17 results about "Relational knowledge" patented technology

Causal discovery is performed using knowledge graph link prediction. Information from a causal network is transformed into a causal knowledge graph according to a mapping, the causal knowledge graph including a plurality of causal links, wherein each causal link includes a cause entity, a causal relation, and an effect entity, with the potential for a mediator. The causal knowledge graph is converted into embeddings, where the embeddings include a latent vector space representation of the causal knowledge graph. The embeddings are trained using a subset of the causal links of the causal knowledge graph. The embeddings are used for causal discovery to predict additional causal links of the causal knowledge graph.

The present application relates to the technical field of information retrieval, and in particular to an information retrieval method, medium and device, which supports the retrieval of three types of keywords and integrates multiple types of nodes in a graph, pre-constructs a material relationship knowledge graph containing multiple-dimensional nodes and associated relationships, combines the visual output of the material information table and the associated sub-graph, and intuitively displays the logical chain between the drug and each associated element, so that the user's associated query requirements in multiple scenarios can be met without multiple searches across platforms; by integrating authoritative data during the graph construction process, the association between the drug, treatment plan and disease has clinical rationality and authority, and by extracting the associated sub-graph through association expansion, false associations and irrelevant information are effectively filtered, and the accuracy and reliability of the search results are improved; by outputting the first and second search result pages in stages, the user's demand for quickly screening the target material is met, and detailed information is also provided, balancing the search efficiency and information depth.

This invention discloses a method for automatically generating evidentiary documents for a document management system manual. First, the chapter requirements of the evidentiary document to be generated are obtained, and relevant knowledge is retrieved from a corpus containing ISO standard texts, organizational background documents, and historical evidentiary documents. A chapter-entity-relationship knowledge graph is constructed by parsing ISO standard clauses. A three-level progressive retrieval strategy is used to match specific instance information from the corpus, and the standard requirements and instance information are integrated to generate dynamic prompt words. After generating preliminary content using a large language model, a multi-level rule-based verification system is used to verify grammatical format, standard compliance, and organizational adaptability layer by layer. If a verification fails, a feedback loop is triggered for correction. Key corpora are stored on a blockchain to ensure the traceability of the generated content. Finally, revision opinions from reviewers are obtained and feedback is used to optimize the generation strategy. This invention achieves automated and compliant generation of evidentiary documents, significantly improving generation efficiency and content quality.

The application discloses a data insight report generation method and system based on knowledge enhancement and fact verification. A domain index relationship knowledge graph is constructed by training a large language model; the insight is mapped to the graph, a weighted insight graph is constructed, and spectral clustering is performed to divide the insight theme area; a viewpoint unit is generated by an intelligent agent, and consistency verification and correction are performed by a fact verification intelligent agent; user intent is analyzed, candidate themes are screened, a report outline is generated by heuristic search arrangement based on SRCI score, semantic progression and logic entropy calculation; finally, verified viewpoint units are integrated according to the outline, and the final report is output. The application realizes the generation of a fully automatic and high-credibility report, and improves accuracy, logic and explainability.

The application discloses a kind of epilepsy medical knowledge graph construction method and system based on rose acid C, it is related to knowledge graph construction technical field, method includes: obtaining initial medical text information;The initial medical text information is preprocessed, and non-standard medical text information is obtained;According to medical terminology relationship knowledge base, the non-standard medical text information is converted into terminology, and standardized medical text information is obtained;From the standardized medical text information, the medical information related to rose acid C molecule mode of action and epilepsy diagnosis and treatment is extracted;The medical information is added to knowledge graph database.The application can convert non-standard medical text information into standardized medical text information by combining medical terminology relationship knowledge base, to construct knowledge graph, improve data recognition accuracy and knowledge graph quality.

The application discloses a kind of indoor multi-scene semantic map construction method based on semantic relation graph, comprising: based on laser radar using grid map algorithm constructs 2D grid map;Using binocular camera obtains scene image, obtains depth map by stereo matching and identifies object in environment using trained target detection model, obtains the class and coordinate of landmark, image data adopts bayesian filtering technology to combine prior information and carries out scene classification, obtains the semantic label of current scene, using the semantic label obtained using the semantic map of occupancy grid method;Based on target identification dataset constructs prior relation graph, fuses space relation knowledge graph and prior relation knowledge graph to construct graph layer, obtains multi-scene semantic map.The semantic relation map of the present application based on deep learning and knowledge graph construction stores a large amount of prior knowledge, gives map comprehensive and accurate environmental information, can realize quick search in multi-scene rescue and daily service.

The invention discloses an intelligent network connection vehicle traffic accident knowledge base construction system, and the system comprises a large model adaptation module which is provided with a lightweight large language model facing the field of intelligent network connection vehicle accidents; the knowledge graph construction module is configured with a structured knowledge graph which is constructed by extracting entities, attributes and relationships from a multi-source corpus based on a lightweight large language model and a preset knowledge modeling rule; the knowledge base generation module is configured with a knowledge base which is generated by carrying out collaborative scheduling on the lightweight large language model and the knowledge graph based on a graph-model complementary fusion inference mechanism; the graph-model complementary fusion inference mechanism performs fusion and confidence evaluation on semantic related knowledge from the large language model and structured associated knowledge from the knowledge graph in response to the query request, and generates an inference result based on the evaluated confidence; and the user interaction module is used for receiving the query request and visually outputting a reasoning result. Meanwhile, the invention further discloses a method and equipment.

This invention belongs to the field of computer vision technology, specifically relating to an image description method that integrates knowledge graphs and sentiment recognition. The method includes: acquiring an image to be processed and performing feature extraction and object detection to obtain object information, image features, and sentiment features; completing an external general knowledge graph using a graph attention network to obtain a dense knowledge graph; inputting the object information into the dense knowledge graph to query entity relationships, obtaining relational knowledge vectors; and using a Transformer decoder to process the relational knowledge vectors, image features, and sentiment features to obtain descriptive text for the image. This invention solves the problems of insufficient semantic understanding, lack of sentiment expression, and weak external knowledge support in existing technologies, generating highly accurate, complete, and emotionally rich image descriptions with promising application prospects.

This invention provides a large-scale model-based operation and maintenance management method, device, equipment, and storage medium. The device receives various operation and maintenance task requests uniformly at the application service layer, and the AI ​​agent layer performs intelligent task planning and instruction generation, achieving centralized and intelligent task scheduling. The data pipeline layer responds to instructions, simultaneously executing multi-protocol real-time data acquisition and vectorized knowledge base-based relational knowledge retrieval, effectively breaking down data silos and providing complete information input that integrates real-time status and historical experience for decision-making. This device fundamentally changes the traditional operation and maintenance model, freeing manual labor from tedious and repetitive inspection, troubleshooting, and reporting work, significantly improving fault response speed, processing accuracy, and knowledge reuse efficiency, and realizing automated, intelligent, and knowledge-based closed-loop management of operation and maintenance work.

This application provides a method, system, medium, product, and terminal for constructing a gene relationship knowledge graph. The method involves acquiring gene data from different databases and preprocessing the gene data; extracting entity information from the preprocessed gene data using a natural language model to generate an initial gene entity dataset and an initial gene entity relationship graph; performing data filtering on the initial gene entity dataset based on an adaptive filtering algorithm to obtain a target gene entity dataset; and constructing a gene relationship knowledge graph based on the target gene entity dataset, the initial gene entity relationship graph, and a pre-built gene database model. This not only comprehensively reveals the entirety of the gene network and its dynamic changes but also intuitively displays the interactions between genes through visualization, providing researchers with an intuitive and convenient research tool.

This invention relates to a knowledge graph completion method in the field of enterprise credit, belonging to the field of artificial intelligence technology. It includes the following steps: S1: data preprocessing and graph structure construction; S2: construction of an efficient multi-relation graph convolutional neural network; S3: dynamic boundary conditions and loss optimization; S4: multi-constraint optimization; S5: training and prediction. This invention significantly improves the completion accuracy of multi-relation knowledge graphs through innovative methods such as multi-relation modeling, dynamic similarity adjustment, and boundary adaptive optimization, resulting in a complete graph structure. It enables the model to maintain high prediction accuracy even with scarce data and low-frequency relationships. Through feature smoothing and cluster consistency regularization, it possesses temporal and dynamic semantic modeling capabilities. It also has the ability to model the "equity chain—supply chain—risk chain" characteristics of the enterprise credit field.

Causal discovery using hyper-relational knowledge graph link prediction is provided. Knowledge graph link prediction is used to perform causal discovery. In accordance with the mapping, information from the causal network is transformed into a causal knowledge graph comprising a plurality of causal links, where each causal link comprises a cause entity, a causal relationship and a result entity, and a potential medium. The causal knowledge graph is converted into an embedding, wherein the embedding comprises a potential vector space representation of the causal knowledge graph. The embedding is trained using a subset of causal links of the causal knowledge graph. Additional causal links are embedded that are used for causal discovery to predict a causal knowledge graph.

The application provides a target relationship knowledge base construction method and a target relationship identification method. The construction method comprises: acquiring target situation data at different historical moments, the target situation data representing spatial position relationships of different targets at the current historical moment in situation collection data; for the target situation data at each historical moment, processing the target situation data based on an attention mechanism to obtain a first spatial adjacency graph containing context, the first spatial adjacency graph representing dependence relationships of different targets in space and time; performing clustering and decoding processing on the first spatial adjacency graph to obtain a second spatial adjacency graph corresponding to the situation collection data; performing text extraction and relationship matching processing on the situation collection data to obtain relationship mapping information between different targets; and integrating the relationship mapping information and the second spatial adjacency graph to obtain a target relationship knowledge base comprising multiple target adjacency graphs corresponding to different historical moments.

A non-transitory computer readable storage medium has instructions executed by a processor to utilize natural language processing operations to produce technology standard document segments from a technology standard document. The technology standard document segments are converted to a knowledge graph characterizing relationships between the technology standard document segments. The structure of the knowledge graph, regulatory compliance rules and product information are utilized to derive large language model prompts for technology standard document segments. Output from the large language model is used to form probability ratings of requirements to test the product.

This invention discloses a HarmonyOS migration method for industrial measurement and control software assisted by a large-scale model and multiple agents. The method includes: constructing a software dependency graph containing business logic, interface layout, and hardware dependencies for the original Qt measurement and control application; dividing the system into independent functional sub-units based on the software dependency graph and generating natural language functional descriptions; constructing a knowledge context of software and hardware interface mapping relationships using the natural language functional descriptions as input, combined with retrieval ranking and semantic reasoning; generating ArkUI interface code and underlying device access encapsulation classes based on the knowledge context of software and hardware interface mapping relationships, and performing consistency checks and iterative optimization; integrating the optimized system to construct the HarmonyOS engineering architecture; and completing the HarmonyOS system migration of the measurement and control software after system-level consistency verification. This invention achieves high-precision seamless migration from Qt to HarmonyOS, significantly improving the automation and accuracy of the migration.

The application provides a simulation model encapsulation knowledge modeling method based on a knowledge graph, and relates to the technical field of simulation modeling. The method comprises the following steps: modeling encapsulation knowledge by using a graph structure element, obtaining a formal description of the encapsulation knowledge, dividing the encapsulation knowledge into nine encapsulation knowledge categories, formally describing the nine encapsulation knowledge categories, obtaining encapsulation knowledge connotation of the encapsulation knowledge, obtaining encapsulation ontology knowledge of the encapsulation knowledge, and obtaining encapsulation ontology relationship knowledge of the encapsulation knowledge. The method provides a standardized encapsulation framework and process, so that the simulation model to be integrated can be integrated into a simulation platform in a plug-and-play manner, and the difficulty and cost of integration are greatly reduced.