46 results about "Semantic Web" patented technology

The invention discloses a microblog emotion analysis method based on standard dictionaries and semantic rules. The microblog emotion analysis method comprises the following steps: collecting microblog data and manually labeling and marking the emotion value of each microblog; proposing corresponding standard micrblog emotion dictionaries, and establishing an emotion dictionary database; based on the standard emotion dictionaries, adding the semantic rules for assistance, and performing parameter adjustment and optimization on parameters of the semantic rules; based on a real dataset experiment, acquiring the final classification accuracy and precision. The technical scheme provided by the invention is adopted to well analyze the emotion tendency of each microblog user by introducing the standard emotion dictionaries, microblog expression dictionaries and the semantic rules, therefore, higher classification accuracy and precision are achieved.

This invention relates to methods and systems of procuring services in the cloud and specifically procurement where the requirements are defined by the consumer in a machine understandable knowledge representation language such as RDF and OWL using semantic web technology to establish a cloud agent for discovery of a service provider and effectuate the automatic generation of a contract, or service-level agreement, that can be monitored due to the coupling between the machine understandable knowledge representation language and the contract.

The invention relates to a knowledge graph data intelligent management method and system based on a semantic web technology, and the method comprises the steps: obtaining text data from a high-frequency data flow in real time, and generating a first semantic set through segmentation processing and semantic extraction; performing noise filtering and sorting on the multi-source data to generate a second semantic set; constructing semantic representation compatible with the knowledge graph; utilizing a graph embedding algorithm to generate graph updating data through source weight optimization; based on a historical conflict mode and a credibility weighting model, intelligent resolution of semantic conflicts is completed; and generating dynamic situation awareness data through real-time incremental loading and multi-dimensional association analysis. According to the method, through time sequence priority dynamic weighting, multi-source noise accurate filtering and cross-modal credibility evaluation, the problems of response lag, redundancy accumulation and insufficient conflict resolution during high-frequency dynamic data processing of a traditional method can be solved, and therefore real-time updating and consistency maintenance of the knowledge graph are achieved.

A System for providing Interactive Transaction Frameworks, utilizing biometric authentication and semantic web technologies is disclosed. The system registers users with a unique biometric-based identifier, and employs ontologies, schemas, and knowledge graphs to create a semantic foundation. A Large Language Model integrated with the Knowledge Graph Module enables intelligent processing of Interactive Transaction data. The system composes Interactive Transactions with fields mapped to semantic triples, maintains them in an Interactive Transaction Library, and manages user permissions. A Transaction Publishing Platform facilitates Interactive Transaction distribution and submission-response cycles. The system refines inputs and responses, determining outcomes based on these interactions. The system offers a sophisticated, secure, and adaptable approach to Interactive Transaction management and processing across various domains.

The invention relates to the technical field of data management, in particular to a data weaving semantic integration method based on a semantic network and a knowledge graph, which comprises the following steps: acquiring multi-source heterogeneous data and preprocessing to obtain standardized data, the multi-source heterogeneous data comprises structured data, semi-structured data and unstructured data from different business scenes; performing rule injection on the standardized data to generate semantic web ontology data; extracting entities and attribute values thereof in the standardized data to generate structured knowledge graph data of the instance layer; and establishing a semantic association mapping network of the semantic network ontology data and the structured knowledge graph data, and generating a semantic integration result of data weaving. According to the method, the basic differences of the multi-source data in the aspects of formats, codes and the like are eliminated, a unified semantic standard is provided, and conflicts caused by non-unified semantics are reduced.

The invention discloses a civil aviation logistics data exchange method, and relates to the technical field of data processing, and the method comprises the steps: receiving to-be-exchanged data, building an intelligent routing model, carrying out the complementation verification of incomplete to-be-exchanged data information through an automatic correction mechanism, and forming a preprocessing data set; establishing a data transcoding engine to receive the preprocessed data set, and converting the preprocessed data set into a target data set; receiving a target data set, and transmitting the target data set to a next transmission node according to a data exchange path; a common and efficient data exchange path is selected through the intelligent routing model, the delay and error rate of data transmission are reduced, and the effect of improving the overall data exchange efficiency is achieved; an automatic correction mechanism is established to realize address verification and completion, a data transcoding engine is established, and the aviation semantic web technology is used to carry out standardization processing on the data, so that the effects of rapidly solving the basic information problem and improving the data processing efficiency are realized.

The invention relates to the technical field of artificial intelligence, and discloses a smart city monitoring management method based on artificial intelligence, comprising the following steps: step A: data acquisition and preprocessing: acquiring data related to city operation from a plurality of heterogeneous data sources; step B, cross-domain data fusion: carrying out feature extraction and fusion on the preprocessed data; c, performing intelligent analysis and prediction, and applying a machine learning algorithm; and step D, decision support and response: transmitting an analysis result and prediction and early warning information to urban managers and related departments through the system. By introducing an artificial intelligence technology, a deep learning model, a transfer learning technology and a semantic web technology or ontology method, the problem of cross-domain data fusion is effectively solved. The methods can process and analyze heterogeneous data from different data sources and convert the data into a unified and comparable format, so that seamless joint and integration of cross-domain data are realized.

The invention relates to a heterogeneous ontology matching method and system based on BERT and graph contrast learning, and belongs to the field of combination of semantic web and deep learning. The method comprises the following steps: extracting triad information in an ontology through a semantic feature extraction module to construct a corpus, inputting a BERT model fine tuning parameter into the corpus, and generating a semantic feature vector of an entity by using a fine-tuned BERT model; performing graph sampling through a graph comparison learning module according to the topological structure of the ontology graph to generate two views; performing training by selecting positive and negative samples and setting a loss function, and generating an entity comprehensive feature vector; and finally, calculating final similarity scores of different entity pairs by adopting a similarity calculation module. According to the method, the topological structure of the ontology graph can be effectively utilized to extract the structural features, the problem that the traditional structural features cannot fully express the complex structural relationship between entities is solved, and the matching accuracy is improved.

The invention discloses a graph-based document expressive description method and system and a readable storage medium. In order to solve the problem that ambiguity is easily generated when a machine understands a document, a cognitive linguistics interactive recognition mode is used for reference, the machine is endowed with document processing and understanding capabilities, and the machine can interpret the document from different angles by adjusting a framework and cognitive domain parameters. Document information resources are semantized through a semantic web technology, and a heterogeneous graph is modeled in a [word-sentence-piece] level at a framework level. In the fine granularity, an internal graph is constructed by using a [subject-called-object] triple, in the coarse granularity, an association graph is constructed by using words, sentences and documents as nodes, and edge weights are respectively calculated by using normalized point-by-point mutual information NPMI, TF-IDF, BM25 and other algorithms. From the perspective of a cognitive domain, the analysis level and depth are controlled by applying URI hierarchical analysis and a smart graph abstract. According to the method, ambiguity can be eliminated, a sharing model can be constructed, knowledge representation and intelligent retrieval are supported, the document processing quality and efficiency are improved, and a new effective scheme is provided.

The invention discloses a journal paper intelligent typesetting and management method and system, and belongs to the technical field of management, and the method comprises the steps: constructing a journal typesetting parameter knowledge base, and carrying out the structural representation and semantic description of the typesetting requirements of various periodicals through the ontology and semantic web technology, and obtaining a standardized journal typesetting parameter ontology library; introducing a graph-based reference link analysis method for the recognized structured paper content, and converting a reference relationship in the paper into a semantic link graph; the content is uniquely identified through a Hash algorithm, and rapid retrieval and updating are achieved.

This invention relates to the field of aero-engine digitization, and discloses a systematic multi-dimensional digital representation method and system for aero-engines. The method involves decomposing the component structure of an aero-engine hierarchically to form an engine product decomposition structure. Based on the product hierarchy and inclusion relationships of this decomposition structure, a multi-dimensional representation framework for the aero-engine is constructed. Feature factors for each dimension are obtained by extracting associated features from the models or data derived from each dimension. Using these extracted feature factors as variable ontologies, semantic web technology is employed to form a skeleton variable map with these feature factors as its framework. Data is dynamically input into the skeleton variable map to form an aero-engine representation space based on this map. Machine learning algorithms are used to perform representation traversal calculations and analyses on the aero-engine representation space to obtain the representation traversal calculation and analysis results. This method solves the representation challenges of existing representation technologies for complex systems like aero-engines.

The present application relates to a large-scale noise semantic graph entity type error detection method, belonging to the computer technical field. The method comprises the following steps: S1: reasoning and perfecting the noise semantic graph based on the semantic net standard, completing the entity type not explicitly declared in the semantic graph through reasoning, and obtaining the finest type of all entities; S2: entity type error detection based on node semantic embedding and anomaly detection; S3: distribution statistics of semantic graph type-attribute; S4: entity type error detection based on fact triple linkage, and finally obtaining the semantic graph entity type error result.

The application discloses a kind of offshore task planning and deduction simulation analysis method, and the knowledge data of war knowledge base is set war ontology knowledge base, and the model based on event ontology is designed in combination with ontology modeling language, the class of sea battlefield event ontology and the hierarchical structure setting of class are determined, and the sea battlefield rule file model is established to describe sea battlefield combat event, and the sea battlefield combat rule of the sea battlefield rule file model is obtained based on war semantic net rule;Sea battlefield combat rule is inferred according to BN reasoning engine module to obtain the execution event of sea battlefield combat rule;And the execution event is visualized by man-machine interactive system, and the expression of battlefield combat rule based on offshore task planning and deduction simulation is realized.

The application discloses a kind of cement-steel slag solidified soil sustainable evaluation method based on ontology, including setting cement-steel slag solidified soil basic data, according to the evaluation index of cement-steel slag solidified soil of basic data setting;According to the multiple classes corresponding to the attribute of the multiple classes of the ontology model defined by evaluation index definition;The evaluation index evaluation reasoning rule of cement-steel slag solidified soil is formulated using semantic web rule, and the semantic web query rule is defined according to the evaluation index evaluation reasoning rule, according to the parameter corresponding to basic data, the multiple classes are assigned, according to the multiple classes after assignment and the attribute corresponding to the multiple classes and the evaluation index evaluation reasoning rule of cement-steel slag solidified soil, obtain the influence of the basic data to be evaluated on the sustainability of solidified soil.By ontology model and semantic web rule language combination, the inference result is obtained by quantifying evaluation index, the optimal design and optimization direction are obtained from macroscopic point of view, which is conducive to improving the work efficiency of designer.

This invention claims protection for an intelligent fault diagnosis method for rotating machinery based on semantics and capsule networks. The algorithm is implemented using the Keras deep learning framework and semantic web technology, and includes the following steps: Step 1: Preprocessing the original acceleration signal to input into a pre-constructed convolutional layer for automatic feature learning; Step 2: Introducing an Inception module into the capsule network to extract feature representations at multiple scales, preserving the spatial pose information of the features to the greatest extent; Step 3: Constructing and introducing a low-level capsule layer based on weight sharing to share the affine transformation matrix, obtaining predicted feature vectors, and then weighted summing them before inputting them into a high-level capsule layer; Step 4: Introducing a protocol-based dynamic routing algorithm into the high-level capsule layer for iterative weight updates; Step 5: Using rule-based reasoning based on Prolog+Lisp, leveraging existing knowledge in the rotating machinery knowledge domain ontology and the results of IWSCN predictions, to infer the cause of the fault, improving the level of diagnostic intelligence.

The present invention discloses a method for constructing an emergency inspection knowledge base driven by knowledge fusion, belonging to the fields of emergency management and information technology. Specifically, it includes: collecting and preprocessing emergency inspection data from different data sources, extracting and representing emergency inspection knowledge using relation extraction methods and semantic web technologies, and constructing an emergency inspection knowledge graph through knowledge fusion strategies; classifying and coding the knowledge in the emergency inspection knowledge graph according to emergency inspection requirements, and formulating a classification vocabulary; conducting knowledge reasoning based on the emergency inspection knowledge graph, constructing an intelligent question-answering system and an emergency inspection process, and forming an emergency inspection knowledge base; optimizing and improving the knowledge base through functional testing, performance testing, and user feedback; the present invention improves the efficiency and accuracy of emergency inspections.

The application discloses a cross-endpoint associated path retrieval method and system driven by RDF class relation, and belongs to the technical field of semantic web data association. The application carries out preprocessing on data in a SPARQL endpoint, extracts RDF class relation from an ontology and entity relation; the extracted RDF class relation is stored in a graph database in the form of graph data; when a user carries out relation query between entities, inputs a retrieval word and selects a data source; the entity URI of the retrieval word is determined, the entity URI is parsed to determine the class to which the entity belongs, and the associated path of the class is queried in the graph database; the associated path information between the classes that are queried is dynamically encapsulated into a SPARQL federated query statement; the SPARQL federated query statement is executed, and the associated path result of the query is dynamically visualized and displayed. The application can retrieve the associated path between entities across SPARQL endpoints, supports multiple data source endpoints and any associated direction, and improves the efficiency and quality of cross-endpoint associated path retrieval.

This invention relates to a heterogeneous ontology matching method and system based on BERT and graph contrastive learning, belonging to the field of semantic web and deep learning integration. The invention constructs a corpus by extracting triple information from the ontology using a semantic feature extraction module. This corpus is then input into a BERT model for parameter fine-tuning, generating semantic feature vectors for entities. A graph contrastive learning module performs graph sampling based on the topological structure of the ontology graph, generating two views. Training is performed by selecting positive and negative samples and setting a loss function to generate a comprehensive entity feature vector. Finally, a similarity calculation module calculates the final similarity score for different entity pairs. This invention effectively utilizes the topological structure of the ontology graph to extract structural features, overcoming the problem that traditional structural features cannot fully express the complex structural relationships between entities, thus improving matching accuracy.

Example methods and systems are directed to determining topics of data objects. A machine learning model may be trained and used to determine topics of data objects. After topics for data objects are determined by the trained machine learning model, data objects having similar topics can be automatically related. A semantic web approach relies upon the metadata of the data objects being generated along with the metadata of the insights being generated (such as topic groups). Such a semantic association between various objects (using metadata) forms a metadata driven network of analytical representation of business entities / objects. A data-stream comprising the semantic web, indicating the relationships between the metadata of the data objects and the metadata for the topics, may be pushed continuously into a central tool or repository to allow users to generate seamless analytical dashboards with minimal efforts.

The invention provides an equipment fault prediction method and equipment based on a power grid equipment semantic network. According to the implementation scheme, under the condition that the operation state of target power grid equipment is abnormal, attribute information of a corresponding entity node in a first power grid equipment semantic net is updated based on the operation state of the target power grid equipment; under the condition that attribute information of a first entity node in the first power grid equipment semantic network is updated, based on a rule edge connected with the first entity node, in the first power grid equipment semantic network, changing a connection relationship between the rule edge and a second entity node associated with the first entity node to obtain a second power grid equipment semantic network; determining an influence subgraph of the target power grid equipment from the second power grid equipment semantic network; and performing fault analysis on the influence subgraph of the target power grid equipment to obtain a fault influence range of the target power grid equipment. According to the invention, the accuracy of the fault influence range of the power grid equipment can be improved.

The invention discloses a distributed database inspection system, method and device and a medium, and relates to the technical field of database management and operation and maintenance, and the system comprises a data collection module used for database operation data, node states and service association data; the federated learning module generates a global model by encrypting and aggregating local model parameters of each node; the semantic network module analyzes and reasones the multi-source data based on the ontology model to generate a fault risk tag; the reinforcement learning module combines the global / local model difference value, the operation data and the fault label to construct a state space, and generates an action instruction packet; the voiceprint recognition module triggers a mandatory authentication process before the instruction is executed; an execution result is fed back to the reinforcement learning module to update the strategy, and the semantic network module is driven to optimize and update data of the target database. By fusing federal cooperative training, autonomous decision and semantic reasoning are enhanced, and the operation and maintenance efficiency and safety of the distributed database are remarkably improved.

A data processing system (5) for a motor vehicle is provided, wherein the data processing system (5) is designed to receive user-related and / or vehicle-related input data elements (a, b, c) (S1), to convert the input data elements (a, b, c) into a uniform data format (S2) that can be processed using a semantic web technology by means of a semantic data layer (10), and to process the converted input data elements (a', b', c') using the semantic web technology (S3).The processing (S3) includes converting (S31) the converted input data elements (a', b', c') to generate further data (A, B, C) designed to be linked in a knowledge graph (12) taking into account predetermined rules, linking (S32) the generated further data (A, B, C) taking into account the predetermined rules in the knowledge graph (12), and generating (S33) an output data element (D) using the further data (A, B, C) linked in the knowledge graph (12).

The invention discloses a cooperative override trip prevention decision-making system for a power supply network, and relates to the technical field of power system protection and control, and the system comprises a data collection module which is connected with a multi-level protection device, a monitoring terminal and an environment sensor in the power supply network, and is used for obtaining electrical quantity data, non-electrical quantity data and environment parameters in real time. According to the cooperative override trip prevention decision-making system for the power supply network, cross-protocol analysis and space-time fusion of multi-source heterogeneous data are realized through a multi-dimensional space-time alignment engine, a dynamic semantic gateway and a wavelet packet decomposition technology are combined, a power frequency quantity, a transient quantity and an environmental parameter are uniformly mapped to a fault characteristic spectrum, and the fault characteristic spectrum is analyzed and analyzed. The problem of incomplete fault feature extraction caused by data islands in the prior art is solved, key fault features can be accurately screened, the Pareto optimal action sequence can be generated, the misjudgment rate of override trip is reduced, meanwhile, the unnecessary power failure duration is shortened, and the fault isolation accuracy and the power supply reliability are improved.

The invention provides a geographic semantic web spatio-temporal knowledge extraction method, which belongs to the technical field of data processing, and specifically comprises the following steps: acquiring multi-source geographic semantic web data, and performing data type judgment and data field automatic analysis on the multi-source geographic semantic web data; constructing a geographic semantic network to extract an ontology, and performing self-adaptive matching on an ontology field and a data field obtained by analysis; the method comprises the following steps: constructing a GeoSemanticWeb2Knowledge extraction framework, operating the GeoSemanticWeb2Knowledge extraction framework, and carrying out adaptive knowledge extraction on multi-source geographic semantic network data; constructing a spatial screening constraint condition, and performing spatial screening on the spatio-temporal knowledge triad; constructing a topic screening mechanism based on semantic similarity matching, and carrying out topic screening on the spatio-temporal knowledge triad according to the topic screening mechanism; and carrying out space-time knowledge dynamic reasoning and updating based on evolution mode driving. Through the scheme of the invention, the extraction efficiency, accuracy and interpretability are improved.

An automated generation of an assembly language (A‑code) for a reconfigurable flexible assembly line includes: extracting geometric, topological, and material information from a three-dimensional product model; constructing an assembly process knowledge graph to define assembly objects, resources, and procedures; instantiating stored data to form a knowledge base; using a community detection algorithm to divide subassemblies and simplify assembly sequence planning; generating an optimal assembly sequence based on subassemblies and a priority relationship matrix; optimizing assembly process allocation and assembly unit layout; formulating SWRL rules and SPARQL query statements; and generating A‑code through reasoning using semantic web tools. Automated A‑code generation and verification ensure program correctness. By automatically extracting assembly information and generating assembly programs directly from the three-dimensional model of the product, the present invention eliminates the step of manually rewriting the assembly program based on product changes in traditional methods, significantly shortening the time for assembly line reconstruction, improving production line stability and product quality, and increasing production efficiency and the ability to respond to market changes.

The invention provides a complex multivariate combination model comprehensive element intelligent extraction and recombination publishing method, and relates to the field of system comprehensive modeling simulation. The complex multivariate combination model comprehensive element intelligent extraction and recombination publishing method is realized through the following technical steps: step 1, preparing and preprocessing multi-source model data, analyzing a *. Mo file of a target system through a Modelica compiler, and generating an AST syntax tree containing component declarations and equation sets; and importing STEP format geometric data of the CAD model, and carrying out grid repair and feature edge extraction by using Paraview. According to the method, a lightweight digital fingerprint algorithm oriented to multi-source heterogeneous data is developed, while encryption strength is kept, feature extraction time of a gigabit-level CAD model is shortened to be within 30 seconds, an interpretable intelligent confirmation framework is constructed, a model element feature map is constructed, a technical barrier is broken through, a model element association rule base based on a semantic network is developed, and a multi-source heterogeneous data encryption algorithm is developed. And automatic binding and intelligent extraction of the geometric parameters and the multi-physical field equation are realized.

The application provides a geospatial semantic web spatio-temporal knowledge extraction method, and belongs to the technical field of data processing, and specifically comprises the following steps: acquiring multi-source geospatial semantic web data, and performing data type judgment and automatic data field analysis on the data; constructing a geospatial semantic web extraction ontology, and adaptively matching the ontology field with the analyzed data field; constructing a GeoSemanticWeb2Knowledge extraction framework and running the framework to adaptively extract knowledge from the multi-source geospatial semantic web data; constructing a spatial filtering constraint condition to perform spatial filtering on the spatio-temporal knowledge triple; constructing a topic filtering mechanism based on semantic similarity matching to perform topic filtering on the spatio-temporal knowledge triple; and performing spatio-temporal knowledge dynamic reasoning and updating based on an evolution mode. Through the scheme, the extraction efficiency, accuracy and interpretability are improved.

This invention aims to propose a proactive data collection method based on semantic expansion. Initial keywords describing the topic are set. The keywords are then used to identify corresponding initial entities in the semantic web. These initial entities are then expanded using upward and downward expansion methods to obtain several new entities. A classification model is then constructed and used to predict the probability of each new entity being recommended as a relevant entity. The relevance weight of each new entity is calculated and ranked from high to low. The new entities with the highest scores are selected as relevant entities. This process is repeated to obtain more relevant entities describing the topic and their scores. This method can effectively semantically expand the initial keywords.

The invention provides a cognitive path cross-space tracking and visualization method based on a DIKWP * DIKWP semantic network interaction structure, which is characterized in that a semantic interaction network is constructed between a source model and a target model on the basis of a data-information-knowledge-intelligence-intention five-layer model, and hierarchical mapping and alignment of cognitive contents are realized. According to the method, AI reasoning activities are synchronously tracked in concept and semantic spaces, paths from input to output are recorded as cross-layer sequences, and abnormal behaviors such as skip reasoning, semantic fold-back and intention distortion are detected. When the model faces an unclear, uncertain and inconsistent situation, the deviation type and degree can be marked and explained. A matched visualization module visually displays a cognitive path through a multi-dimensional atlas, wherein a thermodynamic diagram shows attention, a broken line marks a process, abnormal nodes are highlighted, and target consistency is analyzed and evaluated by intention. The method can be applied to white-box evaluation, multi-model comparison, semantic consistency detection and AI ethical monitoring, the interpretability, transparency and safety controllability of an AI system are improved, and the method has important application value.

Implementations disclosed herein provide systems and methods that determine relationships between objects based on an original semantic mesh of vertices and faces that represent the 3D geometry of a physical environment. Such an original semantic mesh may be generated and used to provide input to a machine learning model that estimates relationships between the objects in the physical environment. For example, the machine learning model may output a graph of nodes and edges indicating that a vase is on top of a table or that a particular instance of a vase, V1, is on top of a particular instance of a table, T1.