Multi-dimensional knowledge base management method and system associated with intelligent labels

Abstract

The invention discloses a multi-dimensional knowledge base management method and system associated with intelligent tags, and belongs to the technical field of knowledge base data management. Standard documents are analyzed through configuration rules, and semantic tags are generated; for non-standard documents and unstructured data, keywords are intelligently extracted according to the documents or user description to generate semantic tags; based on the knowledge element labels, a multi-dimensional knowledge graph is created, and intelligent association retrieval of the heterogeneous knowledge resources is achieved. According to the method, various documents can be managed in a unified manner, the human input in a knowledge base collection process is remarkably simplified, and the implementation cost is greatly reduced; and associated retrieval is supported, so that the efficiency and practicability of knowledge management are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of knowledge base data management, and in particular to a multi-dimensional knowledge base management method and system associated with intelligent tags. Background Art

[0002] In today's era of information explosion, the construction and development of knowledge management systems are crucial for all kinds of organizations and institutions. However, existing knowledge management systems face many challenges that need to be addressed. Among them, the most prominent problems are the serious phenomenon of resource islands and the lack of knowledge structure. Traditional document management tools, such as network disks, shared documents, local hard disks, etc., although they can store various types of files, have obvious deficiencies in knowledge management at the clause level. Taking standard documents such as laws and regulations, industry standards, and national policies as examples, these tools cannot achieve refined management of document clauses. For unstructured data such as pictures and videos, their relevance to text clauses is highly dependent on manual annotation, which is not only inefficient, but also unable to dynamically adapt to differences in document types, resulting in a lack of multi-dimensional association networks between knowledge elements. Therefore, it is of great practical significance to develop a method that can uniformly manage various types of documents and support associated retrieval. Summary of the Invention

[0003] The technical task of the present invention is to address the above shortcomings and provide a multi-dimensional knowledge base management method and system with intelligent tag association, which can uniformly manage various types of documents, significantly simplify the manpower input in the knowledge base collection process, and greatly reduce the implementation cost; and support associated retrieval, greatly improving the efficiency and practicality of knowledge management.

[0004] The technical solution adopted by the present invention to solve its technical problem is:

[0005] A multi-dimensional knowledge base management method with intelligent tag association. By configuring rules, it parses standard documents and generates semantic tags. For non-standard documents and unstructured data, it intelligently extracts keywords based on the document itself or user descriptions to generate semantic tags.

[0006] Based on the labels of various knowledge elements, a multi-dimensional knowledge graph is created to realize intelligent associative retrieval of heterogeneous knowledge resources.

[0007] Through the configured rules, this method can realize the collection of clauses of standard documents (such as laws, regulations, industry standards, etc.) and generate semantic tags. For non-standard documents and unstructured data, keywords can be intelligently extracted based on the document itself or user descriptions to generate semantic tags. This innovative method can significantly simplify the manpower input in the knowledge base collection process and greatly reduce the implementation cost, thereby providing strong support for the expansion of the scale of knowledge base construction. In addition, this method can establish the correlation between various knowledge elements through tags, thereby supporting cross-media type knowledge association mapping and creating a multi-dimensional knowledge graph. When searching, users can obtain multi-dimensional, accurate and comprehensive results, which greatly improves the efficiency and practicality of knowledge management.

[0008] Furthermore, the implementation of the method includes the following steps:

[0009] Step 1: Configure corresponding parsing rules based on the format and structural characteristics of the standard document to provide a basis for subsequent document parsing;

[0010] Step 2: After uploading the standard document, parse the document according to the rules configured in step 1, generate tags, and finally store the parsed structured data in the database;

[0011] Step 3: When uploading non-standard documents, the algorithm filters out high-frequency words in the document as tags. The tags and document content are combined to generate structured data and stored in the database.

[0012] Step 4: For unstructured data, including images and videos, the user enters a description. Keywords are extracted based on the user's description as tags, and an interactive tag modification interface is provided. The modified unstructured data is stored in a high-performance distributed object storage system for subsequent retrieval and management.

[0013] Step 5: Integrate each knowledge element and its corresponding label into Elasticsearch to achieve fast retrieval and query of data in the knowledge base, providing users with a convenient way to acquire knowledge;

[0014] Step 6: Based on the labels of each knowledge element, construct an entity-relationship-attribute knowledge graph and store it in the graph database;

[0015] Step 7: Users can search for knowledge using keywords. The system uses Elasticsearch full-text search and combines it with the knowledge graph to display multi-dimensional results. It supports various filtering and sorting functions to achieve accurate retrieval and knowledge association display.

[0016] Furthermore, in step 1, corresponding parsing rules are configured according to the format and structural characteristics of the standard document, including the naming method of chapters and item titles.

[0017] Furthermore, in step 2, the document is parsed according to the rules configured in step 1, and the specific implementation is as follows:

[0018] Automatically identify titles, split chapters, extract entry content, and extract keywords as tags through intelligent algorithms. It also provides an interactive tag correction interface, allowing users to review and correct generated tags to ensure their accuracy and completeness.

[0019] The step 3 also provides an interactive tag correction interface, allowing users to adjust and optimize the extracted tags. The corrected tags and document content are combined to generate structured data and stored in the database.

[0020] Furthermore, the standard documents support multiple common formats including word, pdf, and txt, including laws and regulations, national / industry standards, and policy documents.

[0021] Furthermore, the keyword extraction is achieved by relying on advanced word segmentation algorithms including TF-IDF, mutual information, maximum entropy model, Word2Vec, etc., to ensure the accuracy and effectiveness of keyword extraction.

[0022] Furthermore, the creation of a multi-dimensional knowledge graph relies on high-performance graph databases including ArangoDB to efficiently store and manage knowledge graph data and support complex association queries.

[0023] The present invention also claims protection for a multi-dimensional knowledge base management system associated with smart tags, comprising:

[0024] The standard document parsing module is used to parse standard documents and generate semantic tags based on configuration rules;

[0025] The non-standard document parsing module intelligently extracts keywords and generates semantic tags based on the document itself or user description for non-standard documents and unstructured data;

[0026] The retrieval module creates a multi-dimensional knowledge graph based on the labels of various knowledge elements, and realizes intelligent and associated retrieval of heterogeneous knowledge resources;

[0027] The system specifically implements multi-dimensional knowledge base management through the above method.

[0028] The present invention also claims protection for a multi-dimensional knowledge base management device associated with smart tags, comprising: at least one memory and at least one processor;

[0029] The at least one memory is configured to store a machine-readable program;

[0030] The at least one processor is configured to call the machine-readable program to implement the above method.

[0031] The present invention also claims protection for a computer-readable medium having computer instructions stored thereon, which are capable of implementing the above method when executed by a processor.

[0032] Compared with the prior art, the multi-dimensional knowledge base management method and system for smart tag association of the present invention has the following beneficial effects:

[0033] 1. The present invention realizes the automatic item parsing of standard documents, reduces the knowledge maintenance cost, improves the knowledge maintenance efficiency, reduces the tedious work of manual intervention, and improves the automation level of knowledge management.

[0034] 2. This invention achieves keyword extraction from text. Using a word segmentation algorithm, it intelligently extracts keywords from text as tags, thereby establishing relationships between knowledge elements. This intelligent tag extraction method not only improves the accuracy of tag generation but also automatically discovers potential connections between knowledge elements, providing a foundation for in-depth knowledge mining and application.

[0035] 3. The present invention realizes full-text retrieval of knowledge and display of relationship graphs. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a diagram illustrating a multi-dimensional knowledge base management method associated with smart tags provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The embodiment of the present invention provides a multi-dimensional knowledge base management method with intelligent tag association. By configuring rules, standard documents are parsed and semantic tags are generated. For non-standard documents and unstructured data, keywords are intelligently extracted based on the document itself or user description to generate semantic tags.

[0038] Based on the labels of various knowledge elements, a multi-dimensional knowledge graph is created to realize intelligent associative retrieval of heterogeneous knowledge resources.

[0039] Among them, the standard documents support multiple common formats such as word, pdf, txt, etc., including laws and regulations, national / industry standards, policy documents, etc.

[0040] The keyword extraction is achieved by relying on advanced word segmentation algorithms such as TF-IDF, mutual information, maximum entropy model, Word2Vec, etc., to ensure the accuracy and effectiveness of keyword extraction.

[0041] The creation of a multi-dimensional knowledge graph relies on high-performance graph databases such as ArangoDB, which can efficiently store and manage knowledge graph data and support complex association queries.

[0042] The retrieval relies on full-text retrieval tools such as Elasticsearch to achieve fast and accurate full-text retrieval functions, meeting users' demand for efficient knowledge retrieval.

[0043] Combined with attachment Figure 1 As shown, the implementation of this method includes the following specific steps:

[0044] Step 1: Configure corresponding parsing rules based on the format and structural characteristics of the standard document, such as the naming method of chapters and item titles, to provide a basis for subsequent document parsing.

[0045] Step 2: After uploading the standard document, the document is parsed according to the rules configured in step 1. The title is automatically identified, the chapters are split, the entry content is extracted, and keywords are extracted as tags through intelligent algorithms. At the same time, an interactive tag correction interface is provided, allowing users to review and correct the generated tags to ensure the accuracy and completeness of the tags. Finally, the parsed structured data is stored in the database.

[0046] Step 3: When uploading non-standard documents, the algorithm filters out high-frequency words in the document as tags. An interactive tag correction interface is also provided, allowing users to adjust and optimize the extracted tags. The corrected tags are generated together with the document content to generate structured data and stored in the database.

[0047] Step 4: For unstructured data such as images and videos, users can enter a description. Keywords are extracted based on the user-entered description as tags, and an interactive tag modification interface is provided. The modified unstructured data is stored in a high-performance distributed object storage system for subsequent retrieval and management.

[0048] Step 5: Integrate each knowledge element and its corresponding label into Elasticsearch to achieve fast retrieval and query of data in the knowledge base, providing users with a convenient way to acquire knowledge.

[0049] Step 6: Based on the labels of each knowledge element, construct an entity-relationship-attribute knowledge graph and store it in the graph database.

[0050] Step 7: Users can search for knowledge using keywords. The system uses Elasticsearch full-text search and combines it with the knowledge graph to display multi-dimensional results. It supports various filtering and sorting functions to achieve accurate retrieval and knowledge association display.

[0051] This method parses standard documents through rule configuration and extracts various text keywords using a word segmentation algorithm. This method then establishes relationships between knowledge elements, creates a multidimensional knowledge graph, and implements intelligent, associative retrieval of heterogeneous knowledge resources through a tag network. Specifically, parsing rules are configured based on the format and structural characteristics of standard documents to accurately segment standard document entries. A word segmentation algorithm is used to extract keywords and form tags based on the entry content, non-standard document content, and unstructured data descriptions. The parsed data and generated tags are then integrated into Elasticsearch. Finally, relationships are established based on the tags of each knowledge element and stored in a graph database.

[0052] The configuration rules described in this method can be flexibly adjusted according to actual needs to adapt to different standard document situations; the parsing entries, extracting keywords, and knowledge integration and storage processes described in this method can be implemented through software programs and can be integrated into existing information systems for use; the knowledge management function described in this method supports users to maintain various knowledge elements and their labels, and can update, modify and optimize the content in the knowledge base according to actual conditions to ensure the timeliness and accuracy of the knowledge base; this method also provides complete configuration management, monitoring and auditing functions to ensure the security, reliability and traceability of the knowledge processing process.

[0053] Through the configured rules, this method can realize the collection of clauses of standard documents (such as laws, regulations, industry standards, etc.) and generate semantic tags. For non-standard documents and unstructured data, keywords can be intelligently extracted based on the document itself or user descriptions to generate semantic tags. This innovative method can significantly simplify the manpower input in the knowledge base collection process and greatly reduce the implementation cost, thereby providing strong support for the expansion of the scale of knowledge base construction. In addition, this method can establish the correlation between various knowledge elements through tags, thereby supporting cross-media type knowledge association mapping and creating a multi-dimensional knowledge graph. When searching, users can obtain multi-dimensional, accurate and comprehensive results, which greatly improves the efficiency and practicality of knowledge management.

[0054] The embodiment of the present invention further provides a multi-dimensional knowledge base management system associated with smart tags, including:

[0055] The standard document parsing module is used to parse standard documents and generate semantic tags through configuration rules; standard documents support multiple common formats such as word, pdf, txt, etc., including laws and regulations, national / industry standards, policy documents, etc.

[0056] The non-standard document parsing module intelligently extracts keywords and generates semantic tags for non-standard documents and unstructured data based on the document itself or user descriptions. This keyword extraction relies on advanced word segmentation algorithms such as TF-IDF, mutual information, maximum entropy model, and Word2Vec to ensure the accuracy and effectiveness of keyword extraction.

[0057] The retrieval module creates a multidimensional knowledge graph based on the tags of various knowledge elements, enabling intelligent, associative retrieval of heterogeneous knowledge resources. This multidimensional knowledge graph relies on high-performance graph databases such as ArangoDB to efficiently store and manage knowledge graph data and support complex associative queries. Retrieval relies on full-text search tools such as Elasticsearch, enabling fast and accurate full-text search, meeting users' demand for efficient knowledge retrieval.

[0058] The system implements multi-dimensional knowledge base management through the multi-dimensional knowledge base management method associated with smart tags described in the above embodiment. The specific steps are as follows:

[0059] 1. According to the format and structural characteristics of the standard document, configure corresponding parsing rules, such as the naming method of chapters and item titles, to provide a basis for subsequent document parsing.

[0060] 2. After uploading the standard document, the document is parsed according to the rules configured in step 1. The title is automatically identified, the chapters are split, the entry content is extracted, and keywords are extracted as tags through intelligent algorithms. At the same time, an interactive tag correction interface is provided, allowing users to review and correct the generated tags to ensure the accuracy and completeness of the tags. Finally, the parsed structured data is stored in the database.

[0061] 3. When uploading non-standard documents, the algorithm filters out high-frequency words in the document as tags. An interactive tag correction interface is also provided, allowing users to adjust and optimize the extracted tags. The corrected tags and document content are combined to generate structured data and stored in the database.

[0062] 4. For unstructured data such as images and videos, users can enter a description. Keywords are extracted based on the description and used as tags. An interactive tag modification interface is provided. The modified unstructured data is stored in a high-performance distributed object storage system for subsequent retrieval and management.

[0063] 5. Integrate each knowledge element and its corresponding label into Elasticsearch to achieve fast retrieval and query of data in the knowledge base, providing users with a convenient way to acquire knowledge.

[0064] 6. Based on the labels of each knowledge element, construct an entity-relationship-attribute knowledge graph and store it in the graph database.

[0065] 7. Users can search for knowledge using keywords. The system uses Elasticsearch full-text search and combines it with the knowledge graph to display multi-dimensional results. It supports various filtering and sorting functions to achieve accurate retrieval and knowledge association display.

[0066] An embodiment of the present invention further provides a multi-dimensional knowledge base management device associated with smart tags, comprising: at least one memory and at least one processor;

[0067] The at least one memory is configured to store a machine-readable program;

[0068] The at least one processor is configured to call the machine-readable program to implement the multi-dimensional knowledge base management method associated with smart tags described in the above embodiment.

[0069] An embodiment of the present invention further provides a computer-readable medium having computer instructions stored thereon, which, when executed by a processor, causes the processor to execute the multi-dimensional knowledge base management method associated with smart tags described in the above embodiments. Specifically, a system or device equipped with a storage medium can be provided, on which software program code implementing the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or device can be caused to read and execute the program code stored in the storage medium.

[0070] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute part of the present invention.

[0071] Examples of storage media for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, the program code can be downloaded from a server computer via a communication network.

[0072] In addition, it should be clear that the functions of any of the above embodiments can be achieved not only by executing the program code read by the computer, but also by enabling the operating system operating on the computer to complete part or all of the actual operations based on the instructions of the program code.

[0073] In addition, it can be understood that the program code read from the storage medium is written into the memory provided in the expansion board inserted into the computer or into the memory provided in the expansion unit connected to the computer, and then based on the instructions of the program code, the CPU installed on the expansion board or expansion unit is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above embodiments.

[0074] The present invention has been shown and described in detail above through the accompanying drawings and preferred embodiments. However, the present invention is not limited to these disclosed embodiments. Based on the above multiple embodiments, those skilled in the art can know that the code review methods in the above different embodiments can be combined to obtain more embodiments of the present invention, and these embodiments are also within the scope of protection of the present invention.

Claims

1. A multi-dimensional knowledge base management method associated with smart tags, characterized by: By configuring rules, standard documents are parsed and semantic tags are generated. For non-standard documents and unstructured data, keywords are intelligently extracted based on the document itself or user descriptions to generate semantic tags. Based on the labels of various knowledge elements, a multi-dimensional knowledge graph is created to realize intelligent associative retrieval of heterogeneous knowledge resources.

2. The multi-dimensional knowledge base management method for smart tag association according to claim 1 is characterized in that: The implementation of this method includes the following steps: Step 1: Configure corresponding parsing rules based on the format and structural characteristics of the standard document to provide a basis for subsequent document parsing; Step 2: After uploading the standard document, parse the document according to the rules configured in step 1, generate tags, and finally store the parsed structured data in the database; Step 3: When uploading non-standard documents, the algorithm filters out high-frequency words in the document as tags. The tags and document content are combined to generate structured data and stored in the database. Step 4: For unstructured data, including images and videos, users enter a description. Keywords are extracted based on the description as tags, and an interactive tag modification interface is provided. The modified unstructured data is stored in a high-performance distributed object storage system for subsequent retrieval and management. Step 5: Integrate each knowledge element and its corresponding label into Elasticsearch to achieve fast retrieval and query of data in the knowledge base; Step 6: Based on the labels of each knowledge element, construct an entity-relationship-attribute knowledge graph and store it in the graph database; Step 7: Users can search for knowledge using keywords. The system uses Elasticsearch full-text search and combines it with the knowledge graph to display multi-dimensional results. It supports various filtering and sorting functions to achieve accurate retrieval and knowledge association display.

3. The multi-dimensional knowledge base management method for smart tag association according to claim 2 is characterized in that: In step 1, corresponding parsing rules are configured according to the format and structural characteristics of the standard document, including the naming method of chapters and item titles.

4. A multi-dimensional knowledge base management method associated with smart tags according to claim 2 or 3, characterized in that: Step 2, parsing the document according to the rules configured in step 1, is specifically implemented as follows: Automatically identify titles, split chapters, extract entry content, and extract keywords as tags through intelligent algorithms. It also provides an interactive tag correction interface, allowing users to review and correct generated tags to ensure their accuracy and completeness. The step 3 also provides an interactive tag correction interface, allowing users to adjust and optimize the extracted tags. The corrected tags and document content are combined to generate structured data and stored in the database.

5. The multi-dimensional knowledge base management method associated with smart tags according to claim 1, characterized in that: The standard documents support multiple common formats including word, pdf, and txt, including laws and regulations, national / industry standards, and policy documents.

6. The multi-dimensional knowledge base management method for smart tag association according to claim 1, characterized in that: The keyword extraction is achieved by using word segmentation algorithms including TF-IDF, mutual information, maximum entropy model, and Word2Vec.

7. The multi-dimensional knowledge base management method for smart tag association according to claim 1, characterized in that: The creation of a multi-dimensional knowledge graph relies on high-performance graph databases including ArangoDB to efficiently store and manage knowledge graph data and support complex association queries.

8. A multi-dimensional knowledge base management system associated with smart tags, characterized by: include: The standard document parsing module is used to parse standard documents and generate semantic tags based on configuration rules; Non-standard document parsing module, for non-standard documents and unstructured data, intelligently extracts keywords based on the document itself or user descriptions to generate semantic tags; The retrieval module creates a multi-dimensional knowledge graph based on the labels of various knowledge elements, and realizes intelligent and associated retrieval of heterogeneous knowledge resources; The system specifically implements multi-dimensional knowledge base management through the method described in any one of claims 1 to 7.

9. A multi-dimensional knowledge base management device associated with smart tags, characterized by: include: at least one memory and at least one processor; The at least one memory is configured to store a machine-readable program; The at least one processor is configured to call the machine-readable program to implement the method according to any one of claims 1 to 7.

10. A computer-readable medium, characterized in that The computer readable medium stores computer instructions, which, when executed by a processor, can implement the method according to any one of claims 1 to 7.

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