Knowledge base treatment method based on data treatment thought

Abstract

The invention relates to a knowledge base treatment method based on a data treatment thought. Along with enterprise development, knowledge base management faces challenges, traditional tools are difficult to meet requirements, and knowledge quality and consistency also have problems. The method takes governance as an idea, and comprises the steps of knowledge metadata management, data acquisition and management by utilizing a meta-model, and model customization by an enterprise; checking the content of the knowledge base, constructing a rule base based on various standards, and combining automation and manual review; knowledge master data management, from demand application to modeling processing and the like; knowledge base markets, resource catalogs are sorted, and safety is guaranteed. The method can improve knowledge management efficiency, enhance asset security, and promote knowledge application and innovation.

Description

Technical Field

[0001] The present invention relates to the technical field of knowledge base management, and in particular to a knowledge base management method based on data management ideas. Background Art

[0002] With the development of enterprises and the continuous expansion of business, the knowledge assets accumulated in the knowledge base are becoming increasingly rich, covering various types such as product manuals, solution blueprints, promotional materials, project management documents, training resources, company regulations and video materials. However, these knowledge assets face many problems in the process of management and utilization.

[0003] Limitations of traditional knowledge base management tools: Traditional knowledge base management tools on the current market are difficult to meet the needs of enterprises for efficient management and in-depth utilization of knowledge assets. On the one hand, there are many types of documents and complex application scenarios, and traditional tools cannot ensure effective management of these assets of different formats and quality. For example, for project documents that contain text, pictures, videos and other formats at the same time, traditional tools may not be able to provide comprehensive management functions. On the other hand, building a comprehensive and complete knowledge system often requires reference to multiple or even multiple formats of files, and traditional knowledge base software lacks the ability to effectively process and merge these diversified assets.

[0004] Knowledge quality and consistency issues: In the enterprise knowledge base, the quality, consistency and reusability of knowledge are difficult to guarantee. Due to the lack of unified standards and specifications, knowledge may have problems such as irregular format, inaccurate content, and inconsistent logic. This not only affects the effective dissemination and sharing of knowledge, but also brings obstacles to the application and innovation of knowledge within the enterprise. For example, product documents submitted by different departments may have differences in terminology use, data format, etc., causing confusion for other departments when using this knowledge.

[0005] The rise of data governance concepts and application needs

[0006] Importance of data governance: In the field of enterprise data management, the concept of data governance is gradually emerging. Data governance emphasizes the comprehensive management of data, including data definition, standard setting, quality control, security assurance, and effective use. Through data governance, the quality and value of data can be improved, data sharing and collaboration within the enterprise can be promoted, and strong support can be provided for enterprise decision-making.

[0007] The need for knowledge base governance: As an important storage and management method for enterprise knowledge assets, the knowledge base also needs to introduce the idea of ​​data governance. Enterprises need a method that can ensure the accuracy, consistency and reusability of knowledge in the knowledge base, while also ensuring the security of knowledge assets and promoting the smooth circulation and efficient application of knowledge. This knowledge base governance method based on data governance ideas can help enterprises better manage and utilize knowledge assets and enhance their competitiveness. Summary of the invention

[0008] With governance as the core concept, it aims to ensure the safety of knowledge assets while maximizing their value. We are committed to creating a new system that can both protect the security of knowledge and promote its smooth circulation and efficient application, so that knowledge can be truly used.

[0009] 2. Core Technology Module

[0010] Knowledge metadata management

[0011] Meta models are used to collect and manage metadata for knowledge in the knowledge base. Enterprises can flexibly specify and customize knowledge models according to their own needs, covering dimensions such as file format, size, content, creator, and rating. Through built-in metadata collection engines, including natural language processing (NLP) and machine learning technology, JAVA IO processing, and providing API interfaces, intelligent extraction of knowledge content and interaction with external systems are achieved, thereby effectively inventorying the enterprise knowledge base.

[0012] Knowledge base content verification

[0013] A comprehensive rule base is built based on national standards, industry standards, local standards and other standards, and the knowledge base is strictly formatted and content verified. The verification process combines automated tools and manual review. Automated tools include Markdown parsers, XML / HTML validators and custom scripts, which can verify titles, paragraphs, lists, quotes, pictures and charts. At the same time, manual review ensures the accuracy of some details. Content verification also involves information source reliability, logical consistency, information integrity, data freshness, comprehensibility and language suitability. Natural language processing tools are used to detect grammar, spelling errors and sentence structure problems, and text similarity algorithms are used to detect duplicate or contradictory information. Data validation rules are defined and executed, and periodic review plans are formulated and version control is implemented.

[0014] Knowledge Master Data Management

[0015] Apply according to internal or personal needs of the enterprise, analyze and understand the needs, and precipitate and organize common business needs. Then carry out knowledge modeling, clarify the content of knowledge files in the master data and subdivided attribute operations, establish the correspondence between metadata and attributes to ensure data consistency and accuracy, and finally clean, integrate and optimize the data through knowledge base processing tasks to ensure data quality and availability, and provide support and services for the business.

[0016] Knowledge Base Marketplace

[0017] A comprehensive knowledge resource catalog is compiled using the low-code development engine combined with metadata inventory results, including all knowledge resources within the enterprise, making it easier for employees to apply and obtain them. At the same time, a series of measures are taken to ensure the security and compliance of knowledge resources, including strict permission management, desensitization of sensitive information, encryption technology, and audit tracking.

[0018] Beneficial effects of the present invention:

[0019] Improve knowledge management efficiency

[0020] Through effective metadata management and content verification, knowledge can be located and utilized more accurately, the inventory efficiency and quality of the enterprise knowledge base can be improved, the spread of erroneous and misleading information can be reduced, the accuracy and consistency of knowledge can be improved, and the needs of various application scenarios can be met.

[0021] Enhance the security of intellectual assets

[0022] The knowledge base content verification mechanism and the security measures of the knowledge base market ensure the security of knowledge assets, prevent data leakage and abuse, and ensure that knowledge is circulated and shared in a safe environment.

[0023] Promoting knowledge application and innovation

[0024] A good knowledge governance system enables knowledge to be better applied to the business processes of an enterprise, promotes knowledge innovation and sharing, and improves the operational efficiency and competitiveness of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 It is a core module and functional schematic diagram of the present invention;

[0027] Figure 2This is a schematic diagram of knowledge metadata management of the present invention;

[0028] Figure 3 It is a schematic diagram of checking the contents of the knowledge base of the present invention;

[0029] Figure 4 This is a schematic diagram of knowledge master data management of the present invention;

[0030] Figure 5 It is a schematic diagram of the knowledge base market of the present invention;

[0031] Figure 6 This is a schematic diagram of knowledge base metadata management of the present invention;

[0032] Figure 7 This is a schematic diagram of the knowledge base content verification of the present invention. DETAILED DESCRIPTION

[0033] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0034] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0035] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0036] like Figures 1 to 7 Shown

[0037] Example 1

[0038] Knowledge metadata management

[0039] Metamodel and modular system

[0040] Design a modular system with built-in meta-model, so that enterprises can specify and customize knowledge models according to their own understanding and needs of knowledge, including dimensions such as file format, size, content, creator, and rating.

[0041] Metadata collection engine

[0042] Natural Language Processing (NLP) and Machine Learning Technologies

[0043] These technologies are used to automatically extract key information from unstructured documents, such as keywords and summaries. For example, for large amounts of text data, valuable information can be extracted through advanced algorithms to provide support for subsequent data analysis and knowledge management.

[0044] JAVA IO processing

[0045] For knowledge files stored in the knowledge base, key attributes such as file size, format, and creation time are extracted to better manage and organize files and ensure data accuracy and accessibility.

[0046] API

[0047] Provides an API interface to facilitate external systems to call metadata or integrate with other services to achieve efficient data interaction and processing.

[0048] Metadata Integration and Application

[0049] The collected metadata is integrated into the graph database to form applications such as knowledge maps and knowledge lineage maps. These applications help understand knowledge relationships, provide support for data governance and knowledge management, and improve enterprise decision-making quality and business efficiency.

[0050] 2. Knowledge Base Content Verification

[0051] Verification rule base construction

[0052] Extract and analyze standardized content, build a knowledge verification rule base, and verify the format and content of the knowledge base. The verification scope includes multiple formats such as titles, paragraphs, lists, citations, pictures and charts, using tools such as Markdown parsers, XML / HTML validators and custom scripts.

[0053] Combining manual review with automation

[0054] Some details need to be manually reviewed to confirm accuracy, and manual review can find problems that automated tools miss. Content verification also involves aspects such as the reliability of information sources, logical consistency, information completeness, data freshness, comprehensibility, and language appropriateness.

[0055] Use natural language processing tools to automatically detect grammar, spelling errors and sentence structure problems, identify text sentiment, and keep the content consistent. Use text similarity algorithms to detect duplicate or contradictory information, and define and execute data validation rules, such as field length, value range, and required fields.

[0056] Review plan and version control

[0057] Develop a periodic comprehensive review plan, which should be conducted every six months or every year. Record the content, date and person in charge in detail for each modification, and implement a strict version control system to track version changes.

[0058] 3. Knowledge Master Data Management

[0059] Demand analysis and precipitation

[0060] Apply based on business or personal needs, carefully analyze and understand the needs. Sort out common business needs for further processing and application.

[0061] Knowledge Modeling and Attribute Segmentation

[0062] Conduct knowledge modeling based on business conditions, clarify the knowledge file content contained in the master data, and how to subdivide attribute operations in order to understand and master the data structure and content.

[0063] Metadata and attribute relationship establishment

[0064] Establish a correspondence between metadata and attributes to ensure data consistency and accuracy and improve data use efficiency and effectiveness.

[0065] Knowledge base processing tasks

[0066] Through knowledge base processing tasks, data is cleaned, integrated, and optimized to ensure data quality and availability, providing support and services for the business.

[0067] 4. Knowledge Base Marketplace

[0068] Knowledge resource catalogue sorting

[0069] By using a low-code development engine and combining metadata inventory results, a comprehensive knowledge resource catalog is compiled that includes all knowledge resources within the enterprise, making it easier for employees to apply and obtain them.

[0070] Safety and Compliance Measures

[0071] Permission Management

[0072] Implement strict permission management to ensure that only authorized employees can access specific knowledge resources.

[0073] Desensitization

[0074] Desensitize sensitive information to prevent data leakage and abuse.

[0075] Encryption

[0076] Encryption technology is used to ensure the security of knowledge resources during transmission and storage.

[0077] Audit Trail

[0078] Conduct audit trails to record the use of knowledge resources and review and analyze when necessary.

[0079] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0080] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A knowledge base governance method based on data governance ideas, characterized in that: By building a knowledge meta-model and knowledge metadata management module, the knowledge base content is collected, classified and managed in multiple dimensions to ensure the accuracy, completeness and availability of the knowledge base content.

2. According to claim 1, a knowledge base governance method based on data governance concept is characterized in that: The knowledge metadata management module realizes the standardized collection of information such as knowledge entry format, size, author, and rating through dynamic metamodel configuration, and uses natural language processing (NLP) technology to intelligently extract and classify unstructured knowledge content in order to optimize the management and use of enterprise knowledge assets.

3. According to claim 1, a knowledge base governance method based on data governance concept is characterized in that: The metadata collection engine has a dynamic task binding function, which can add collection tasks according to business needs, and realize continuous inventory and update of knowledge base data through metadata collection workflow to support continuous optimization of the knowledge base.

4. A knowledge base verification method based on multiple standards, used in the knowledge base management method according to any one of claims 1 to 3, characterized in that By building a rule base based on national standards, industry standards, and internal corporate specifications, we ensure that the content of the knowledge base is consistent in format and logic and complies with specifications, thereby improving the reliability of the knowledge base.

5. The multi-standard knowledge base verification method according to claim 4, comprising: (a) Verification engines for Word, PDF, images, videos and other formats, such as Apache POI, which can detect the font, paragraph and line spacing of Word documents; (b) grammar and spelling checking based on natural language processing technology; (c) Perform logic and format verification on knowledge content through regular expressions, NLP semantic analysis and OCR recognition engine.

6. A knowledge processing method based on knowledge master data, used in the knowledge base management method according to any one of claims 1 to 5, characterized in that Systematically model and deeply process the knowledge base content to generate a knowledge master data structure that meets business needs, thereby achieving effective organization and convenient access to knowledge.

7. The knowledge processing method as described in claim 6 includes a master data modeling engine, which supports the extraction, classification and integration of knowledge content, forms structured information through data cleaning and processing, and is used to support business processes and enterprise decision-making.

8. A knowledge protection method based on knowledge desensitization and encryption, used in the knowledge base management method as claimed in any one of claims 1 to 7, characterized in that By generating desensitization and encryption rules from knowledge metadata, sensitive information in the storage, transmission and sharing of knowledge content is protected to prevent unauthorized access.

9. The intellectual property protection method according to claim 8, comprising: (a) Desensitization strategy module, which adjusts the desensitization level according to enterprise needs and supports encryption at different security levels; (b) Partial or global masking of sensitive information to ensure data security during knowledge sharing.

10. A knowledge base market management module, used in the knowledge base management method according to any one of claims 1 to 9, characterized in that Through permission management and auditing functions, secure sharing of knowledge resources can be achieved, ensuring efficient circulation of knowledge within the enterprise under the premise of compliance and security.

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