Patents
Literature
13 results about "Semantic data model" patented technology
Efficacy Topic
Property
Owner
Technical Advancement
Application Domain
Technology Topic
Technology Field Word
Patent Country/Region
Patent Type
Patent Status
Application Year
Inventor
Semantic data model(SDM) is a high-level semantics-based database description and structuring formalism (database model) for databases. This database model is designed to capture more of the meaning of an application environment than is possible with contemporary database models. An SDM specification describes a database in terms of the kinds of entities that exist in the application environment, the classifications and groupings of those entities, and the structural interconnections among them. SDM provides a collection of high-level modeling primitives to capture the semantics of an application environment. By accommodating derived information in a database structural specification, SDM allows the same information to be viewed in several ways; this makes it possible to directly accommodate the variety of needs and processing requirements typically present in database applications. The design of the present SDM is based on our experience in using a preliminary version of it. SDM is designed to enhance the effectiveness and usability of database systems. An SDM database description can serve as a formal specification and documentation tool for a database; it can provide a basis for supporting a variety of powerful user interface facilities, it can serve as a conceptual database model in the database design process; and, it can be used as the database model for a new kind of database management system.
Intelligent number asking method, device and system based on semantic data model and large model
The invention discloses an intelligent number asking method, device and system based on a semantic data model and a large model, and belongs to the field of artificial intelligence. After a current question of a user is received, a task plan corresponding to the current question of the user is obtained based on the large model, then MQL information extraction is performed based on the task plan to obtain a target MQL, then a word semantic data model converts the target MQL to obtain an SQL query statement, query is performed based on the SQL query statement to obtain reply data, and the reply data is sent to the user. And finally, returning the reply data to the user. According to the technical scheme, the natural language is converted into the MQL through the large model, then the MQL is converted into the SQL through the semantic data model, and compared with a traditional scheme that the natural language is directly converted into the SQL, data training does not need to be carried out; and the large model can accurately identify the user intention, so that the obtained MQL unified semantic layer provides a standard caliber, and the reply accuracy is greatly improved.
Owner:BEIJING DIPU TECH CO LTD
Self-healing generative ai / ML pipeline for generating complex data queries leveraging semantic data model
PendingUS20260003879A1Digital data information retrievalSpecial data processing applicationsTheoretical computer scienceData access
A method includes providing a user query to an AI / ML pipeline. The user query requests a response based on data stored in a data topology, and the data topology is modeled using a semantic data model. The method also includes generating an initial data access query for retrieving the data from the data topology using the AI / ML pipeline and the semantic data model. The method further includes determining that the initial data access query includes a hallucination or error and performing an automatic loop one or more times. The automatic loop includes generating an updated data access query for retrieving the data; determining whether the updated data access query includes a hallucination or error; and, if so, repeating the automatic loop. In addition, the method includes using a final data access query with no hallucination or error to retrieve the data from the data topology in order to generate the response.
Owner:GOLDMAN SACHS & CO LLC
Computer system and method for classifying assets in automated and industrial control systems
Classifying one or more assets in an automated and industrial control system (AIC) according to a classification standard. In a computer monitoring tool, a classification query is received for an asset managed by the AIC. Responsive to this classification query, the computer monitor tool retrieves a listing of candidate ontology classes for the queried asset utilizing information received from a semantic data model of known assets. The computer monitor tool then captures, preferably from a database coupled to the AIC, certain classification attribute variables associated with the queried asset. Additionally, the computer monitor tool receives user information describing certain building information associated with the queried asset. The computer monitor tool then generates a computer query configured for requesting results from a machine learning (ML) algorithm indicative of one or more classification standards for the queried asset.
Owner:SCHNEIDER ELECTRIC USA INC
Self-healing generative ai / ML pipeline for generating complex data queries leveraging semantic data model
ActiveUS20260003880A1Digital data information retrievalSpecial data processing applicationsTheoretical computer scienceData access
A method includes providing a user query to an AI / ML pipeline. The user query requests a response based on data stored in a data topology, and the data topology is modeled using a semantic data model. The method also includes generating an initial data access query for retrieving the data from the data topology using the AI / ML pipeline and the semantic data model. The method further includes determining that the initial data access query includes a hallucination or error and performing an automatic loop one or more times. The automatic loop includes generating an updated data access query for retrieving the data; determining whether the updated data access query includes a hallucination or error; and, if so, repeating the automatic loop. In addition, the method includes using a final data access query with no hallucination or error to retrieve the data from the data topology in order to generate the response.
Owner:GOLDMAN SACHS & CO LLC
Systems and methods for using a segmented query model in an analytical application environment
According to embodiments, described herein are systems and methods for providing extensibility in an analytics application environment, including enabling the use of custom semantic extensions to extend a semantic layer of a semantic data model (semantic model). According to embodiments, the system enables the use of a segmented query model - when customizing a semantic model, the system is able to dynamically incorporate changes from various increments at query time at runtime to dynamically surface appropriate data based on the extended semantic model.
Owner:ORACLE INT CORP
Self-healing generative AI / ML pipeline for generating complex data queries leveraging semantic data model
ActiveUS12619620B2Structured data retrievalSpecial data processing applicationsTheoretical computer scienceData access
A method includes providing a user query to an AI / ML pipeline. The user query requests a response based on data stored in a data topology, and the data topology is modeled using a semantic data model. The method also includes generating an initial data access query for retrieving the data from the data topology using the AI / ML pipeline and the semantic data model. The method further includes determining that the initial data access query includes a hallucination or error and performing an automatic loop one or more times. The automatic loop includes generating an updated data access query for retrieving the data; determining whether the updated data access query includes a hallucination or error; and, if so, repeating the automatic loop. In addition, the method includes using a final data access query with no hallucination or error to retrieve the data from the data topology in order to generate the response.
Owner:GOLDMAN SACHS & CO LLC
Data fusion method and device for multiple data sources and medium
PendingCN122064847ADatabase management systemsDatabase distribution/replicationEngineeringData query
The embodiment of the invention discloses a multi-data-source data fusion method and device and a medium, and relates to the technical field of data fusion, the method comprises the following steps: receiving a triggered data query request to determine a required target source data entity and associated fusion calculation logic; according to the target source data entity, screening a standardized change event associated with the target source data entity from a persistently stored standardized change event stream, so as to map the changed data in the standardized change event into a standardized data record based on a pre-constructed unified semantic data model and a mapping rule base; and performing data quality verification and cleaning on the standardized data record, determining a target data record, and determining a fusion data result corresponding to the data query request according to the fusion calculation logic.
Owner:天元大数据信用管理有限公司
Resource management for modular devices
ActiveCN114547836BProgramme controlDesign optimisation/simulationResource Management SystemData description
Embodiments of the present disclosure relate to resource management for modular devices. There is a need for more efficient resource planning for modular devices. Therefore, a resource management system for modular devices is provided. The system comprises a database providing a library of semantic modules representing respective modules in a pool of modules, at least one of the semantic modules comprising a semantic description of the respective module, wherein the semantic description comprises abstract data conforming to a semantic data model, and wherein the abstract data describes properties of the respective module not found in a standard description file for the module. Based thereon, the system facilitates the automatic generation and optimization of a module pipeline.
Owner:ABB (SCHWEIZ) AG
Computer system and method for classifying assets in system
One or more assets in an automation and industrial control system (AIC) are classified according to classification criteria. In a computer monitoring tool, a classification query for assets managed by an AIC is received. In response to the classification query, the computer monitoring tool retrieves a list of candidate ontology classes for the queried asset using information received from the semantic data model of the known asset. The computer monitor tool then preferably captures certain classification attribute variables associated with the queried asset from a database coupled to the AIC. In addition, the computer monitor tool receives user information describing certain building information associated with the queried asset. The computer monitor tool then generates a computer query configured to request a result from a machine learning (ML) algorithm indicative of one or more classification criteria of the query asset.
Owner:SCHNEIDER ELECTRIC USA INC
A smart data analysis method and system integrating Chat BI and Headless BI
PendingCN122132495ADatabase management systemsVisual data miningDatasheetTheoretical computer science
This invention discloses an intelligent data analysis method and system integrating Chat BI and Headless BI; belonging to the field of artificial intelligence and data analysis technology, the operation steps are as follows: Building a unified semantic data model based on the semantic layer of Headless BI, constructing a knowledge graph of data table associations; receiving natural language query requests and completing intent parsing through a large model; calling the semantic layer of Headless BI to generate standardized semantic SQL; after verifying and injecting permissions into the semantic SQL, converting it into physical SQL and executing it; displaying the query results and generating intelligent analysis summaries through the Chat BI multimodal module. The system includes a large model parsing module, a semantic layer processing module, an SQL conversion and verification module, a multimodal display module, a Chat BI interaction module, and a data storage module. This invention achieves deep integration of Chat BI and Headless BI, balancing convenient interaction with professional analysis, improving SQL generation accuracy, reducing the risk of large model illusion, efficiently supporting complex enterprise-level business queries, and adapting to multi-source heterogeneous databases.
Owner:NARI INFORMATION & COMM TECH
Query processing method and device, computer equipment and storage medium
PendingCN121958330ADigital data information retrievalSpecial data processing applicationsEngineeringQuery statement
The invention belongs to the technical field of data processing, and relates to a query processing method which comprises the following steps: receiving an input business query request; performing analysis processing on the business query request to extract index information and dimension information; finding out logic definition information corresponding to the index information from a preset unified index target; based on the logic definition information, dynamically compiling the index information and the dimension information based on a preset semantic data model to obtain a corresponding physical query statement; executing processing on the physical query statement based on a preset database to obtain a corresponding query result; and performing output processing on the query result. The invention further provides a query processing device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the query result can be stored in a block chain. The method can be applied to query processing scenes in the financial science and technology field and the digital medical field, and the processing efficiency, flexibility and accuracy of query processing are effectively improved.
Owner:PING AN TECH (SHENZHEN) CO LTD
Dynamic receiving and processing system for electric detection data
The invention discloses an electric detection data dynamic receiving and processing system, and belongs to the field of electric detection. The system comprises an electric detection original data source, an electric detection operation model and a dynamic semantic data model, wherein the electric detection original data source is used for acquiring electric detection original data from different data sources and classifying the electric detection original data into a plurality of data units; the electric detection operation model is used for converting electric detection original data into structured electric detection data; and the dynamic semantic data model is used for matching the required structured electric detection data according to the vehicle type and converting the structured electric detection data into a standardized XML electric detection file. According to the method, the full-process automatic processing from the original disordered electric detection data to the available XML file is realized.
Owner:ANHUI HAIXINGYUN IOT TECH CO LTD
Intelligent question answering method, device and system based on semantic data model and large model
The application discloses an intelligent question answering method, device and system based on a semantic data model and a large model, and belongs to the field of artificial intelligence. After receiving a current question of a user, a task plan corresponding to the current question of the user is obtained based on a large model, then MQL information extraction is performed based on the task plan to obtain a target MQL, then the target MQL is converted into an SQL query statement by a word semantic data model, query is performed based on the SQL query statement to obtain reply data, and finally the reply data is returned to the user. The technical scheme of the application converts natural language into MQL by using a large model, and then converts the MQL into SQL by using a semantic data model. Compared with a traditional scheme of directly converting natural language into SQL, the technical scheme does not need to train data, and the large model can accurately identify the intention of the user, so that the MQL obtained has a unified semantic layer and provides a standard caliber, and the accuracy of reply is greatly improved.
Owner:BEIJING DIPU TECH CO LTD