42 results about "Knowledge organisation" patented technology

The invention relates to the technical field of electric power engineering, and discloses an electric power engineering multi-modal RAG system based on a knowledge graph and multi-Agent collaboration, and the system comprises a multi-modal dynamic knowledge base construction module which is configured to carry out the structural processing, multi-dimensional knowledge organization and dynamic optimization of electric power engineering multi-modal data; the self-adaptive retrieval strategy engine module is configured to construct a weight decision network based on deep reinforcement learning and execute multi-channel parallel retrieval and result fusion; the iterative self-reflection reasoning module is configured to generate a reasoning path in combination with the retrieval result and verify evidence validity from multiple dimensions; the MCP tool intelligent calling module is configured to integrate multiple types of standardized MCP tools; and the multi-Agent collaborative framework is configured to provide multiple types of Agents which are specific in function and have a cross-module interaction capability. According to the method, the question and answer accuracy, the reasoning depth and the result interpretability in the complex multi-modal scene of the electric power engineering can be remarkably improved.

The invention relates to the technical field of data processing, in particular to an integrated multi-mode culture resource intelligent data governance and management system. The system comprises a data acquisition module, an intelligent processing module, a data management module, a knowledge organization module and an interactive display module. Synchronous acquisition of videos, audios, images and three-dimensional point clouds is realized through camera equipment, audio acquisition equipment and a laser scanner, and cultural elements are extracted through action recognition, speech recognition model processing dialect transcription, image semantic segmentation and named entity recognition by adopting a convolutional neural network. The system realizes structured description and semantic mapping of data on the basis of non-abandoned domain ontology, and constructs a knowledge graph by means of a graph database and a graph neural network. And finally, realizing three-dimensional visual display of the cultural resources by combining a virtual reality technology. According to the method, the problems of non-abandoned multi-modal data processing splitting, insufficient semantic organization and weak display interactivity in the prior art are solved, and the digital governance level and the propagation capability of cultural resources are improved.

The invention discloses a robot generative adversarial self-imitation learning method based on large language model feedback, and belongs to the technical field of computer systems based on specific calculation models, and the method comprises the steps: (1) initializing a strategy network, a discriminator network and a demonstration pool; (2) constructing and training a large language model feedback network: obtaining a training sample of the large language model feedback network, and training the large language model feedback network according to the training sample; and (3) an iterative learning step (33): updating the strategy network according to the reward calculated by the new cost function. According to the robot generative adversarial self-imitation learning method based on large language model feedback, the advantages of the large language model in the aspects of semantic comprehension, reasoning and knowledge organization are used as a potential award priori source, and a stable award signal can be provided when the demonstration data quality is poor, so that a strategy exceeding original demonstration is learned.

The invention relates to a program design aided teaching agent system based on retrieval enhancement generation, and aims to solve the problems of knowledge fragmentation, low retrieval precision, deviation of generated content from teaching materials, insufficient teaching guidance and the like of a traditional teaching system. A knowledge unit construction technology combining hierarchical semantic segmentation and position coding, a precise matching mechanism combining two-stage hybrid retrieval and cross encoder reordering, a structured problem-document splicing fidelity technology and a multi-round progressive multi-round guide strategy based on a CDIO concept are adopted. Knowledge unit division with complete semantics is realized through an HAC-BERT + KMeans algorithm (the accuracy is improved by 28%), BM25 and dense retrieval weighted fusion (the weight is 0.4: 0.6) is combined with cross encoder reordering (NDCG at10 reaches 0.735), textbook format information (the content reuse rate is 87%) is reserved through structured splicing, and finally a complete technical scheme from knowledge organization and accurate retrieval to progressive teaching guidance is formed. And the intellectualization level and the knowledge conversion efficiency of program design teaching are obviously improved.

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to a satellite communication field data retrieval enhancement generation method and device based on a large model. The method comprises the steps of data cleaning and metadata labeling, text double-layer clustering construction, double-layer knowledge graph construction, normalized user semantic input, knowledge graph retrieval and answer optimization. According to the double-layer semantic clustering structure and the double-layer knowledge graph construction mechanism, multi-granularity semantic units in the field of satellite communication can be effectively expressed, cross-document and cross-standard knowledge organization and retrieval are supported, a large language model is introduced to complement and standardize entities and relationships, the automation degree of the knowledge graph construction process is higher, and the construction efficiency is improved. And the relation extraction quality is better. According to the method, intelligent analysis, accurate retrieval and high-quality answer generation of complex natural language query in the satellite communication field can be realized, and the method has remarkable semantic understanding ability, reasoning ability and closed-loop optimization ability.

The invention provides a method and a system for acquiring a port equipment operation and maintenance result based on scene perception. The method comprises the following steps: S1, receiving a natural language query about port equipment operation and maintenance of a user; s2, executing document offline processing and scene modeling, and establishing an association relationship between a document block and a knowledge graph; s3, dynamic planning extension of scene perception is executed, a dynamic planning state table is established to record the optimal path and score of each entity under different extension hops, a scene weight factor is utilized to guide composite score calculation, whether continuous extension can bring valuable new information or not is judged through a marginal revenue mechanism, and if yes, the dynamic planning state table is established to record the optimal path and score of each entity under different extension hops; the expansion depth is controlled through dynamic threshold attenuation; s4, executing multi-level knowledge organization, and hierarchically integrating the core spanning tree and the supplementary spanning tree to form a final knowledge context; and S5, inputting the final knowledge context into the large language model, performing automatic reasoning, and generating a port equipment operation and maintenance solution text.

The invention provides an industrial large model construction method and a storage medium. The method comprises the steps of obtaining multi-modal fusion data of an industrial field; performing scene classification on the industrial field to obtain industrial subdivided scenes; according to the multi-modal fusion data, determining a multi-modal data set of each type of industrial subdivision scene; according to the multi-modal data set, constructing a knowledge organization structure of each type of industrial subdivision scene; according to a text mode in the multi-mode data set, finely adjusting the small language model to obtain a routing center command body; the routing center command body is used for identifying a received user instruction and determining a target industrial subdivision scene confidence degree set; the target industrial subdivision scene confidence coefficient set is used for screening a target model from a combat model library by adopting an adaptive routing decision algorithm according to the target industrial subdivision scene confidence coefficient set, and generating a target answer by adopting the target model; wherein the combat model library comprises a scene model library, a standby reasoning body and a scene expert agent.

The invention discloses a printing field knowledge graph automatic construction method based on a large language model, which comprises the following steps: collecting a printing field multi-source heterogeneous document, processing the document to form a structured text block set, and taking the structured text block set as the input of the large language model; the model generates a primary abstract for each document, then performs cross-document semantic association analysis, constructs a document-level access sequence and a potential association network, and generates a secondary abstract; based on a printing field ontology mode, constructing a knowledge extraction mechanism fused with a structured prompt template, and guiding the model to perform structured knowledge extraction to form a candidate triple; and performing entity alignment, conflict resolution and multi-source knowledge fusion on the triple to form a structured knowledge graph. According to the method, end-to-end and automatic construction from the unstructured document to the structured knowledge graph is realized, the integrity, reasonability and construction efficiency of knowledge organization are remarkably improved, and core technical support is provided for printing intelligent diagnosis, process optimization and knowledge reuse.

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 provides a science and technology paper knowledge graph construction method and device based on hierarchical constraint and fusion, and belongs to the technical field of knowledge graph construction and natural language processing. The method comprises the following steps: performing chapter structure analysis on an input thesis; then defining a knowledge graph Schema specification oriented to scientific and technical literatures; guiding knowledge extraction of other chapters by adopting a top-down hierarchical constraint strategy, taking the abstract sub-graph as a global constraint and taking the abstract sub-graph and the citation sub-graph as double constraints; fusing the sub-graphs layer by layer according to the set membership of the chapter tree by adopting a hierarchical fusion strategy from bottom to top, and generating a paper-level knowledge graph through entity alignment and relation merging; and finally, graph quality detection and knowledge completion are executed. Hierarchical structure information of the papers is fully utilized, global consistency of knowledge extraction is guaranteed through a constraint mechanism, integrity, accuracy and credibility of knowledge graph construction of the science and technology papers are effectively improved, and the method is suitable for knowledge organization and management of large-scale science and technology literatures.

The invention relates to the technical field of artificial intelligence technology application, in particular to an electric power knowledge question-answering system and method based on large language model generation, and the system comprises the steps: constructing an electric power knowledge graph and double indexes through a question-answering knowledge base; the query rewriting module analyzes an original query of a user and outputs a standard rewriting query; the knowledge retrieval module performs double recall rearrangement and outputs a Top-K result; the content generation module generates answers, and closed-loop optimization is carried out after reflection evaluation. The method comprises the steps of constructing an electric power knowledge graph and double indexes, rewriting original queries based on a large language model, obtaining Top-K results through double recall and rearrangement, and generating and optimizing answers in an inverse mode. The problems that in an existing electric power knowledge question-answering technology, retrieval semantic understanding is weak, the professional degree and the reasoning ability of a basic language model are insufficient, and knowledge organization lacks a systematic structure are solved.

The invention relates to the technical field of natural language processing, in particular to a construction method of a text knowledge base, computer equipment and a program product. The invention provides a method for constructing a text knowledge base. The method comprises the following steps: extracting a first-level title of an original text through a large language model; segmenting the original text based on the first-level titles to obtain first-level texts corresponding to the first-level titles in number; performing parallel progressive segmentation on the first-level text which does not meet a preset segmentation termination condition, and stopping segmentation until the segmented sub-text meets the preset segmentation termination condition; and constructing a multi-level text structure tree based on the main title of the original text and all the segmented first-level texts and sub-texts. According to the method, layer-by-layer text structure recognition and recursive construction of the multi-layer text structure tree are achieved, the original logic hierarchy of the original text is reserved, the problems of semantic segmentation and structure disorder are reduced, the hierarchy relation is clear and controllable, and the preciseness and integrity of knowledge organization are effectively improved.

The application discloses a kind of mineral flotation industry knowledge graph construction methods based on multi-level cross-process association.The flotation process is systematically expressed by combining ontology modeling and tree structure.The method regards the flotation process as a dynamic tree, the root node is used to represent the feed parameters, the trunk and branch nodes correspond to the main process steps and their sub-processes respectively, and the leaf nodes represent process parameters and performance index feedback. The application introduces ontology in the knowledge graph, dynamically models the process data, process association and input-output relationship, and uses ontology semantics to realize the unified description and integrated expression of multi-source heterogeneous data. Through this method, the intuitive modeling problem of complex association of multiple parameters in the flotation process is effectively solved, providing a clear process expression and knowledge organization form, which lays a foundation for intelligent analysis and optimization of mineral flotation process.

The invention provides a scientific research logic representation-oriented composite knowledge chain construction method, and relates to the technical field of knowledge extraction, and the method comprises the steps: constructing a composite knowledge chain structure model through defining a core knowledge component; constructing an instruction set of the components and the sub-elements, performing instruction fine tuning on the pre-trained large language model, processing the scientific and technical literature through the fine-tuned large language model, and generating a knowledge extraction result; analyzing the internal structure relationship of the component and the cross-component logic relationship; and performing structured organization on the components, the component internal element relationship and the cross-component logic relationship to generate a composite knowledge chain. Through the method and the device, the technical problem that the scientific research knowledge extraction accuracy is relatively low due to the fact that a knowledge organization method is difficult to deeply characterize a complex logic relationship and dynamic dependence in a scientific research activity in the prior art can be solved, and the knowledge extraction accuracy is improved by deeply characterizing scientific research logic and automatically constructing a composite knowledge chain.

The application relates to the technical field of electric power engineering and discloses a power engineering multi-modal RAG system based on a knowledge graph and multi-Agent cooperation, which comprises a multi-modal dynamic knowledge base construction module configured to perform structural processing, multi-dimensional knowledge organization and dynamic optimization on power engineering multi-modal data; an adaptive retrieval strategy engine module configured to construct a weight decision network based on deep reinforcement learning, and perform multi-channel parallel retrieval and result fusion; an iterative self-reflection reasoning module configured to generate a reasoning path in combination with a retrieval result and verify the effectiveness of evidence from multiple dimensions; an MCP tool intelligent calling module configured to integrate multiple types of standardized MCP tools; and a multi-Agent cooperation framework configured to provide Agents with multiple types of function-specific and cross-module interaction capabilities. The application can significantly improve the question and answer accuracy, reasoning depth and result explainability in a complex multi-modal scene of electric power engineering.

The application discloses a kind of publishing field knowledge service construction methods based on knowledge graph, it is related to intelligent digital publishing technical field, including, according to publishing content structure node set, constructs publishing content structure diagram, and in combination with publishing original resource set executes publishing field semantic information extraction and semantic correlation analysis, obtains publishing semantic diagram;From the structural distribution characteristics and content anchoring characteristics of semantic node in publishing resource in publishing semantic diagram are extracted, and the anchor point edge between publishing semantic diagram and publishing content structure diagram is established, and the multi-granularity anchor point double diagram is formed;Based on graph service execution plan, execute double diagram collaborative retrieval and service path generation, obtain knowledge service candidate set, and carry out service ordering and arrangement, generate publishing field knowledge service content.The application realizes the collaborative mapping of semantic relationship and content bearing position, enhances knowledge organization precision and service direction.

The application provides a composite knowledge chain construction method for scientific research logic characterization, and relates to the technical field of knowledge extraction. The method comprises the following steps: constructing a composite knowledge chain structure model by defining core knowledge components; constructing an instruction set of components and sub-elements, and fine-tuning a pre-trained large language model; processing scientific literature through the fine-tuned large language model to generate a knowledge extraction result; executing analysis of internal structural relationships of components and cross-component logical relationships; structurally organizing components, internal element relationships of components and cross-component logical relationships to generate a composite knowledge chain. The application can solve the technical problem of low accuracy of scientific research knowledge extraction in the prior art due to the difficulty of knowledge organization methods in deeply characterizing complex logical relationships and dynamic dependencies in scientific research activities. By deeply characterizing scientific research logic and automatically constructing a composite knowledge chain, the accuracy of knowledge extraction is improved.

The present application relates to the technical field of ecological environment management, in particular to a knowledge graph construction method and system for regional ecological environment management technology, wherein the method comprises: receiving a standardized technology template file to be constructed as a knowledge graph to read a technology library worksheet; performing a preset normalization processing on the technology library worksheet to obtain a normalized technology library worksheet; taking the key technology of ecological environment management as a root node, generating a stable node identifier of the normalized technology library worksheet to construct a multi-level node relationship; taking the stable node identifier as a unique index, performing attribute validity determination, attribute fusion and relationship deduplication on the multi-level node relationship to obtain a unified graph data file. Thus, the problem of lack of a high-quality, consistent and knowledge organization conversion method for regional ecological environment management technology data in the prior art is solved.

The application provides a kind of big model cooperation-based adolescent depression intelligent diagnosis and treatment method and system, the method comprises: constructing remote sensing human settlement microenvironment and growth soft environment dataset, pre-processing and feature extraction are carried out on the dataset, space-time matching and representation are carried out on environment and subject individual brain-behavior clinical data, individualized intervention cohort data and feature representation matched with environment are collected;Based on the multidimensional data obtained, a knowledge graph based on large model is established;Based on the established knowledge graph, environment-brain-behavior enhanced intelligent diagnosis and intervention are carried out.The application is based on the knowledge organization system of auxiliary diagnosis, pathogenesis evaluation and individualized intervention of environment-brain-behavior, constructs environment-brain-behavior multidimensional knowledge graph for adolescent depression diagnosis, based on the big model cooperative reasoning mechanism enhanced by environment-brain-behavior multidimensional knowledge, provides intelligent and precise method system for adolescent depression diagnosis and treatment, greatly improves the level of diagnosis and treatment.

The invention discloses a medical personal knowledge base management method, system and device based on a large model and a medium, and mainly relates to the technical field of knowledge base management. The method is used for solving the problems that in an existing scheme, medical knowledge sources are diversified, cross-modal unified modeling and integrated analysis are difficult to achieve, a personalized knowledge organization and calling mechanism is lacked, and context understanding and retrieval cannot be supported. Comprising the following steps: configuring a semantic question-answer framework in a reading interface, and obtaining an actual retrieval demand through an input box in the semantic question-answer framework; analyzing an actual retrieval demand through a large model to obtain a semantic structure and keyword information; determining a corresponding knowledge fragment from a preset knowledge database according to a relationship between the keyword information and the knowledge label vector; and based on the corresponding knowledge fragments, the actual retrieval requirements and a preset knowledge database, utilizing a medical AI large model to obtain an output answer result.

The neural network-based subject field dictionary construction method, system, terminal and medium of the present application automatically construct a subject field knowledge dictionary by performing semantic analysis on massive specific subject field data and mining subject professional terms; the present application can automatically construct a subject knowledge architecture from massive academic data, the method has high accuracy and recall rate, and significantly improves knowledge organization efficiency and reduces knowledge maintenance cost. Furthermore, the knowledge structure of the present application is academic, the subject knowledge system hierarchy is complete and unified, and is authoritative and practical, and the present application can realize automatic construction and regular incremental updating of the knowledge dictionary, adopts the most advanced artificial intelligence technology, and has strong core semantic correlation mapping capability. The present application can effectively replace 90% of manual work, save economic and time costs, and has strong universality and operability, has certain engineering application prospects, and effectively helps the knowledge service field to perform semantic indexing, organization and retrieval of academic resources.

The invention discloses a general knowledge construction method and system based on vertical and horizontal multi-dimensional linkage and a storage medium. The method comprises the following steps: receiving a user operation instruction; acquiring data of longitudinal analysis dimensions (including a basic information layer, a logic association layer and a significance principle layer) and transverse application dimensions (including a resource attribute layer, a rule technique layer and a comprehensive scheme layer) from a domain knowledge graph database according to the instruction; an intelligent linkage mechanism is triggered through a rule engine, and the intelligent linkage mechanism comprises longitudinal internal linkage, transverse internal linkage and cross linkage (including learning-to-use, use-to-learn and reflection backtracking). The system comprises a processor, a memory and a corresponding software module. Through longitudinal and transverse multi-dimensional knowledge organization and intelligent linkage, seamless connection and deep fusion of knowledge from analysis to application, from learning to practice and from problems to principles are realized, and the efficiency and effect of knowledge acquisition, understanding and application are improved.

The application discloses an audit literature mind map generation method and system based on feature refining and a large language model, first extracts object theme information, text information, reference object information and hierarchical information in target audit literature and converts them into structured audit text information, and then adopts a hybrid weight model to calculate the global importance of keywords in the structured audit text information and extract target text keywords; the application realizes the function of efficiently generating corresponding audit mind maps for audit literature, and through the adoption of a graph neural network, the logical coherence of text object information can be enhanced and the logical relationship between different nodes can be ensured to be rigorous, and through rule constraints, the text information can be ensured to conform to the audit field specifications, which not only can enhance the systematicness and logicalness of knowledge organization, but also can finely control the generated content, and at the same time, can provide accurate audit literature reading assistance for users and greatly improve the understanding and analysis efficiency of audit literature.

The invention discloses a value mining method and system for education course resources, and relates to the technical field of data processing. Comprising the steps that firstly, multi-dimensional features such as content complexity, structural integrity, information density and timeliness are extracted from a course resource library, comprehensive value scores of resources are calculated through a preset weighted mathematical model, and scientific quantitative sorting is achieved; and then, on the basis of the screened target resources, constructing a self-adaptive course knowledge graph fitting a teaching rule by calculating statistical association strength between concepts. Finally, a strategy based on dynamic clustering and multi-objective optimization is adopted, key content pages are automatically extracted from resources, and abstracts are generated. The whole process is based on a mathematical calculation and statistical method, deep coupling of all technical modules is achieved, and the accuracy of high-quality resource discovery, the effectiveness of knowledge organization and the intelligent level of content generation are remarkably improved.

This invention relates to the field of database construction technology, and discloses a knowledge base construction method and system based on AI text parsing and hybrid retrieval. By integrating semantic relevance and document layout features through adaptive block segmentation, it achieves accurate identification of core semantic units in the text and maintains logical integrity in the division, fundamentally avoiding information fragmentation and laying the foundation for high-quality knowledge organization. Secondly, through a hybrid retrieval mechanism combining keyword, semantic vector retrieval, and multi-dimensional re-ranking, it effectively balances retrieval response speed with the depth and accuracy of results, meeting users' multi-level query needs from rapid location to in-depth correlation mining. Finally, through dynamic updates and automatic association mapping functions, it achieves real-time synchronization and intelligent association of newly added knowledge, not only ensuring the timeliness of the knowledge system but also proactively building a cross-document knowledge network, thereby breaking down information silos and enhancing the overall utilization value and discovery capability of knowledge.

The application relates to the technical field of knowledge graphs, can be applied to business scenarios such as financial technology, and discloses a time-series knowledge graph driven knowledge retrieval method, device, equipment and medium, which comprises the following steps: subjecting unstructured text in a target field to field adaptability training to obtain a field-specific large model, using the model to extract entity relationship triples from business source text and construct a dynamic time-series knowledge graph, simultaneously performing comparative learning training on multi-modal data to establish cross-modal semantic indexing, and when a query request is received, performing joint retrieval and outputting feedback content. Through the fusion of field semantic modeling, time-series knowledge organization and cross-modal retrieval mechanism, the application enables scattered unstructured and multi-modal data to form a knowledge system that can evolve over time and be uniformly retrieved, thereby improving the accuracy of knowledge acquisition and the efficiency of business response.

The present application relates to the technical field of electric digital data processing, and especially relates to a target activity rule intelligent analysis method based on sky net big data, comprising the following steps: constructing a knowledge graph and knowledge organization of a target based on sky net big data; acquiring real-time or non-real-time situation data of the target, and tracking and analyzing the activity situation of the target based on the knowledge graph and knowledge organization; analyzing the activity rule of the target based on the tracking analysis result of the activity situation, the knowledge graph and the knowledge organization, and generating and visually displaying a target portrait. Through the research on related technologies such as target knowledge modeling, knowledge organization, intelligent identification, situation tracking research and judgment, activity track information extraction, activity rule reasoning, target portrait generation and final target portrait visual display, the present application realizes the establishment of the correlation between target activities and space-time events from a large number of fragmented and static data, and the mining of certain activity rules.

The present application relates to the technical field of natural language processing, and particularly relates to a text knowledge base construction method, computer equipment and program product. The present application provides a text knowledge base construction method, which extracts a first-level title of an original text through a large language model; splits the original text based on the first-level title to obtain first-level texts corresponding to the number of first-level titles; performs parallel progressive splitting on the first-level texts that do not meet a preset termination splitting condition until the split subtexts meet the preset termination splitting condition, and then stops the splitting; and constructs a multi-level text structure tree based on the main title of the original text and all the split first-level texts and subtexts. The method realizes layer-by-layer identification of the text structure and recursive construction of the multi-level text structure tree, not only retains the original logical hierarchy of the original text, but also reduces the problems of semantic fragmentation and structural disorder, so that the hierarchical relationship is clear and controllable, and the rigor and integrity of knowledge organization are effectively improved.

The application relates to a law enforcement document element extraction and law article matching system based on retrieval enhancement generation, which comprises the following modules: document access, text analysis, element modeling, law regulation knowledge organization and joint index, retrieval enhancement, law article matching, risk identification, result generation and audit record, etc. Through the structural analysis and case element modeling of law enforcement documents, the candidate law articles are recalled by combining keyword retrieval, semantic retrieval and relationship retrieval, the law article matching is completed through the alignment and consistency checking of conditions, the risks of law article omission, insufficient evidence, procedure loss and abnormal punishment are identified, and finally the standardized results are generated and the whole process is left with traces and traced back. The system can improve the processing efficiency of law enforcement documents and the accuracy of law article application, and enhance the standardization and auditability of law enforcement.

The invention belongs to the technical field of artificial intelligence, and relates to a multi-agent-based knowledge graph automatic construction and query method and system. The method comprises the following steps: performing entity identification on an input document to obtain an entity set; determining a theme of the input document, and obtaining a seed entity according to the association strength between each entity in the entity set and the theme; extracting a relationship between seed entities based on a layered strategy label system; the seed entities and the extracted relations form candidate knowledge triples, and verification of different dimensions is carried out on the candidate knowledge triples through multi-agent cooperation; storing the knowledge triples passing the verification into a knowledge base to form a knowledge graph; and querying by using the knowledge graph to obtain answers to the questions. According to the method, the knowledge can be organized into a traceable logic chain, the interpretability and reasoning capability of the knowledge graph are improved, the consistency and reliability of the stored knowledge can be improved, and the timeliness maintenance capability and query accuracy of the knowledge base can be improved.