125 results about "Relational knowledge" patented technology

The invention discloses a knowledge graph construction method based on a fine-tuning large language model, and the method comprises the following steps: S1), data preprocessing: S2), extracting knowledge points and relationships in a data source, and obtaining preprocessed knowledge points and relationships; s3) disambiguating knowledge points and relationships; and S4) constructing a knowledge graph, forming a dynamic extensible knowledge graph, regularly using the AI model to re-analyze the text data in the later use process, and updating the knowledge graph. According to the method, the knowledge points are automatically extracted by utilizing the knowledge point extraction fine tuning model to determine the entity and the relation edge, the knowledge points are further identified through multiple disambiguation means, and the complex relation between the knowledge points is constructed, so that the efficiency and the accuracy of data processing are effectively improved; and finally, storing the data subjected to ambiguity elimination processing into a graph database model to construct a knowledge graph, thereby realizing automatic construction and updating of the knowledge graph.

A computer-implemented method of generating an answer from an input query and input documents, comprising extracting input query entities from the input query and input document entities from the input documents, sampling a schema of in-domain queries with the input query to generate a query sampled schema, generating an entity-document graph from the input documents and input document entities, generating a hyper-relational knowledge graph by extracting, for each input query entity, a document title and relation to an input document entity of the input document entities from the input documents in the entity-document graph, sampling the hyper-relational knowledge graph with the query sampled schema to generate a query focused hyper-relational knowledge graph, predicting an answer to the input query by inputting the query focused hyper-relational knowledge graph and input query into a pretrained neural network, outputting the answer.

The invention discloses a decision support method for time-space dynamic data of Internet of Vehicles based on knowledge retrieval enhancement, which comprises the following steps: collecting perception data inside and outside a vehicle, and separating general data and private data; preprocessing the data, and compressing and encrypting the private data; performing data processing by adopting a data knowledge dual-drive mode and a large language model, and converting the data into structured data; constructing a super-relation knowledge graph, and carrying out vectorization processing based on an embedded vector model; performing node importance evaluation based on the super-relation knowledge graph, and performing data updating and increment; storing the private data locally, uploading the general data to a public database of the cloud, and defining a dynamic access range of the public database; and extracting a user questioning entity by using the local entity recognition model, matching data existing in the local and the cloud, obtaining background knowledge contents, and generating an enhanced retrieval result. According to the method, the problems of acquisition of decision support information and use safety of data in an Internet of Vehicles environment are solved.

The invention discloses a quantitative source tracing method for river and lake water pollution in combination with a knowledge graph and machine learning. The invention relates to the technical field of water quality pollution traceability, and the method comprises the steps: collecting and processing target river section data, constructing a hydrodynamic force-water quality model, and obtaining river downstream pollutant diffusion characteristics and target section concentration time sequence characteristics under different discharge situations of each discharge port based on the discharge port position of a target river section through a unit pulse response test; the method comprises the following steps: constructing a source intensity-time-concentration relation knowledge graph, randomly extracting a sample set from the graph, training the sample set by adopting a machine learning method, learning a nonlinear mapping relation between a downstream section concentration time sequence and multi-outlet source intensity, carrying out dynamic inversion of a river pollutant diffusion process, and realizing rapid positioning and contribution quantification of a pollution source. And finally, generating probability distribution of pollution source positions by adopting a Monte Carlo sampling method to assist in improving the traceability judgment priority, thereby improving the accuracy and response speed of river water quality pollution traceability.

The invention relates to the technical field of knowledge graph construction, in particular to an intelligent customer relationship knowledge graph construction method, which comprises the following steps of: firstly, acquiring customer data from a multi-source data source; preprocessing the collected data, and protecting sensitive information by adopting a differential privacy technology; defining a mode of a knowledge graph, identifying an entity by adopting a BERT model, extracting a cross-domain relationship in combination with a graph neural network, and constructing a preliminary customer relationship knowledge graph; designing an incremental updating algorithm, and updating the knowledge graph in real time or regularly; based on the constructed customer relationship knowledge graph, a Cypher query language and a graph algorithm are applied to mine customer relationships; performing deep mining on the customer relationship by using a graph algorithm, and identifying key customers, potential customer groups and association relationships among the customers; in this way, the technical problems that in the prior art, when the knowledge graph is constructed, the data processing efficiency is low, relation extraction is inaccurate, and the knowledge graph is not updated in time are solved.

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 computer information, in particular to a joint combat target system data service system based on a large model. The system comprises a target system data processing module, a combat target feature fusion module, a combat relation knowledge graph construction module and a combat system service early warning module, and can obtain joint combat target system data and perform format classification division and target cleaning processing to obtain joint combat target system standard data; performing feature fusion mapping on the standard data of the joint combat target system to obtain combat target system fusion feature vector representation; and performing entity identification and relation extraction on the combat target system fusion feature vector representation, performing relation combination weight graph construction and combat service query and situation early warning analysis, generating combat situation early warning information, and providing a corresponding joint combat coping strategy suggestion scheme. According to the invention, the data processing and combat service mode of the joint combat target system can be innovated by using a large model technology.

The invention discloses an enterprise actual holder identification and risk conduction relation mining method and equipment. The method comprises the following steps: performing data acquisition from a data source, preprocessing the acquired data, and performing entity identification and relationship extraction according to the acquired data; according to the identified entity and the extracted relationship, constructing a relationship knowledge graph; deducing the relation knowledge graph based on a graph neural network model to mine a potential relation of an undominant record; analyzing a risk conduction path and quantifying conduction influence; and a visual interaction interface is provided, and result display and model optimization are realized. According to the method, an AI large model is taken as a core, knowledge graph construction and graph neural network reasoning technologies are combined, enterprise actual holder identification and risk conduction relation mining are completed through a multi-step process, diffusion paths and intensity changes of risks in a multi-level stockholder network can be simulated, key risk nodes and high-influence paths can be identified, and the risk prediction accuracy is improved. And risk conduction prediction and early warning capabilities are provided for a supervision mechanism.

The invention provides a medical intelligent causal decision-making and scheduling system based on an improved large language model, and the system comprises a pre-training language model module which is used for processing unstructured medical text data, and extracting a text feature vector and a medical entity relationship; the knowledge graph enhancement module is used for constructing a medical knowledge graph based on the medical entity relationship, and reasoning to generate a knowledge enhancement feature vector; the multi-modal feature fusion module is used for generating a fusion feature vector based on the text feature vector and pre-stored structured data; the dynamic time sequence modeling module is used for generating a time sequence prediction vector based on the fusion feature vector and the knowledge enhancement feature vector; the self-adaptive scheduling optimization module is used for making a medical scheduling decision based on the time sequence prediction vector and generating a scheduling scheme; and the scheduling calibration module is used for matching similar case features for the scheduling scheme through a context learning mechanism based on the knowledge enhancement feature vector and calibrating the similar case features to generate an optimized scheduling scheme. And medical resources and scheduling can be allocated more accurately.

A large-scale language model for adapting to an industrial domain is provided, a graph database stores a domain-specific knowledge graph containing instance-level semantic information about the industrial domain, including true triple statements about physical entities from the industrial domain and their interrelation. A knowledge graph to corpus translator converts the domain-specific knowledge graph into a natural language corpus. A domain adaptation component pre-trains the large-scale language model with the natural language corpus to a provide a domain-adapted large-scale language model. This approach does not require any assumptions on the structure of the domain-specific knowledge graph, for example, it does not require the domain-specific knowledge graph to contain only instance data. Neither does it need linguistic templates since the verbalization of triples can be built on their existing labels or relation names. The triple-based information encoding of RDF facilitates a natural translation into language during the converting operation.

A computer-implemented database management system for communicating with two or more databases, at least two of the two or more databases having a different database structure, is provided. The system comprises a semantics component, a knowledge graph component, and a query interface. The semantics component obtains data items from the databases and determine relationships therebetween. The knowledge graph component generates a knowledge graph based on the identified relationships. Each node of the knowledge graph is associated with a respective data item stored in the databases and defines a structured database query for accessing the respective data item in the databases. The query interface receives a user input, determines a knowledge graph query based on the user input, and provides the knowledge graph query to the knowledge graph component for identifying at least one node of the knowledge graph corresponding to the knowledge graph query, thereby obtaining a corresponding database query.

The invention relates to the technical field of knowledge graph construction, in particular to a chemical industry park industrial chain data knowledge graph construction method and system based on big data. The method comprises the following steps: acquiring industrial chain data of a chemical industry park; nodes and edges of the knowledge graph are defined, an evolution graph convolutional network model is used for predicting the number change relation of adjacent nodes and placing the number change relation in the edges, and a product reserve relation knowledge graph is constructed; introducing timestamps to update node definitions, converting the product reserve relationship knowledge graph into a product time relationship knowledge graph by using a dynamic time sequence graph network algorithm, and combining repeated routes and products of the chemical industry park to obtain a final product time relationship knowledge graph; and displaying the product reserve relationship knowledge graph and the product time relationship knowledge graph by using a visual interface. According to the chemical industry park industrial chain data knowledge graph construction method and system based on big data, time sequence management and dynamic optimization of a product reserve relationship are realized by constructing two knowledge graphs.

The invention discloses a collaborative recommendation method and device fusing lightweight relation path completion and dynamic negative sampling, equipment and a medium, and relates to the technical field of recommendation. The collaborative recommendation method comprises the following steps: acquiring historical interaction data of a user, and constructing an interaction relationship knowledge graph; vectorizing the interactive relationship knowledge graph, and then dynamically constructing a multi-hop relationship path based on a lightweight relationship path completion mechanism to obtain a path completion representation. According to the lightweight relation path completion mechanism, a path order L belongs to {1, 2, 3} is set according to the sparse degree of a user-article pair, and a first-order, second-order or third-order reasoning path is constructed. And then performing intelligent feature fusion on the constructed path through a triple pooling strategy. And projecting the embedded representation of the entity to the corresponding relation subspace through orthogonal projection based on the path completion representation. And calculating a recommendation score according to the embedded representation after projection, and obtaining an article recommendation list.

The invention discloses an unregistered geographical name discovery and spatial position reasoning method and a related device, and relates to the technical field of geographic information, and the method comprises the steps: constructing a geographical name space-time derivation relation knowledge graph based on an open source geographic database; inputting the target place name text data into a sequence labeling model to obtain a labeling result, and extracting candidate derived place names based on the labeling result; performing general name segmentation on the candidate derived geographical names to obtain potential basic geographical names, and judging whether the potential basic geographical names meet preset association conditions or not based on a geographical name space-time derivation relationship knowledge graph; if yes, eliminating potential basic place names; if not, determining the potential basic place name as an unlogged place name; and determining the spatial position of the unregistered place name according to the general name of the unregistered place name and the place name space-time derivation relationship knowledge graph. According to the invention, the automation degree of unregistered place name discovery and spatial position reasoning can be improved.

The invention provides a spatial knowledge graph completion method based on a topological relation, and relates to the technical field of spatial knowledge graphs. Constructing the topological relation into a topological relation knowledge graph; taking the relation prediction task as a downstream task of space entity embedding learning, learning topological relation embedding of the space entity, and constructing a topological relation prediction model; training a topological relation prediction model by using the topological relation knowledge graph, and training the topological relation prediction model; constructing a spatial knowledge graph completion model, and fusing topological relation embedding of spatial entities and spatial knowledge graph structure embedding; training the spatial knowledge graph completion model; and training a completion model in the spatial knowledge graph, calculating the confidence coefficient of the triple, and taking the triple with the highest confidence coefficient as supplementary knowledge.

The invention discloses a defect hidden danger data diagnosis method based on a distribution network SVG single line diagram. The method comprises the steps that basic analysis is conducted on the input SVG single line diagram, basic geometric figure elements are extracted and normalized, a topological connection relation is preprocessed, and a multi-level index structure is constructed; a dynamic topology path reasoning mechanism is constructed, and reasoning and self-adaptive optimization of the topological relation of the electrical equipment are realized through path consistency calculation, topological entropy minimization and a dynamic topology updating strategy; a self-adaptive implicit relational knowledge graph is constructed, and semantic association between the electrical equipment is established through explicit topological relation fusion, implicit function dependence reasoning and dynamic semantic embedding optimization; and constructing a multi-modal time sequence feature vector, calculating the hidden danger risk of the equipment by adopting a space-time defect probability estimation model, and giving an alarm through a dynamic risk early warning strategy to realize prediction and early warning of the hidden danger of the defects of the electrical equipment. According to the method, the problems of semantic understanding, topological reasoning, state inference and defect prediction of the SVG single line diagram are effectively solved.

The invention relates to the technical field of drug safety evaluation, in particular to a multi-center combined evaluation method and system for drug safety based on a graph neural network and federated learning. Known and unknown drug interaction is systematically predicted based on a drug multi-relation knowledge graph and a graph neural network, a key path of DDI is identified through a graph attention mechanism, a molecular mechanism of the interaction is revealed, and natural language interpretation is generated. And meanwhile, through privacy protection and multi-center cooperation, a federal learning architecture is utilized to break data islands and improve the external effectiveness of an evaluation conclusion on the premise of protecting patient privacy and meeting data compliance requirements. The heterogeneity of data of different mechanisms is effectively evaluated through distribution deviation detection, deviation caused by blind extrapolation is avoided, a real-world evidence methodology report and a data traceability auditing clue are automatically generated, the requirement of a supervision mechanism for real-world evidence quality is met, medicine supervision decision is supported, and medicine research, development and review are accelerated.

A computing platform may configure a dependency knowledge graph indicating file dependencies for mainframe applications, and an error knowledge graph indicating errors and corresponding solutions for the mainframe applications. The computing platform may receive mainframe source code. The computing platform may analyze, using the knowledge graphs, the mainframe source code to identify potential errors and corresponding solutions. Based on identifying an error in the mainframe source code, the computing platform may cause the mainframe source code to be updated according to the corresponding solution. The computing platform may analyze, using the dependency knowledge graph and the error knowledge graph, the updated mainframe source code to identify remaining errors. Based on identifying an absence of the remaining errors, the computing platform may send, to a mainframe build and deployment engine, the updated mainframe source code, which may cause the mainframe build and deployment engine to automatically execute a build process.

The invention discloses an intelligent dialogue memory management method and system based on the logistics field, and relates to the technical field of logistics intelligent dialogues, and the method comprises the steps: obtaining the historical logistics dialogue data of a target user, generating a user feature-interactive entity-semantic relationship initial knowledge graph based on a QWen2.5-32B large language model, and constructing a Milvus semantic vector library; monitoring a logistics dialogue flow in real time, and starting an adaptive memory management mechanism by taking three rounds of dialogue as a judgment threshold value; extracting user dialogue content attribute features and behavior preferences, and dynamically updating a user feature-interactive entity-semantic relationship initial knowledge graph and a Milvus semantic vector library; and triggering a logistics dialogue according to a target user, starting a multi-modal memory recall mechanism, cooperatively retrieving a user feature-interactive entity-semantic relationship knowledge graph and a Milvus semantic vector library, generating a personalized intelligent dialogue response, and realizing a logistics intelligent dialogue memory management closed loop. The beneficial effect of the invention is that the intelligent and personalized capabilities of the system are enhanced.

The invention discloses a multi-modal semantic understanding method and system oriented to an unstructured PDF document, and relates to the related field of data processing.The method comprises the steps that a relational knowledge representation plan is called to analyze a target PDF document, a target relation framework is obtained, cross-modal alignment processing is conducted on the target relation framework, and a target alignment framework is obtained; performing multi-modal interaction analysis on the target alignment framework to obtain target fusion information; performing reconstruction processing on the target PDF document based on the target fusion information to obtain a target reconstruction document; and taking the semantic information of the target reconstructed document as multi-modal semantic understanding of the target PDF document. The technical problem that the semantic understanding precision is insufficient due to the fact that modal semantic association is missing and interaction is insufficient in existing unstructured PDF document-oriented multi-modal semantic understanding is solved, and the technical effect of improving the semantic understanding precision by integrating the multi-modal information in the document is achieved.

The invention provides a data question answering method and device and a medium, and relates to the technical field of computers. The method comprises the steps of obtaining a data problem and determining that the data problem points to a first data table generated by continuous operation; positioning a first node corresponding to the first data table in the data blood relationship knowledge graph of the continuous operation; obtaining a preorder data consanguinity knowledge graph comprising the first node and the preorder data consanguinity of the first node; analyzing the preorder data blood relationship knowledge graph by using a large language model (LLM) to obtain an answer to the data question; wherein the data blood relationship knowledge graph records the dependency relationship of each data table in the continuous operation process and the operation on each data table in the continuous operation process. According to the method, the large model is used for analyzing the data blood relationship knowledge graph related to the data question, the data question is answered, automatic question answering of the data question is achieved, the data operation efficiency is improved, and the data operation cost is reduced.

The invention discloses a multivariate time series anomaly detection method and system based on spatio-temporal knowledge. The method comprises the steps that specified sensor data or index data form multivariate time series data; periodic time sequence decomposition and causal discovery are carried out to construct a space-time knowledge graph, nodes in the space-time knowledge graph represent the state of each variable in specific time, directed edges represent the causal relationship between the variables, and the weights of the directed edges reflect the causal influence intensity; performing data preprocessing and feature extraction on the multivariate time sequence data to obtain sequence features; extracting space-time synchronous attention features from the space-time causal knowledge graph and the sequence features by using a space-time synchronous attention network; and performing anomaly detection based on the spatio-temporal synchronization attention features to determine whether the device or the network is abnormal or not. The invention aims to simultaneously model complex short-term and long-term space-time dependencies for multivariate time series anomaly detection and fuse prior causal relationship knowledge to improve the anomaly detection accuracy and reduce false alarms.

The invention provides an intelligent warehouse integrated management and control system for port logistics, and relates to the technical field of port warehouse management, and the system comprises a graph module which is configured to be used for constructing an operation relation knowledge graph based on the entity relation of a port warehouse, the nodes of the knowledge graph represent warehouse operation elements, and the edges of the knowledge graph represent the incidence relation between the warehouse operation elements; the analysis module is connected with the graph module and is configured to be used for monitoring the node state of each node in the knowledge graph and reasoning the potential influence of the abnormal node state on the global operation plan by analyzing the association relationship between the nodes when the abnormal node state is monitored; and the strategy generation module is connected with the analysis module and is configured to be used for generating a target coping strategy according to the potential influence and outputting a corresponding management and control instruction. According to the invention, the risk that local abnormity is not disposed in time and is evolved into global operation interruption is avoided, so that stable execution of a port logistics overall operation plan is guaranteed, and the throughput efficiency and operation toughness of a warehouse are improved.

The invention discloses an AI artificial intelligence-driven tax, accounting and auditing method and system, and relates to the technical field of tax and financial management, and the method comprises the following steps: S1, data collection and preprocessing; s2, performing iterative optimization on the xLSTM model through an improved Adame optimizer; s3, extracting a financial data articulation knowledge graph; s4, tracking an abnormal source by using the articulation knowledge graph; s5, generating a comprehensive regulation suggestion; and S6, generating a standardized audit report. According to the method, the problems of long manual calculation time, low tax risk prediction precision, auditing response lag and the like in a traditional financial management method are solved, and an efficient, accurate and real-time financial management technical solution is provided through intelligent automatic analysis.

The invention relates to a tin smelting process flow super-relation knowledge graph embedding representation method, and belongs to the technical field of knowledge representation. The method comprises the following steps: based on each stage of a tin smelting process flow, collecting and sorting a to-be-constructed tin smelting process flow field knowledge text; setting cue words extracted by the tin smelting process flow super-relation fact information; the large language model extracts a super-relation fact in the to-be-constructed domain knowledge text according to the cue word, and a super-relation knowledge graph is constructed; arranging the extracted super-relation facts as samples in a training set for training a representation model; and the final embedding of the tin smelting process flow knowledge graph is obtained through the trained characterization model and is used for downstream tasks. According to the method, knowledge-based expression of the tin smelting process flow is realized, and digital and intelligent development of the tin smelting industry is promoted.

The embodiment of the invention discloses a key anaphora analysis method and device in voice-to-text conversion, a medium and a product. The method comprises the following steps: constructing an interpersonal relationship knowledge graph of a user and splitting according to an interpersonal relationship type; when the text sequence of the user voice conversion contains the third-person pronouns and meets a preset condition, acquiring context information and acquiring anaphora objects of the third-person pronouns, a relationship type between the anaphora objects and the user and the confidence coefficient of the relationship type; sorting and traversing the relation sub-atlases based on the relation type and the confidence coefficient to obtain candidate relation sub-atlases; and when the reference object is successfully matched with any one of the sub-nodes in the candidate relation sub-graph and the first annotation fields of the sub-nodes, acquiring a character sequence containing a correct third-person pronoun based on the second annotation fields of the sub-nodes. According to the method, the anaphora objects of the third-person pronouns are obtained through semantic analysis, and the anaphora objects are matched in a multi-dimensional mode in combination with the interpersonal relationship knowledge graph so as to obtain the correct third-person pronouns.

The invention discloses a big data relation mining analysis method based on a graph neural network, which comprises the following steps: S1, acquiring multi-source data, extracting data features, constructing a data relation graph, and mapping the data relation graph into a low-dimensional vector as a basic data structure; s2, inputting a low-dimensional vector through the graph neural network model, outputting a prediction result, calculating an error by using a loss function based on a real relation label, adjusting parameters of the graph neural network model, and obtaining a target data model; s3, obtaining a data relationship prediction result through the target data model, mining a data potential relationship, and mining a data relationship result through the data potential relationship; and S4, combining a data relationship result with domain knowledge and business rules, constructing a relationship knowledge base, and displaying the data relationship through a visualization technology. According to the method, efficient and accurate big data relationship mining is realized, and the accuracy and practicability of potential relationship discovery in a complex data environment are improved.

The invention provides an identification analysis data governance and association method and system based on an AI large model, and the method comprises the steps: carrying out the pre-classification of a new registration identification through an identification analysis registration module in the AI large model, and determining the industry type of the new registration identification; using a template industry attribute matching module in the AI large model to perform template classification on the new registration identifier, and determining a template category to which the new registration identifier belongs; performing identification analysis on the industry category and the template category by utilizing the inter-enterprise relationship knowledge graph, and mining enterprise information involved in identification analysis data; based on the preset association preconditions, causal association and process association between enterprise information are established, and through automatic identification analysis classification and identification, the association relationship between different identification analyses is automatically mined, so that the treatment level and value mining capability of identification analysis data are effectively improved; and powerful technical support is provided for intelligent development of an industrial internet identification analysis system.

The invention discloses a causal relationship knowledge graph construction method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting input data which comprises topological structure data, performance index data, event log data and external environment data of a network; constructing a causal relationship knowledge graph between network entities based on the input data; generating potential causal relationship candidate pairs of the causal relationship knowledge graph by using a causal discovery algorithm; and optimizing the causal relationship knowledge graph on the basis of the causal relationship candidates to obtain an optimal causal relationship knowledge graph, thereby being beneficial to solving the problem that in the prior art, in large-scale wireless network diagnosis, the causal relationship between network entities is difficult to accurately and efficiently excavate, so that the causal information cannot be effectively utilized to perform network fault diagnosis.

The invention discloses a data insight report generation method and system based on knowledge enhancement and fact verification. Building a domain index relation knowledge graph by training a large language model; mapping the insights to a map, constructing a weighted insights map, carrying out spectral clustering, and dividing insights theme areas; a viewpoint unit is generated through the writing agent, and consistency verification and correction are carried out through the fact verification agent; the user intention is analyzed, candidate topics are screened, and a report outline is generated through heuristic search arrangement based on SRCI scoring, semantic progression and logic entropy calculation; finally, the verified viewpoint units are integrated according to the outline, and a final report is output. According to the method, the full-automatic and high-credibility report generation is realized, and the accuracy, logicality and interpretability are improved.