24 results about "Knowledge classification" patented technology

The invention discloses a multi-source information fusion retrieval method and system combined with an information credibility portrait, and belongs to the technical field of intelligent information retrieval and auxiliary decision making. The method comprises the steps that the credibility portrait is constructed according to a predefined credibility index, and multi-source knowledge classification is completed; obtaining a candidate knowledge point set corresponding to query, and dividing the candidate knowledge point set into contrast knowledge point groups for conflict detection; according to a conflict detection result, performing complementary enhancement on the contrast knowledge point group without conflicts according to multi-source knowledge classification to form a fused knowledge point set; according to multi-source knowledge classification, credibility dynamic weighted fusion and human-in-loop conflict recheck and correction are carried out on the contrast knowledge point groups containing conflicts, and after conflict resolution is completed, a fused knowledge point set is obtained; and using the fused knowledge point set as a basis to feed back a retrieval result corresponding to the query. According to the method, the credibility of retrieval and decision results can be improved by utilizing the multi-source information through credibility enhancement and conflict resolution of the multi-source information.

This application relates to a method, apparatus, device, and medium for constructing a test set based on a multimodal knowledge tree. The method includes: acquiring industry seed prompts, text data, and scene data; constructing a hierarchical industry knowledge tree and a seed question-and-answer set mapped to the industry knowledge tree based on the industry seed prompts, text data, and scene data; performing knowledge classification-guided instruction evolution processing on the seed question-and-answer set based on the industry knowledge tree to generate an evolved question-and-answer set, and attaching data traceability information to each question and answer in the evolved question-and-answer set; setting verification interruption points after the generation of the industry knowledge tree and after the instruction evolution processing, and verifying the industry knowledge tree and the evolved question-and-answer set according to manual verification instructions to obtain the target multimodal benchmark test set. This application improves the efficiency of benchmark test set construction, eliminates the need for manual data construction, reduces construction costs, and enables the construction of large-scale multimodal benchmark test sets.

The application provides a micro-class teaching method and system, wherein the micro-class teaching method comprises the following steps: constructing a knowledge classification database; obtaining the demand of a target object and determining the course content according to the demand of the target object; carrying out knowledge point keyword extraction and scoring on the course content, forming an index item with scores and adding the index item to the knowledge classification database, and establishing the association between the knowledge classification database and the teaching courseware; obtaining the target explanation knowledge point from the course content and obtaining the first teaching information, the second teaching information, the third teaching information and the fourth teaching information according to the target explanation knowledge point; and sending the final teaching information to a specified position after obtaining the final teaching information according to the first teaching information, the second teaching information, the third teaching information and the fourth teaching information. The application can meet the demand of students in different situations and improve the learning efficiency of students.

This invention discloses a method and system for generating real-time content in a specific domain based on AI technology, belonging to the field of artificial intelligence technology. The method includes the following steps: 1. Constructing a local material knowledge base RAG_src in the form of word vector storage using AI technology, whose materials include multimodal materials such as text, voice, audio / video, or H5; 2. Constructing a local domain knowledge classification knowledge base RAG_policy in the form of file storage using AI technology, whose content includes domain specification documents and technical classification documents categorized by domain knowledge and information; 3. Constructing PageIndex by combining a Large Language Model (LLM) with RAG_policy; 4. Receiving content generation requests from real-time software, calling RAG_src and PageIndex through LLM to generate real-time content that meets the requirements of the corresponding scenario; 5. Checking the generated content and outputting compliant content. This invention solves the problems of non-standard content generation, disconnection from relevant standards, low reuse rate of proprietary materials, and low retrieval efficiency in existing technologies, achieving accurate, efficient, and real-time content generation. It is adaptable to various scenarios such as real-time content generation, content compliance checking, and real-time domain assistance in specific domains. Application scenarios include, but are not limited to, oral practice and oral examinations according to teaching syllabi.

The invention discloses a dynamic construction method and device for fusing a hypertension prevention and treatment knowledge system based on double theories, can systematically solve the core problems of knowledge organization, classification and iteration, and constructs a hypertension prevention and treatment knowledge system with theoretical support, precise classification and dynamic iteration by fusing Bauer double view and Anderson four-level classification theories. The method comprises the following steps: (1) performing knowledge double-view disassembly based on a Bauer health development model; (2) performing knowledge four-level classification mapping based on an Anderson knowledge classification theory; and (3) constructing a knowledge system dynamic update closed loop.

The invention discloses a water conservancy knowledge base and intelligent question and answer construction method based on a large model and RAG, and the method comprises the steps: 1, combing a water conservancy knowledge system, and sorting water conservancy professional documents and data as materials of the knowledge base; step 2, constructing a water conservancy knowledge library management system through a J2EE system, a B / S architecture and a distributed object storage system; step 3, based on the RAG technology, forming different knowledge bases according to the sorted knowledge classification and confidentiality security level of the water conservancy industry, and establishing a water conservancy knowledge intelligent question-answering system on the knowledge bases; 4, performing extended application on the basis of the constructed water conservancy knowledge intelligent question-answering system; the problems that in the prior art, due to the fact that unstructured data processing is weak when a water conservancy knowledge base is constructed, a large number of image-text mixed documents such as PDF and PPT cannot be effectively utilized, and the recall rate and the accuracy rate are not high are solved.

1. Name of the product in this design: Graphical User Interface (Business Knowledge Guide) for Water Resources Business Analysis and Management System for Electronic Equipment. 2. Purpose of this design: An electronic device. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Uses of the graphical user interface: for building a learning system for project-related knowledge, evaluating work statistics, compiling and querying information on honors and awards, and assisting in work. 6. Human-computer interaction method of graphical user interface: Click "Knowledge Map" in the upper left corner of the main view to enter the interface change state diagram 1, which can view mind maps of various knowledge categories. Clicking "Add Knowledge Category" on the upper right of the interface change state diagram 1 will take you to the interface change state diagram 2. This interface allows you to add a root node. By entering information such as "Knowledge Title", "Sorting" and "Knowledge Content Type", you can add a new knowledge category. In the interface, "XXX" represents text, numbers, letters, or symbols.

The application relates to the technical field of education training, and discloses a training subject management method and system based on knowledge classification. The system comprises a knowledge classification unit, a knowledge evaluation unit and a matching management unit. Through unique algorithms and technologies, knowledge is accurately divided into three types, namely, basic knowledge, advanced knowledge and application knowledge. For example, basic knowledge is compared by using a keyword library, advanced knowledge is constructed to calculate a correlation degree, and application knowledge is analyzed in combination with actual cases, thereby laying a solid foundation for subsequent training. A standardized evaluation process is designed for different types of knowledge, and the knowledge level of a trainer can be comprehensively understood. Different recommendation strategies are formulated according to the level of each knowledge type of the trainer, for example, excellent basic knowledge is focused on advanced and application knowledge, and large projects are recommended for excellent application knowledge, so that the learning needs of different trainers can be met, the problem of mismatched learning content is avoided, the training effect and efficiency are improved, and the knowledge and application ability of the trainer is more efficiently improved.

The invention provides a zero sample curriculum knowledge extraction and filtering method and device and electronic equipment, and the method comprises the steps: carrying out the blocking processing of a text of a to-be-processed teaching material, extracting knowledge points, obtaining a knowledge point hierarchical structure, carrying out the filtering of the knowledge points in the knowledge point hierarchical structure through reasoning judgment, and obtaining a zero sample curriculum knowledge extraction and filtering result. Obtaining a filtered knowledge point hierarchical structure; and based on a predefined knowledge type, classifying and marking the knowledge points in the filtered knowledge point hierarchical structure. According to the method, the integrity of the context is ensured through an iterative semantic partitioning strategy, a large model is guided to extract knowledge points of a hierarchical structure from a teaching perspective, low-quality contents are effectively filtered, and the teaching expressive power of a graph structure is further enhanced through semantic knowledge classification.

The present disclosure provides a question analysis method and device, electronic equipment and storage medium, relates to the technical field of Internet, and particularly relates to the field of knowledge graph. The specific implementation scheme is: obtaining a question input by a user; performing knowledge classification on the question according to a pre-constructed classification type to obtain classification information of the question; performing knowledge understanding on the question to obtain subject segment understanding information and predicate segment understanding information of the question; and generating final analysis information of the question based on the subject segment understanding information and the predicate segment understanding information and the classification information. The embodiments of the present application can provide a general analysis scheme for knowledge class Query, can be applied in different scenes with knowledge class demand, and have stronger explainability and higher accuracy.

The present application relates to the technical field of computer sentiment analysis, and particularly relates to a layered noise injection based on dynamic sensitivity driven association sentiment knowledge model construction method. It comprises the following steps: downloading the model to the local client; obtaining the local client user emotion entity sensitivity classification; injecting noise into the information input in the local client; training the downloaded local client emotion knowledge classification model; updating the server emotion knowledge classification model. Through the fusion of semantic correlation, social influence weight and data sparsity and other multi-dimensional features, the present application realizes the fine sensitivity classification of different emotional data. The method effectively solves the problem that the traditional unified noise injection mechanism cannot adapt to the sensitivity difference of emotional data. Users do not need to rely on complex privacy risk assessment experience, and can configure the optimal privacy protection strategy for different types of emotional data according to the sensitivity classification results generated by the system.

The application discloses a decision auxiliary question and answer method based on subdivided industry knowledge classification and an open source large model, comprising the following steps: 1, constructing an industry knowledge classification method; 2, classifying and vectorizing and storing classified knowledge of multi-source heterogeneous data; 3, similarity matching of classified auxiliary query and knowledge; 4, combining matched industry knowledge and decision auxiliary query into prompt and inputting the prompt into multiple open source large models to obtain corresponding decision auxiliary answers. The application combines multiple methods to construct a subdivided industry knowledge classification method, can utilize more fine-grained industry knowledge to improve the effect of decision auxiliary answers of the large model, simultaneously utilizes a text quality scoring model to sort and display answers of multiple open source large models, fully gives play to respective advantages of different large models, and thus improves the rationality and accuracy of decision auxiliary answers.

The application discloses a kind of based on big model's multimodal knowledge automatic classification and retrieval method and system, it is related to knowledge classification retrieval technical field, including to knowledge fragment is classified, obtains knowledge classification data;Retrieval text is input in platform by user, the interredundancy degree between different candidate knowledge categories of retrieval text is analyzed, the retrieval fit degree of different knowledge fragments in different target knowledge categories to retrieval text is analyzed, and marked retrieval data is obtained;The retrieval quality of marked knowledge fragment in target knowledge category to retrieval text is evaluated, and target retrieval data is obtained;According to target retrieval data, the retrieval answer of retrieval text is generated, and is output to user through platform, so that last generation retrieval answer not only with the content of user's retrieval text content is fit, and also makes different modal knowledge data effectively combined, greatly improve the quality of generated retrieval answer.

The invention discloses a knowledge classification acquisition method, device and system, and a storage medium. The method comprises the following steps: acquiring and preprocessing original education data; extracting a core knowledge point set; analyzing the hierarchy, preposition or correlation between the knowledge points, and generating a relation tuple; and constructing a structured knowledge system based on the knowledge point set and the relation tuple, and executing consistency verification and correction updating. According to the technical scheme, the problems that traditional educational knowledge system construction depends on manual arrangement, efficiency is low, subjectivity is high, updating is difficult, and association mining is insufficient are solved.

The invention discloses a knowledge classification method and device. The method comprises the following steps: acquiring a geographic information ontology conceptual model; processing the geographic information ontology conceptual model to obtain an ontology knowledge set; the ontology knowledge comprises N pieces of ontology knowledge information, and each piece of ontology knowledge information comprises an ontology knowledge title and ontology knowledge content; and processing the ontology knowledge set to obtain a knowledge classification result. According to the method, the ontology knowledge set is extracted and constructed from the geographic information ontology concept model, and the obtained ontology knowledge title and content naturally bear the core concept, attribute association and hierarchical logic of the geographic information field, so that the subsequent classification result is ensured to completely accord with the internal system of the geographic information knowledge; the problems of disordered classification logic and poor field adaptability caused by knowledge source mixing of a traditional classification method are solved.

Techniques for generating knowledge-adapted content based on a knowledge classification of a user are disclosed. A system determines that a trigger condition is satisfied for requesting a knowledge-adapted content element for augmenting information for display on a user interface. In response to determining that the trigger condition is satisfied, the system generates, in real time, an input prompt element for requesting the knowledge-adapted content element and directs the input prompt element to a machine learning (ML) model to generate the knowledge-adapted content element. The knowledge-adapted content element includes machine-generated content pertaining to the target concept. The system receives the knowledge-adapted content element from the ML model and augments the information at least by concurrently displaying the information and machine-generated content on the user interface.

The application discloses a content analysis method and device, an electronic device and a computer readable storage medium. Embodiments of the application determine at least two learning contents to be compared, compare and analyze the learning contents in at least one specified content expression dimension, and obtain a comparison and analysis result. The learning contents are the answer results of at least one target user in the same period or different periods, and the content expression dimension is a knowledge classification perspective included in the learning contents. The embodiments of the application can improve the accuracy of analyzing the learning contents.

The invention provides a knowledge graph-based knowledge classification, conflict resolution and knowledge fusion method, which comprises the following steps of: preprocessing a new strategy branch to obtain a new strategy branch vector, and generating a strategy branch confidence coefficient; determining a to-be-processed similar strategy branch list in a retrieval range by utilizing an expression of cosine similarity according to the new strategy branch vector and a similarity threshold; performing condition intersection solution and action judgment on each similar strategy branch in the to-be-processed similar strategy branch list to obtain a corresponding judgment result; executing a corresponding conflict resolution strategy or fusion evolution strategy according to a judgment result to generate a final strategy branch and storing the final strategy branch into a knowledge base; and checking and storing the final strategy in sequence to obtain an updated strategy branch, so that when the event occurs again, the updated strategy branch is called to form a self-adaptive closed loop. Therefore, the fusion efficiency is improved, the possibility that high-quality rules are adopted is improved, the interpretability of the method is high, and the traceability of fusion is high.

The present disclosure discloses a knowledge extraction method, system, device and storage medium. The knowledge extraction method comprises: obtaining and processing text data describing an object to determine a plurality of sentences and an object category corresponding to each sentence; generating a syntax relationship template for each sentence, the syntax relationship template comprising the part of speech of each word and the dependency relationship between the words; generating a syntax tree corresponding to each sentence through syntax analysis; matching the syntax relationship template and the syntax tree, and if the match is consistent, generating a candidate knowledge using the matched syntax subtree; using a knowledge prediction model to predict whether the candidate knowledge is correct knowledge; and using a knowledge classification model to process the predicted correct knowledge to identify the knowledge category corresponding to each piece of knowledge. The knowledge extracted according to the present scheme has the characteristics of low redundancy and high accuracy.

The invention relates to the technical field of aviation intelligence intelligent processing and knowledge mapping, in particular to a digital navigation intelligence knowledge classification and structured analysis evaluation method, which comprises the following steps: establishing a navigation announcement data set based on historical navigation announcement messages, each sample of the navigation announcement data set comprising an instruction, an announcement text, an entity and an event; performing entity-event joint extraction on the announcement text of the sample to obtain an entity and event predicted value corresponding to the announcement text; constructing a knowledge graph based on the entity and event prediction value of the sample; an entity-event quality evaluation model is established, and the entity-event quality evaluation model can perform weighted evaluation on a plurality of corresponding evaluation indexes according to the entity and event predicted values to obtain a quality score; obtaining a quality score of the notification text to be evaluated; according to the method, the generalization ability and accuracy of navigation announcement analysis can be improved.

The application discloses a kind of implicit knowledge explicitization method for tobacco enterprise production scheduling, comprising the following steps: step S1: extraction and pre-processing scheduling knowledge;Step S2: build implicit and explicit scheduling knowledge classification word library;Step S3: key word extraction is carried out to implicit knowledge and explicit knowledge, the similarity of key word of the two is compared using n-gram matching algorithm, if the similarity m of the two is greater than or equal to the set threshold n, then according to the explicit knowledge, construct scheduling knowledge framework, combine scheduling knowledge framework to carry out knowledge integration to implicit knowledge key word set, complete implicit knowledge explicitization;Step S4: integrate implicit and explicit scheduling knowledge;Based on explicit knowledge, directly adopt OWL language and with the aid of Protege ontology development tool, top-down scheduling ontology is built;Through ontology mapping and ontology fusion method, the semantic integration between scheduling IS semantic model and scheduling ontology is realized, and the integration of implicit and explicit scheduling knowledge is completed.

The invention discloses a content analysis method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: determining at least two to-be-compared learning contents, carrying out the comparative analysis of each learning content in at least one specified content expression dimension, and obtaining a comparative analysis result, the learning content is an answer result of at least one target user in the same period or different periods, and the content expression dimension is a knowledge classification view included in the learning content. According to the embodiment of the invention, the accuracy of analyzing the learning content can be improved.

The invention relates to a test set construction method and device based on a multi-modal knowledge tree, equipment and a medium, and the method comprises the steps: obtaining industry seed prompts, text data and scene data, and constructing a hierarchical industry knowledge tree and a seed question and answer set mapped to the industry knowledge tree based on the industry seed prompts, the text data and the scene data; performing instruction evolution processing guided by a knowledge classification method on the seed question and answer set based on the industry knowledge tree, generating an evolved question and answer set, and adding data traceability information to each question and answer in the evolved question and answer set; and setting a verification interruption point after the generation of the industry knowledge tree and the instruction evolution processing, and verifying the industry knowledge tree and the evolved question and answer set according to a manual verification instruction to obtain a target multi-modal benchmark test set. According to the method, the benchmark test set construction efficiency is improved, manual data construction is not needed, the construction cost is reduced, and a large-scale multi-modal benchmark test set can be constructed.

The invention discloses an intelligent knowledge retrieval method and system oriented to a basic part manual, and belongs to the technical field of industrial knowledge retrieval. The method comprises the following steps: constructing a professional knowledge meta-model according to obtained professional knowledge of a basic file; wherein the professional knowledge meta-model comprises basic part knowledge classification, cross-standard equal-order mapping and a knowledge dependence graph; obtaining a basic part manual, and determining manual coordinate information of the basic part manual; establishing a mapping relation between chapter content of the basic part manual and basic part knowledge classification, and marking difference reasons of manual content of the same basic part knowledge classification; analyzing the chapter content to obtain analyzed content, and marking a mapping relation and manual coordinate information for the analyzed content to obtain knowledge atoms; analyzing to obtain user query information and executing retrieval to obtain an initial knowledge set; and generating an initial answer according to the initial knowledge set, and performing iterative optimization on the initial answer to obtain a target retrieval result.