, not shown in fig. 5, configured to: in response to receiving the historical search information for the target object, a revised history of the application specification of the target object is determined from the knowledge-graph.
In some optional implementations of the present embodiment, the apparatus 500 may further include a second query unit, not shown in fig. 5, configured to: responding to the received query information aiming at the target object, analyzing the query information and determining the query edge or the query attribute information of the target object; and determining answers of the query information according to the knowledge graph and the query edge or according to the knowledge graph and the query attribute information.
In some optional implementations of the present embodiment, the apparatus 500 may further include a third query unit, not shown in fig. 5, configured to: responding to the received query information of the target entity, and searching attribute information and related entities of the target entity in a knowledge graph; and summarizing the searched attribute information and the related entity, and outputting the summarized information.
It should be understood that the units 501 to 503 described in the knowledge graph construction apparatus 500 correspond to the respective steps in the method described with reference to fig. 2, respectively. Thus, the operations and features described above for the knowledge graph construction method are equally applicable to the apparatus 500 and the units contained therein, and are not described herein.
In the technical scheme of the disclosure, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the user accord with the regulations of related laws and regulations, and the public order colloquial is not violated.
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
Fig. 6 shows a block diagram of an electronic device 600 performing a knowledge-graph construction method, in accordance with 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 telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary 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 can perform various suitable actions and processes according to 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 required for the operation of the electronic device 600 can also be stored. The processor 601, the ROM 602, and the RAM603 are connected to each other through a bus 604. An I/O interface (input/output interface) 605 is also connected to the bus 604.
A number of components in the electronic device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, mouse, etc.; an output unit 607 such as various types of displays, speakers, and the like; memory 608, e.g., magnetic disk, optical disk, etc.; 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.
The processor 601 may be a variety of general and/or special purpose processing components having 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, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 601 performs the various methods and processes described above, such as a knowledge graph construction method. For example, in some embodiments, the knowledge graph construction method may be implemented as a computer software program tangibly embodied on 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 RAM 603 and executed by processor 601, one or more steps of the knowledge graph construction method described above may be performed. Alternatively, in other embodiments, processor 601 may be configured to perform the knowledge-graph construction method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On 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, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out 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 into 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/operations specified in the flowchart and/or block diagram to be implemented. 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. The 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 pointing device (e.g., a mouse or 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 may 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 input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background 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 background, 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 a client and a server. The client and server are typically 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 that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service ("Virtual PRIVATE SERVER" or simply "VPS") are overcome. The server may also be a server of a distributed system or a server that incorporates a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions, improvements, etc. that are within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (8)
1. A knowledge graph construction method comprises the following steps:
Obtaining a plurality of specification documents of the target object, the plurality of specification documents being used to represent application specifications for the target object, the plurality of specification documents including at least one revision document of at least one specification document;
Keyword matching is carried out on each specification document, and target sentences in each specification document are determined; determining the paragraph of the target sentence as a target paragraph;
searching preset language description in each specification document, and determining a history entity, a replacement entity of the history entity and a relation between the history entity and the replacement entity; performing entity identification on each specification document, and determining a preset type entity in each specification document;
Determining an associated entity of the preset type and a relation between the entity of the preset type and the associated entity according to punctuation marks in sentences in which the entity of the preset type is positioned; extracting the determined attribute information of each entity;
Constructing a knowledge graph of the target object according to the entity, the relation and the attribute information;
In response to receiving historical search information for the target object, a revision history of an application specification of the target object is determined from the knowledge-graph.
2. The method of claim 1, wherein the method further comprises:
Responding to the received query information aiming at the target object, analyzing the query information, and determining the query edge or the query attribute information of the target object;
and determining answers of the query information according to the knowledge graph and the query edge or according to the knowledge graph and the query attribute information.
3. The method of claim 1, wherein the method further comprises:
Responding to the received query information of the target entity, and searching attribute information and related entities of the target entity in the knowledge graph;
And summarizing the searched attribute information and the related entity, and outputting the summarized information.
4. A knowledge graph construction apparatus comprising:
A document obtaining unit configured to obtain a plurality of specification documents of a target object, the plurality of specification documents representing an application specification for the target object, the plurality of specification documents including at least one revision document of at least one specification document;
The information extraction unit is configured to match keywords of each specification document and determine target sentences in each specification document; determining the paragraph of the target sentence as a target paragraph; searching preset language description in each specification document, and determining a history entity, a replacement entity of the history entity and a relation between the history entity and the replacement entity; performing entity identification on each specification document, and determining a preset type entity in each specification document; determining an associated entity of the preset type and a relation between the entity of the preset type and the associated entity according to punctuation marks in sentences in which the entity of the preset type is positioned; extracting the determined attribute information of each entity;
A knowledge graph construction unit configured to construct a knowledge graph of the target object according to the entity, the relationship, and the attribute information;
And a first query unit configured to determine a revision history of an application specification of the target object according to the knowledge-graph in response to receiving history search information for the target object.
5. The apparatus of claim 4, wherein the apparatus further comprises a second querying element configured to:
Responding to the received query information aiming at the target object, analyzing the query information, and determining the query edge or the query attribute information of the target object;
and determining answers of the query information according to the knowledge graph and the query edge or according to the knowledge graph and the query attribute information.
6. The apparatus of claim 4, wherein the apparatus further comprises a third querying element configured to:
Responding to the received query information of the target entity, and searching attribute information and related entities of the target entity in the knowledge graph;
And summarizing the searched attribute information and the related entity, and outputting the summarized information.
7. 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 of any one of claims 1-3.
8. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-3.
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