Knowledge graph ontology construction and updating method and device, storage medium and equipment

Abstract

The invention provides a knowledge graph ontology construction and updating method and device, a storage medium and equipment, and the method comprises the steps: automatically recognizing and analyzing key information in data from a big data platform through employing a big model technology, obtaining metadata, constructing a metadata knowledge graph ontology through employing an ontology engineering method according to the metadata, and monitoring the change of the metadata in real time, and dynamically updating the metadata knowledge graph ontology according to the changed metadata. According to the method, the metadata can be automatically obtained from the big data platform, the tedious process of manual data collection and arrangement is reduced, the metadata graph ontology is automatically constructed and updated, the dependence on professionals is reduced, the labor cost is saved, data changes can be quickly responded through automatic processing, the metadata graph ontology is updated in time, and the efficiency is improved. The intelligent algorithm can accurately identify and process metadata, reduce errors and omission, and improve efficiency and accuracy.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a knowledge graph ontology construction and update method, device, storage medium and equipment. Background Art

[0002] Knowledge Graph refers to the technology of using visualization technology to describe knowledge resources and their carriers, and to mine, analyze, construct, draw and display knowledge and their interrelationships.

[0003] At present, the relevant technical solutions generally construct knowledge graphs directly based on original data through manual methods. However, this method reduces the security of the original data, requires a lot of human resources, and is costly. Summary of the invention

[0004] In view of this, the present invention provides a knowledge graph ontology construction and update method, device, storage medium and equipment, which can reduce labor costs and improve efficiency and accuracy.

[0005] In a first aspect, an embodiment of the present invention provides a method for constructing and updating a knowledge graph ontology, the method comprising:

[0006] Use big model technology to automatically identify and parse key information in data from the big data platform to obtain metadata;

[0007] Constructing a metadata knowledge graph ontology based on the metadata using an ontology engineering method;

[0008] Monitor changes in metadata in real time, and dynamically update the metadata knowledge graph ontology based on the changed metadata.

[0009] Furthermore, the use of big model technology to automatically identify and parse key information in data from the big data platform to obtain metadata includes:

[0010] Preprocessing the data in the big data platform;

[0011] Leverage the natural language processing capabilities of large model technology to extract key information from pre-processed data;

[0012] The key information is parsed to obtain structured metadata.

[0013] Furthermore, by using the natural language processing capabilities of the big model technology, key information extracted from the preprocessed data includes:

[0014] Using the entity recognition capability of large model technology, entity information is identified and extracted from preprocessed data;

[0015] Using the relationship extraction capability of large model technology, we can identify and extract the relationship information between entities;

[0016] By understanding the semantic structure in the preprocessed data, event information in the preprocessed data is identified and extracted, and the entity information, relationship information and event information constitute the key information.

[0017] Furthermore, constructing a metadata knowledge graph ontology using an ontology engineering method according to the metadata includes:

[0018] Define ontology concepts, establish concept hierarchies, define relationship types, and define attributes;

[0019] Use ontology modeling tools to build ontology models;

[0020] The data items in the metadata are mapped to the ontology model to obtain the metadata knowledge graph ontology.

[0021] Furthermore, real-time monitoring of metadata changes and dynamically updating the metadata knowledge graph ontology according to the changed metadata include:

[0022] An incremental update method is adopted to update only the metadata knowledge graph ontology corresponding to the metadata part that has changed.

[0023] Furthermore, real-time monitoring of metadata changes and dynamically updating the metadata knowledge graph ontology according to the changed metadata include:

[0024] Select the appropriate update algorithm based on the changes in metadata;

[0025] Determine the updated data;

[0026] The update algorithm is executed on the update data to obtain an update result, and the metadata knowledge graph ontology is updated based on the update result.

[0027] Furthermore, the update algorithm includes an incremental update algorithm or a pattern-based update algorithm.

[0028] In a second aspect, an embodiment of the present invention provides a knowledge graph ontology construction and update device, the device comprising:

[0029] The metadata acquisition module is used to automatically identify and parse key information in the data from the big data platform using big model technology to obtain metadata;

[0030] A knowledge graph ontology construction module, used to construct a metadata knowledge graph ontology based on the metadata using an ontology engineering method;

[0031] The knowledge graph ontology update module is used to monitor the changes of metadata in real time and dynamically update the metadata knowledge graph ontology according to the changed metadata.

[0032] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any one of the methods in the first aspect when executed.

[0033] In a fourth aspect, an embodiment of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any one of the methods described in the first aspect.

[0034] The technical solution provided by the present invention has the following four beneficial effects. First, the system can automatically obtain metadata from the big data platform, reducing the tedious process of manual collection and organization of data, automatically constructing and updating the metadata map ontology, reducing dependence on professionals, and saving labor costs; second, automated processing can quickly respond to data changes and timely update the metadata map ontology, and intelligent algorithms can accurately identify and process metadata, reduce errors and omissions, and improve efficiency and accuracy; third, the system can process metadata from different big data platforms, adapt to changes in different data sources and data formats, support multiple metadata types and relationships, and construct complex and diverse metadata map ontology; fourth, the system architecture is flexible and easy to add new functional modules and algorithms to adapt to the development of future data and technology, and can support larger-scale metadata processing and analysis to meet the growing data needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flow chart of the knowledge graph ontology construction and updating method provided in the first embodiment of the present invention;

[0036] Figure 2 It is a schematic diagram of the structure of the knowledge graph ontology construction and update device provided in the second embodiment of the present invention;

[0037] Figure 3 It is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Embodiment 1

[0040] See also Figure 1 , Figure 1 The following is a flow chart of a method for constructing and updating a knowledge graph ontology according to an embodiment of the present invention. The method comprises the following steps:

[0041] Step 11: Use big model technology to automatically identify and parse key information in the data from the big data platform to obtain metadata.

[0042] Big model technology refers to the use of advanced artificial intelligence technologies such as deep learning to build models with powerful computing power and high intelligence. These models usually contain a large number of parameters and complex neural network structures, can process and analyze large amounts of data, and have excellent natural language processing, image recognition, speech recognition and other capabilities.

[0043] Big data platforms refer to systems or platforms that can store, process, and analyze large amounts of data. These platforms typically include multiple data sources, such as relational databases, non-relational databases, log files, social media data, etc., and provide a wealth of data processing and analysis tools.

[0044] In a big data platform, data usually contains a lot of information, but not all information is useful. In order to obtain metadata, we need to identify key information from this data. This usually involves natural language processing, information extraction and other technologies. Big model technology can use these technologies to automatically extract useful information from data, such as entities, attributes, relationships, etc.

[0045] Metadata is data about data, which describes the structure, meaning, source, quality and other aspects of the data. By automatically identifying and parsing key information in the data, we can obtain these metadata. These metadata are of great significance for data management, analysis and mining.

[0046] In some embodiments, step 11 may be implemented by:

[0047] Step 111: pre-process the data in the big data platform.

[0048] In a big data platform, data usually comes from multiple different data sources and may have different formats, structures, and qualities. Therefore, before extracting key information, these data need to be preprocessed. The main purpose of preprocessing is to clean the data, unify the format, remove redundancy and outliers, and possibly transform the data to ensure the accuracy and efficiency of subsequent processing steps.

[0049] Preprocessing may include the following sub-steps:

[0050] Data cleaning: remove duplicate data, process missing values, correct erroneous data, etc.

[0051] Data format unification: Convert data in different formats into a unified format for subsequent processing.

[0052] Data de-redundancy: remove unnecessary data, reduce data volume, and improve processing efficiency.

[0053] Data conversion: Perform necessary conversions on data, such as data type conversion, data encoding conversion, etc.

[0054] Step 112: Utilize the natural language processing capability of the big model technology to extract key information from the preprocessed data.

[0055] The preprocessed data may contain a large amount of text information, such as descriptions, instructions, tags, etc. These text information may hide key information, such as entity names, attribute descriptions, relationships, etc. In order to extract this information, the natural language processing capabilities of large model technology can be used.

[0056] Big model technology usually includes advanced natural language processing algorithms and models, such as deep learning models, neural network models, etc. The entity recognition capability of big model technology is used to identify and extract entity information from preprocessed data, and the relationship extraction capability of big model technology is used to identify and extract relationship information between entities. By understanding the semantic structure in the preprocessed data, event information in the preprocessed data is identified and extracted. The entity information, relationship information and event information constitute the key information.

[0057] Step 113: parse the key information to obtain structured metadata.

[0058] The extracted key information is usually unstructured or semi-structured, such as entity names and attribute descriptions in the text. In order to convert this information into structured metadata, it needs to be parsed.

[0059] The parsing process may include the following sub-steps:

[0060] Entity Recognition: Identifying entity names from text and associating them with specific categories or types.

[0061] Attribute extraction: Identify the attributes of an entity and extract its value. Attributes may include descriptions, features, relationships, etc. of the entity.

[0062] Relationship construction: Identify the relationships between entities and build corresponding relationship models. Relationships may include associations, dependencies, and hierarchies between entities.

[0063] Through parsing, the extracted key information can be converted into structured metadata, which can clearly describe the structure, meaning and relationship of the data, providing strong support for subsequent data management, analysis and mining.

[0064] Step 12: Construct a metadata knowledge graph ontology based on the metadata using an ontology engineering method.

[0065] Ontology engineering is a systematic approach to building and maintaining ontologies. Ontology is a clear specification of shared conceptualizations that defines concepts and relationships within a domain, enabling communication and knowledge sharing between different systems and users. When building metadata knowledge graph ontologies, ontology engineering provides a set of systematic processes and tools to ensure the accuracy, consistency, and scalability of the ontology.

[0066] In some embodiments, step 12 may be implemented by:

[0067] Step 121, define ontology concepts, establish concept hierarchies, define relationship types, and define attributes.

[0068] In this step, it is necessary to clarify the core concepts (or entities) that will be included in the metadata knowledge graph ontology. These concepts should be able to fully reflect the domain knowledge described by the metadata.

[0069] A concept hierarchy (or class hierarchy) describes the inheritance relationship between concepts. For example, a more general concept (such as "entity") can have one or more more specific sub-concepts (such as "person", "organization", etc.). This hierarchy helps to organize and manage concepts in an ontology.

[0070] Relationship types describe how concepts are related. For example, you can define relationship types such as "belongs to", "contains", and "is related to" to represent different connections between concepts.

[0071] Attributes are elements that describe the characteristics of a concept. Each concept can have multiple attributes, which can be data type attributes (such as "age", "date", etc.) or object attributes (such as "parent class", "child class", etc.). The definition of attributes helps to describe the concept in more detail.

[0072] Step 122: Use an ontology modeling tool to build an ontology model.

[0073] In this step, you need to use an ontology modeling tool (such as Protegé, Ontolingua, WebOnto, etc.) to transform the concepts, hierarchical structures, relationship types, and attributes defined in step 121 into a specific ontology model. Ontology modeling tools provide a graphical interface and rich functions, allowing users to easily create, edit, and manage ontologies.

[0074] Step 123: Map the data items in the metadata to the ontology model to obtain the metadata knowledge graph ontology.

[0075] In this step, the data items (such as fields, records, etc.) in the metadata need to be mapped with the concepts, attributes, and relationships in the ontology model. This mapping process ensures that the metadata can be correctly understood and represented in the ontology model.

[0076] Through data item mapping, metadata can be converted into structured data that conforms to the ontology model. The representation of these data in the ontology model is the metadata knowledge graph ontology. The metadata knowledge graph ontology not only retains the original information in the metadata, but also provides richer semantics and context for this information through the concepts and relationships in the ontology model.

[0077] Step 13: monitor the changes of metadata in real time, and dynamically update the metadata knowledge graph ontology according to the changed metadata.

[0078] Real-time monitoring of metadata changes and dynamic updating of the metadata knowledge graph ontology based on the changed metadata are key steps to ensure the timeliness and accuracy of the knowledge graph ontology.

[0079] In some embodiments, step 13 may be performed by incrementally updating only the metadata knowledge graph ontology corresponding to the metadata portion that has changed.

[0080] First, you need to identify exactly which metadata has changed. This can be done by comparing old and new metadata, listening to metadata change events, or using a version control system.

[0081] Once the changed metadata is identified, the next step is to determine which parts of the metadata knowledge graph ontology these changes have affected. This may involve querying, analyzing, and matching the ontology model.

[0082] After the update, the metadata knowledge graph ontology needs to be checked for consistency to ensure that the update does not damage the integrity of the ontology or introduce errors. After the update, the updated version should be recorded and the old version should be retained for rollback or comparison when necessary. If the metadata knowledge graph ontology is shared by multiple systems or services, these systems or services should be notified to synchronize after the update to ensure that they are using the latest ontology.

[0083] Incremental updates only update the affected areas, avoiding unnecessary full updates, thereby improving update efficiency. At the same time, since only the changed parts are updated, incremental updates reduce the consumption of computing and storage resources, and incremental updates can reflect metadata changes more quickly, thereby improving the timeliness of the metadata knowledge graph ontology.

[0084] In some other embodiments, step 13 may also be implemented by the following steps:

[0085] Step 131: Select a suitable update algorithm according to the change of metadata.

[0086] In this step, it is necessary to select an appropriate update algorithm based on factors such as the type, scale, frequency of metadata changes, and the impact on the metadata knowledge graph. Update algorithms may include but are not limited to:

[0087] Direct mapping: If the metadata changes are simple and directly correspond to a concept or attribute in the ontology, you can use direct mapping to update it.

[0088] Reasoning update: If the metadata changes involve the relationship between multiple concepts or attributes, it may be necessary to use reasoning algorithms to update the ontology to ensure that the updated ontology remains consistent.

[0089] Batch processing: For large-scale metadata changes, it may be necessary to use a batch processing algorithm to improve update efficiency.

[0090] Incremental update: As mentioned before, only the affected part of the ontology is updated to reduce unnecessary computation and storage overhead.

[0091] Step 132: Determine the update data.

[0092] In this step, it is necessary to clarify which metadata needs to be updated. This may involve parsing metadata change events, querying or filtering data in the data source, and querying and matching ontology models. Ensuring that only data that really needs to be updated is processed can improve the efficiency and accuracy of the update process.

[0093] Step 133: execute the update algorithm on the update data to obtain an update result, and update the metadata knowledge graph ontology based on the update result.

[0094] In this step, the previously selected update algorithm is applied to the determined update data to obtain the update results. The update results may include new concepts, attributes, or relationships, or modifications to existing concepts, attributes, or relationships. Then, the metadata knowledge graph ontology is actually updated based on these update results. During the update process, the integrity, consistency, and semantic correctness of the ontology need to be ensured.

[0095] The technical solution provided by the embodiment of the present invention has the following four beneficial effects. First, the solution makes data processing tasks efficient and automated. Through the metadata-driven mechanism, it realizes the automatic scheduling of the execution order of nodes in the DAG model without manual intervention, which greatly improves the automation level of task execution. The nodes are sorted by the topological sorting algorithm to ensure that all predecessor nodes of each node are arranged before the node, thereby realizing efficient execution of tasks. At the same time, the solution also supports parallel processing, which can fully utilize the technical solution provided by the present invention and has the following four beneficial effects. First, the system can automatically obtain metadata from the big data platform, reducing the tedious process of manual collection and organization of data, automatically constructing and updating the metadata map ontology, reducing dependence on professionals, and saving labor costs; second, automated processing can quickly respond to data changes, timely update the metadata map ontology, and intelligent algorithms can accurately identify and process metadata, reduce errors and omissions, and improve efficiency and accuracy; third, the system can process metadata from different big data platforms, adapt to changes in different data sources and data formats, support multiple metadata types and relationships, and build complex and diverse metadata map ontology; fourth, the system architecture is flexible and easy to add new functional modules and algorithms to adapt to the development of future data and technology, and can support larger-scale metadata processing and analysis to meet the growing data needs.

[0096] Embodiment 2

[0097] See also Figure 2 , Figure 2 : is a schematic diagram of a structure diagram of a knowledge graph ontology construction and update device provided in Embodiment 2 of the present invention, wherein the device comprises:

[0098] The metadata acquisition module 21 is used to automatically identify and parse key information in the data from the big data platform using the big model technology to obtain metadata;

[0099] A knowledge graph ontology construction module 22, used to construct a metadata knowledge graph ontology based on the metadata using an ontology engineering method;

[0100] The knowledge graph ontology updating module 23 is used to monitor the changes of metadata in real time and dynamically update the metadata knowledge graph ontology according to the changed metadata.

[0101] In some embodiments, the metadata acquisition module 21 may include:

[0102] A preprocessing unit 211 is used to preprocess the data in the big data platform;

[0103] An extraction unit 212, used to extract key information from the preprocessed data using the natural language processing capability of the large model technology;

[0104] The parsing unit 213 is configured to parse the key information to obtain structured metadata.

[0105] In some embodiments, the extraction unit 212 may be specifically configured to:

[0106] Using the entity recognition capability of large model technology, entity information is identified and extracted from preprocessed data;

[0107] Using the relationship extraction capability of large model technology, we can identify and extract the relationship information between entities;

[0108] By understanding the semantic structure in the preprocessed data, event information in the preprocessed data is identified and extracted, and the entity information, relationship information and event information constitute the key information.

[0109] In some embodiments, the knowledge graph ontology building module 22 may include:

[0110] Definition unit 221, used to define ontology concepts, establish concept hierarchies, define relationship types, and define attributes;

[0111] A construction unit 222 is used to construct an ontology model using an ontology modeling tool;

[0112] The mapping unit 223 is used to map the data items in the metadata into the ontology model to obtain the metadata knowledge graph ontology.

[0113] In some embodiments, the knowledge graph ontology updating module 23 specifically adopts an incremental update method to update only the metadata knowledge graph ontology corresponding to the metadata part that has changed.

[0114] In some other embodiments, the metadata knowledge graph ontology updating module 23 may include:

[0115] The algorithm selection unit 231 is used to select a suitable update algorithm according to the change of metadata;

[0116] A determination unit 232, configured to determine update data;

[0117] The updating unit 233 is used to execute the updating algorithm on the updating data to obtain an updating result, and update the metadata knowledge graph ontology based on the updating result.

[0118] Therefore, the technical solution provided by the embodiment of the present invention has the following four beneficial effects. First, the solution makes data processing tasks efficient and automated. Through the metadata-driven mechanism, it realizes the automatic scheduling of the execution order of nodes in the DAG model without manual intervention, which greatly improves the automation level of task execution. The nodes are sorted by the topological sorting algorithm to ensure that all predecessor nodes of each node are arranged before the node, thereby realizing efficient execution of tasks. At the same time, the solution also supports parallel processing, which can fully utilize the technical solution provided by the present invention and has the following four beneficial effects. First, the system can automatically obtain metadata from the big data platform, reducing the tedious process of manual collection and organization of data, automatically constructing and updating the metadata map ontology, reducing dependence on professionals, and saving labor costs; second, automated processing can quickly respond to data changes, timely update the metadata map ontology, and intelligent algorithms can accurately identify and process metadata, reduce errors and omissions, and improve efficiency and accuracy; third, the system can process metadata from different big data platforms, adapt to changes in different data sources and data formats, support multiple metadata types and relationships, and build complex and diverse metadata map ontology; fourth, the system architecture is flexible and easy to add new functional modules and algorithms to adapt to the development of future data and technology, and can support larger-scale metadata processing and analysis to meet the growing data needs.

[0119] It should be noted that the device for constructing and updating the knowledge graph ontology in the embodiment of the present invention and the method for constructing and updating the knowledge graph ontology in the above-mentioned embodiment belong to the same inventive concept. The technical details not described in detail in this device can be found in the previous description of the method and will not be repeated here.

[0120] In addition, an embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the aforementioned method when running.

[0121] Figure 3 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0122] like Figure 3 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0123] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0124] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as an idle detection method.

[0125] In some embodiments, the idle detection method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the idle detection method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the idle detection method in any other appropriate manner (e.g., by means of firmware).

[0126] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0127] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0128] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0129] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the 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, voice input, or tactile input).

[0130] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0131] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0132] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0133] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A knowledge graph ontology construction and updating method, characterized in that: The method comprises: Use big model technology to automatically identify and parse key information in data from the big data platform to obtain metadata; Constructing a metadata knowledge graph ontology based on the metadata using an ontology engineering method; Monitor changes in metadata in real time, and dynamically update the metadata knowledge graph ontology based on the changed metadata.

2. The method according to claim 1, characterized in that The method of automatically identifying and parsing key information in data from a big data platform using big model technology to obtain metadata includes: Preprocessing the data in the big data platform; Leverage the natural language processing capabilities of large model technology to extract key information from pre-processed data; The key information is parsed to obtain structured metadata.

3. The method according to claim 2, characterized in that Using the natural language processing capabilities of large model technology, key information extracted from pre-processed data includes: Using the entity recognition capability of large model technology, entity information is identified and extracted from preprocessed data; Using the relationship extraction capability of large model technology, we can identify and extract the relationship information between entities; By understanding the semantic structure in the preprocessed data, event information in the preprocessed data is identified and extracted, and the entity information, relationship information and event information constitute the key information.

4. The method according to claim 1, characterized in that: Constructing a metadata knowledge graph ontology using an ontology engineering method based on the metadata includes: Define ontology concepts, establish concept hierarchies, define relationship types, and define attributes; Use ontology modeling tools to build ontology models; The data items in the metadata are mapped to the ontology model to obtain the metadata knowledge graph ontology.

5. The method according to claim 1, characterized in that Real-time monitoring of metadata changes and dynamic updating of the metadata knowledge graph ontology according to the changed metadata include: An incremental update method is adopted to update only the metadata knowledge graph ontology corresponding to the metadata part that has changed.

6. The method according to claim 1, characterized in that Real-time monitoring of metadata changes and dynamic updating of the metadata knowledge graph ontology according to the changed metadata include: Select the appropriate update algorithm based on the changes in metadata; Determine the updated data; The update algorithm is executed on the update data to obtain an update result, and the metadata knowledge graph ontology is updated based on the update result.

7. The method according to claim 6, characterized in that The update algorithm includes an incremental update algorithm or a pattern-based update algorithm.

8. A knowledge graph ontology construction and update device, characterized in that: The device comprises: The metadata acquisition module is used to automatically identify and parse key information in the data from the big data platform using big model technology to obtain metadata; A knowledge graph ontology construction module, used to construct a metadata knowledge graph ontology based on the metadata using an ontology engineering method; The knowledge graph ontology update module is used to monitor the changes of metadata in real time and dynamically update the metadata knowledge graph ontology according to the changed metadata.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 7 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 7.

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