124 results about "Relational reasoning" patented technology

An intelligent analysis method based on medical document structure perception and multi-modal fusion comprises the following steps: carrying out structure topology modeling on a medical document, extracting visual layout, text meta-information, space coordinates and semantic keyword features, constructing a semantic topological graph and dynamically shielding irrelevant contents; selecting an extraction path according to a document type, performing deep semantic analysis and entity recognition on a text-type document, and performing visual enhancement OCR recognition on a scanning-type document; the features are injected into a medical knowledge graph, and feature fusion, semantic verification, relation reasoning and information completion are achieved through a graph neural network; a three-stage strategy optimization model of basic pre-training, domain adaptation and online reinforcement learning is adopted; and large-scale processing is realized through a dynamically aggregated distributed architecture. The method is used for intelligent analysis and structured conversion of documents of hospitals, medical insurance and medical scientific research. The problems that heterogeneous medical document analysis adaptability is poor, multi-modal fusion is difficult, medical knowledge utilization is insufficient, and large-scale processing efficiency is low are solved.

The invention discloses a cross-domain heterogeneous data query system and method based on a large model and a knowledge graph, belongs to the technical field of information retrieval, and aims to solve the technical problem of complex relation reasoning in cross-domain heterogeneous data query. Comprising a data input and preprocessing module used for collecting multi-modal data to obtain feature vectors; the knowledge graph construction and management module is used for constructing a knowledge graph and providing query service based on the knowledge graph; the bidirectional enhancement module is used for writing the reasoning result of the large language model into a knowledge graph and carrying out version management; the domain adaptation layer is used for carrying out model training on the large language model based on a lightweight adapter and a domain adaptation mechanism; the real-time query and reasoning module verifies and supplements the candidate answers based on a knowledge graph to generate an initial answer, and explains a reasoning path based on a causal reasoning network to generate a final answer; and the interpretability and transparency module is used for displaying knowledge in the knowledge graph through a visual interface and providing auditing service based on the operation day.

The invention provides a game strategy retrieval method and device based on event-driven knowledge graph embedding. The method comprises the following steps: performing structured analysis on strategy data, and updating entities and relationships; performing graph embedding calculation on entities and relationships in the knowledge graph to obtain vector representation among the entities, and constructing a searchable vector index; vectorizing a strategy query request of a user, and performing similarity retrieval on the strategy query request and the vector index to obtain a first candidate entity set similar to the query vector; performing relation reasoning by taking the first candidate entity set as a starting point in the knowledge graph to obtain a second candidate entity set in semantic association with the first candidate entity set, and combining the second candidate entity set with the first candidate entity set to form a target entity set; and performing correlation sorting on the target entity set to obtain a strategy result. According to the method, the strategy data can be automatically and structurally managed, and deep semantic retrieval is supported, so that the accuracy of game strategy content retrieval is improved, and the dynamic expansion capability is achieved.

The invention discloses a knowledge graph fused IT operation and maintenance knowledge base intelligent construction method and system. The method comprises the steps of preprocessing collected original data; an initial entity set and a relation set of the knowledge graph are constructed based on the standardized operation and maintenance data set, entities comprise equipment, components, fault types and solutions, and relations comprise association between the equipment and the components and association between the fault types and the solutions; performing semantic analysis on text information in the operation and maintenance data set, extracting key information as attribute information of the knowledge graph, and associating the key information with corresponding entities or relationships; optimizing the knowledge graph according to a preset algorithm, including entity alignment, relation reasoning and knowledge fusion, so as to generate a complete IT operation and maintenance knowledge graph; and storing the constructed IT operation and maintenance knowledge graph in a knowledge base. According to the method, the quality and intelligence of the knowledge graph are effectively improved, the problems of entity redundancy, insufficient excavation of implicit relationships and knowledge repetition are solved, the construction efficiency of the IT operation and maintenance knowledge base and the quality of a basic structure are remarkably improved, and a solid foundation is laid for subsequent knowledge optimization, reasoning and application.

The invention provides a method for efficiently detecting a fish target in a complex underwater environment based on a priori knowledge guide network, which comprises the following steps of: 1, acquiring an underwater image and preprocessing the underwater image; 2, establishing a recovery subnet module, a relation reasoning attention module and a self-adaptive feature fusion module, and performing complex underwater environment fish target detection; the recovery subnet module is used for guiding network learning to remove underwater turbid features through an underwater scattering model, generating a clear image through a water body recovery decoder WR, and reconstructing an underwater turbid image; the relation reasoning attention module is used for constructing a co-occurrence relation graph, performing relation reasoning by using a graph convolution network, and dynamically adjusting attention weight; and the adaptive feature fusion module is used for optimizing feature expression in combination with a Sigmoid function and a channel-by-channel weighting mechanism. The method can effectively cope with the conditions of sudden change of turbidity of a water body or complex background and the like, and can keep higher detection performance under different underwater conditions.

The invention belongs to the technical field of engineering information intelligent processing, and relates to a highway bridge drawing multi-modal information extraction and semantic understanding method and system.The method comprises the steps that firstly, self-adaptive judgment is conducted on a vector drawing and a scanning drawing, geometric distortion correction, drawing frame and title bar positioning and layout segmentation are completed, and then the vector drawing and the scanning drawing are obtained; constructing a hierarchical document structure comprising texts, tables, images and two-dimensional drawing objects; then, a front-end target detection network and a rear-end cross-modal document understanding model are fused, and detection and relation reasoning of elements such as view blocks, labels, symbols and tables are achieved; and image-text feature alignment is further performed by using a visual coding network and a text coding network, structural description conforming to engineering semantics is generated by means of a multi-modal language model, an engineering parameter database is established, and parameter query and multi-modal question and answer output are supported. According to the method, the accuracy and efficiency of automatic acquisition and semantic understanding of the key information of the highway bridge drawing are improved.

The invention provides a document element rapid extraction system based on a pre-trained large model, and relates to the technical field of computer software application, the system comprises a parameter field adaptation module used for textualizing a document and constructing an industry standard corpus based on a textualized processing result, adjusting a preset language model by utilizing an industrial standard corpus; the dynamic document partitioning module is used for performing semantic segmentation processing on the industrial standard document to obtain a plurality of text blocks; the entity alignment module is used for carrying out entity and relation extraction on the text blocks and carrying out entity alignment in combination with a uniform manifold approximation and projection method; and the relation reasoning and knowledge graph completion module is used for performing completion processing on the preliminary knowledge graph and storing a completion result. According to the method, the element extraction efficiency can be directly improved without pre-defining a rule template or performing data annotation.

The invention relates to the field of compliance management, and particularly discloses a real-time compliance management system dynamic evaluation system and method based on data driving. External law and regulation documents and enterprise internal compliance rule documents are respectively mapped into knowledge maps; semantic association analysis is carried out on cross-graph regulation provisions and compliance rule provisions to identify the relationship type between the two provisions, and when conflicts are found, a correction mechanism is triggered to realize cross-graph node relationship bridging. When the regulation is changed, further positioning the embedding position of the change provision in the map, capturing the diffusion path of the affected compliance rule provision, and performing semantic analysis and relation reasoning on the regulation change provision and the affected compliance rule set to reveal the chain influence mode of the regulation change on the enterprise compliance rule; and generating compliance rule updating suggestions. According to the method, dynamic evaluation and optimization of the compliance management system can be realized, and the efficiency and accuracy of enterprise compliance management are improved.

The invention discloses a target retrieval method and system. The method comprises the following steps: S1, data access and preprocessing: acquiring multi-source data, performing cleaning, formatting and time-space standardization, and performing target detection and cutting on an image / video to generate a structured target object; s2, feature extraction: extracting deep semantic feature vectors and auxiliary understanding information, which have discriminability and adapt to complex scenes, from the target image; s3, constructing a data index, including constructing a spatio-temporal semantic hypergraph index of multiple types of nodes and hyperedges based on deep features and spatio-temporal information of the targets to express a complex relationship between the targets; and S4, data query: receiving multi-modal query information of a user, performing candidate region screening, feature matching and relation reasoning by utilizing a hypergraph index, and finally outputting a high-confidence target retrieval result. According to the method, the robustness of complex scenes and target changes and multi-dimensional query of depth are improved.

The invention relates to the technical field of financial data analysis and artificial intelligence, in particular to a multi-source heterogeneous financial data fusion and intelligent analysis system which comprises a data standardization processing module, a time sequence event fusion module, a knowledge graph construction module, a relation reasoning module and a self-adaptive anomaly detection module. The data standardization processing module is used for converting heterogeneous financial data from different sources into unified tensor representation; the time sequence event fusion module adopts a double-clue cooperation mechanism to establish a mapping relation between continuous time sequence data and discrete events; the knowledge graph construction module extracts financial entities and relationships thereof, and constructs a multi-level knowledge graph; the relation reasoning module performs deep reasoning based on a graph attention mechanism; and the adaptive anomaly detection module dynamically adjusts the detection threshold according to the market environment. According to the system, implicit association in heterogeneous financial data can be deeply mined, market anomalies are recognized in advance, and comprehensive support is provided for financial decision making.

The invention relates to the technical field of artificial intelligence and data processing, and discloses a multi-mode identity relation inference system based on a graph neural network. The system comprises a multi-modal feature extraction module, a cross-modal alignment module, a graph structure construction module, a dynamic relation reasoning module and a decision output module. According to the method, the cross-modal alignment module is introduced to project the image features and the text features to a unified public semantic space, so that the nonlinear distribution difference of heterogeneous modals in an embedding space is effectively eliminated, and cross-modal alignment errors are avoided from the source; by integrating the attention mechanism of modal perception in the graph neural network, the system can dynamically learn the semantic association strength between the nodes in different modals, adaptively adjust the weight distribution in the neighborhood information aggregation process, and significantly improve the accuracy of node characterization.

The invention belongs to the technical field of information, and relates to a time sequence knowledge graph reasoning method and system for distinguishing similar historical structures. The method comprises the steps that relation reasoning paths between entity pairs are extracted, the confidence degree of each relation reasoning path is evaluated, the confidence degrees of the relation reasoning paths under different timestamps are aggregated, and relation reasoning scores are obtained; and then calculating an entity sub-graph structure score, obtaining a prediction score of the candidate entity according to the relationship reasoning score and the entity sub-graph structure score, and complementing the missing entity in the prediction query by using the candidate entity with the highest prediction score to realize time sequence knowledge graph reasoning. According to the method, similar historical structures are distinguished from the combination of the relation and the entity, modeling is carried out on the potential logic of the relation in the time sequence and the time evolution of the sub-graph structure of the entity, and candidate objects with similar sub-graph structures or similar relation connection can be distinguished; and the performance of the external reasoning task of the time sequence knowledge graph is effectively improved.

The invention discloses a group behavior identification method based on multi-view individual relationship interaction, which comprises the following steps of: firstly, acquiring joint point information of different individuals in a scene by utilizing a skeleton point estimation network and a position information coding technology; then extracting multi-granularity feature representation of the body part through a depth model; then, utilizing an attention mechanism in Transform to respectively construct a structured relation reasoning module in the individual under multiple view angles and a spatio-temporal information interaction module among individuals under multiple view angles, and realizing deep interaction of individual body part information from different view angles; self-adaptive fusion factors are designed, cross-view cross-granularity individual interaction features are integrated, and high-discrimination individual content representation is obtained. And finally, a multi-head loss training strategy is added, the types of the individual and group behaviors are judged from the enhanced individual and group behavior characteristics, and a group behavior recognition task in a complex scene is realized.

The invention discloses an automobile network security knowledge reasoning method based on a graph neural network, and relates to the field of Internet of Vehicles. Due to the fact that entities and relationships in Internet of Vehicles security have the characteristics of complex semantics, diversified types and the like, a traditional rule-based knowledge reasoning method is difficult to effectively predict implicit relationships and is low in efficiency, and meanwhile, a graph neural network method based on a same composition cannot capture high-order semantic information, so that the reasoning precision is reduced. Therefore, a reasoning method capable of supporting complex relation modeling is needed. The invention provides an automobile network security implicit relation reasoning method based on a graph neural network. A knowledge graph embedding module, a data preprocessing module, a graph neural network modeling module and a decoder module are included. According to the method, knowledge reasoning of the implicit relationship is realized, the problem that a traditional method is difficult to model a complex semantic relationship is solved, and the efficiency and accuracy of implicit relationship prediction are improved.

The invention discloses a knowledge graph construction method, device and equipment and a readable storage medium, and is applied to the technical field of natural language processing and knowledge engineering.The method comprises the steps that document content is divided to obtain initial document fragments, and all the initial document fragments are merged and divided based on semantic similarity to obtain target division blocks; based on the target division block, subject-predicate-object formatting processing is carried out to obtain a subject-predicate-object formatting result; entity and relation extraction is carried out according to the subject-predicate-object formatting result to obtain a display triple, implicit relation reasoning is carried out to obtain an implicit relation triple, entity and relation type normalization is carried out to obtain a normalized triple, and the knowledge graph is constructed based on the normalized triple. The block segmentation driven by semantic similarity is adopted to avoid sentence breakage, cascade errors are reduced based on subject-object formatting processing, and the problems of fragmentation, link missing and the like are solved based on implicit relations, so that the integrity and accuracy of knowledge graph construction are improved.

The invention provides a robot task planning method and device, computer equipment and a storage medium, and the method comprises the steps: receiving a natural language instruction of a user; determining a task target based on the natural language instruction and scene context information; constructing a semantic map based on the environment data and the visual identification and relation reasoning result; based on the task target and the semantic map, generating an action sequence through a formalized planning engine; and controlling the robot to execute the action sequence. By adopting the method, the usability, accuracy and reliability of robot task planning can be improved.

The invention discloses a CVD (Chemical Vapor Deposition) death subgroup identification method and device combining causal reasoning and consensus clustering. The method comprises the following steps: collecting and preprocessing original feature data; performing intermediary causal relationship reasoning on the preprocessed feature data, and taking features contained in an intermediary causal relationship reasoning result as an initial feature set; performing death risk prediction according to the input feature data by using a plurality of machine learning models, and training by using the initial feature set to obtain a plurality of death risk prediction models; calculating SHAP values of the feature variables of all the death risk prediction models by adopting an SHAP algorithm, and selecting an optimal model and an optimal feature set by utilizing the SHAP values of the feature variables; performing consensus clustering on the optimal feature set and a death risk prediction result output by the optimal model to obtain a plurality of death subgroups; and carrying out statistical analysis on the feature data and survival results of each death subgroup, and identifying a path of each death subgroup in combination with an intermediary causal relationship reasoning result.

The invention discloses a subject entity labeling method and system fusing image recognition and a knowledge graph, and relates to the technical field of image recognition and natural language processing. The method comprises the following steps: carrying out preprocessing and image-text association on multi-source heterogeneous subject data; detecting a visual entity in the image through an improved YOLO model, and extracting and linking a text entity in combination with a subject dictionary and a knowledge graph; cross-modal collaborative disambiguation is realized by calculating the semantic similarity of visual candidate entities and text context vectors; multi-modal entities are combined, relation reasoning and enrichment labeling are carried out in a knowledge graph, and a deep labeling result containing the entities and a semantic relation network of the entities is generated; the problems of difficulty in multi-source data fusion, inaccurate professional entity recognition and difficulty in semantic ambiguity elimination are effectively solved, the depth and accuracy of subject knowledge semantic understanding are remarkably improved, and key technical support is provided for intelligent education application.

The invention provides a multi-modal fusion-driven agent interaction method and system, and relates to the technical field of intelligent interaction, and the method comprises the steps: extracting image, text and audio feature vectors through a deep neural network, carrying out the self-adaptive fusion through a cross-modal self-attention sub-module and an inter-modal interaction attention sub-module, and carrying out the self-adaptive fusion. And updating node features of the agent knowledge graph based on the difference degree, performing relation reasoning, and generating an interaction strategy to control the agent to execute an interaction action. According to the method, effective fusion of multi-modal information and dynamic updating of the knowledge graph are realized, and the accuracy and adaptability of agent interaction are improved.

The invention discloses a sub-graph reasoning method fusing logic rule learning and attack semantic enhancement. The method comprises the steps that an input layer dynamically integrates knowledge graph topology and an AMIE rule base, an initial k-hop sub-graph is generated, and structured input is provided for attack chain mining; the sub-graph extraction module is used for executing double confidence filtering, screening high-value attack chains and applying dictionary filtering to enhance semantic reliability; the sub-graph coding module adopts an entity perception update layer and a relationship aggregation evolution layer of a dual-channel mechanism to collaboratively model the spatial-temporal characteristics of an attack chain; the relation reasoning optimization module is used for dynamically injecting high confidence rules and optimizing triple scores; and the training optimization module is used for implementing task perception negative sampling. According to the subgraph reasoning method fusing logic rule learning and attack semantic enhancement, based on inductive reasoning and semantic perception modeling, by taking subgraph modeling guided by a logic path as a core, attack chain rules with high confidence in a training graph are mined, and the understanding ability of a model structure is improved.

The invention relates to the technical field of artificial intelligence, knowledge representation and knowledge retrieval, in particular to a high-performance knowledge base system based on multi-modal mixed retrieval, which comprises a vector calculation module used for converting an unstructured text into vector data represented by feature vectors; the knowledge graph module is used for converting a structured text into knowledge graph data, the mixed retrieval module is used for performing retrieval according to a query request, the mixed retrieval fusion module is used for performing retrieval on the storage module according to the query request, and mixed retrieval comprises vector retrieval and graph retrieval. And generating a fusion retrieval result. The vector calculation module and the knowledge graph module respectively process unstructured and structured data, and the hybrid retrieval fusion module respectively executes vector retrieval and graph retrieval based on the query request and fuses the retrieval results, so that the semantic matching precision and the relationship reasoning ability are greatly improved.

The invention relates to an intelligent question and answer and scheme recommendation system based on a fire-fighting toughness knowledge graph, in particular to the field of knowledge graphs, according to the scheme, full-process optimization of fire-fighting knowledge from collection to application is achieved through cooperation of multiple modules, a data processing module converts heterogeneous data into standardized knowledge units, and the knowledge units are stored in a database; the problem of multi-source data fusion is effectively solved; the relation reasoning module remarkably improves the knowledge quality through dynamic relation reasoning and conflict detection; the graph updating module realizes real-time evolution of the knowledge graph based on an event-driven mechanism, so that the timeliness of information is ensured; a closed-loop optimization mechanism formed by the optimization verification module continuously improves the processing precision of each module, the overall scheme effectively enhances the consistency, accuracy and real-time performance of knowledge, reliable knowledge support is provided for intelligent question answering and scheme recommendation, and the decision-making efficiency of fire-fighting emergency command is remarkably improved.

Methods and processors are disclosed. The method includes acquiring an input indicative of a Relational Question-Answering (RQA) problem, generating an instance graph based on the input, generating an intermediate output indicative of one or more reasoning paths in the instance graph and generating, based on the input and the intermediate output, an output indicative of one or more potential answers to the RQA problem.

The present invention proposes an instance-level cross-modal retrieval method for relational reasoning and cross-modal independent matching networks. First, a modal feature extractor is used to convert the input original image into regional features and the input text into a word sequence. Then, modal feature relational reasoning is performed on both the image and text modalities respectively to explore the interaction relationships between local features. Next, a graph pooling method based on a graph network is used to perform modal global semantic aggregation on the rearranged features. Finally, the similarity between multi-modal features is calculated, and the cross-modal retrieval results are returned according to the similarity. During the neural network training process, a gravitational loss function is used to guide and correct the learning process of the matching relationships within and between modalities; the present invention can effectively perform local relational reasoning and global semantic aggregation on multi-modal data, and efficiently and accurately complete the fine-grained instance-level cross-modal retrieval task in a multi-modal scenario.

A big language model question-answering method based on knowledge graph enhancement relates to the technical field of knowledge graphs, and is characterized in that key entities are extracted from medical texts, and evidence sub-graphs are constructed by using knowledge graphs, so that the reasoning ability of a big language model is enhanced. And feature extraction and relation reasoning are performed on the sub-graphs by using a graph neural network, so that the understanding ability of the model to the medical scene is enhanced. Through global smooth feature initialization and dynamic weight reconstruction, the relationship between nodes is accurately adjusted, and the reasoning space is reduced to the most relevant part. In addition, the sub-graphs are formatted into entity chains, converted into natural language description and integrated into a unified reasoning graph, and a comprehensive view angle is provided for the model. Finally, the model generates a final answer based on the reasoning graph and the key reasoning elements, and constructs an interpretation context at the same time, so that the interpretability of the model is enhanced.

The invention discloses a character relationship reasoning system and a character relationship reasoning method based on a knowledge graph, and the method comprises the steps: aligning entities in different data sources to entities in the knowledge graph, extracting character relationships from the data sources, and adding the character relationships into the knowledge graph; extracting features of a plurality of modes from the figures in the knowledge graph, projecting the extracted features of the plurality of modes to the same semantic space, and generating a unified semantic representation after fusion; on the basis of a graph neural network model, performing representation learning on entity and character relationships in the knowledge graph after the unified semantic representation is generated, so as to obtain graph topological structure information and semantic information of knowledge; an incremental learning technology is used, and the graph neural network model is trained only through newly added data; utilizing a domain adversarial network to extract domain features of each data source, and dynamically adjusting parameters and a structure of the graph neural network model according to the domain features; and capturing associated entities and relationships in the reasoning process by using an attention mechanism, and generating a reasoning path.

The invention belongs to the technical field of data retrieval, and particularly discloses an inquiry method and system based on a vector database and graph query, a terminal and a medium, and the method comprises the following steps: importing structured data into a graph database, and constructing a knowledge graph with entity nodes and edge relationships; generating a first semantic vector through the semantic coding model, storing the first semantic vector into a vector database, and establishing an index; a user inquiry text is received, and after sensitive information detection and context analysis are executed, a second semantic vector is generated through the semantic coding model; performing similarity retrieval in a vector database based on the second semantic vector to obtain a plurality of candidate node identifiers; executing query in a limited range by taking the candidate node as a starting point in the graph database, and extracting associated nodes and relationships to form a sub-graph; performing joint scoring on the sub-graph structures, and sorting the paths based on a scoring result; and generating and outputting an answer text according to the sorting result. The problem that in the prior art, semantic understanding and relation reasoning are disjointed and difficult to co-process is solved.

The invention discloses a manufacturing enterprise supply chain digital transformation maturity evaluation method driven by multi-dimensional indexes. The method comprises the following steps: widely collecting internal and external multi-source data of an enterprise through a data acquisition module, storing preprocessed data in a graph structure through a knowledge graph construction module, and optimizing query efficiency; potential association and abnormal modes among multi-modal data mining indexes are fused through a semantic understanding and relation reasoning module, field adaptability parameter adjustment and optimization are performed on each key model through a model training optimization module, and a hierarchical evaluation system is established and a quantitative improvement scheme is generated through an intelligent evaluation and improvement suggestion module. And finally, a complete closed loop from data acquisition to decision optimization is formed, and accurate evaluation and continuous improvement of the digital transformation maturity of the supply chain of the manufacturing enterprise are realized. The method is characterized in that a manufacturing enterprise supply chain digital transformation maturity evaluation system driven by a multi-dimensional index is used for evaluation.

The invention provides a process flow diagram intelligent analysis method based on YOLO computer visual identification, which combines a YOLO algorithm with specific domain knowledge of a process diagram, carries out technical combination of symbol detection, connection relation reasoning and semantic information extraction aiming at a data set construction and data enhancement method, and establishes a process flow diagram through embedding a CBAM attention module in YOLO Backbone. According to the method, the symbol key area can be dynamically concerned, the interference of complex backgrounds (characters and grid lines) is inhibited, the detection precision of small targets, easy-to-confuse targets and non-standard drawings is improved, the strict requirements on the quality of the drawings are reduced, the scene adaptability of the method is enhanced, and the process diagram analysis efficiency is remarkably improved; finally, the structured JSON data can be directly connected with visual software, efficient data support is provided for digital twinning and other industrial digital scenes, and the problems that a traditional method is low in efficiency and difficult to connect with downstream digital application are solved.

The invention discloses a knowledge base accurate calculation-oriented adaptive AI agent generation method, and relates to the technical field of artificial intelligence, and the method comprises the following steps: establishing a multi-source heterogeneous data collection interface matrix, constructing a semantic network modeling engine containing an RDF triple parser, designing a dynamic structure adjustment algorithm based on an LSTM-GRU hybrid neural network, and generating a semantic network model based on the LSTM-GRU hybrid neural network. And developing a task hierarchical computing framework. According to the self-adaptive AI agent generation method provided by the invention, by constructing the multi-source heterogeneous data acquisition interface matrix, the problems of large data format difference and uneven quality are effectively solved, the efficiency and accuracy of data acquisition and preprocessing are improved, efficient semantic alignment of heterogeneous ontologies is realized by utilizing a semantic network modeling engine, and the generation efficiency of the heterogeneous ontologies is improved. And the precision of entity disambiguation and relation reasoning is improved, powerful support is provided for dynamic processing and reasoning of knowledge, and the frequency can be updated according to the task complexity and data.