321 results about "Database querying" patented technology

The invention provides a method for generating an SQL (Structured Query Language) by a natural language based on bidirectional mapping and semantic parsing, which relates to the technical field of database query and comprises the following steps of: extracting natural language query elements and packaging the natural language query elements into structured data, and establishing a mapping relationship from a query field to a service attribute and a physical data table by adopting a bidirectional Hash index technology; and automatically identifying multi-table association keys, performing semantic extension and compliance verification, generating an abstract syntax tree, performing processing according to user permission, and finally converting the abstract syntax tree into an SQL statement conforming to a target database syntax specification. According to the method, the accuracy and the efficiency of converting the natural language into the SQL are improved, and the flexibility and the safety of the system are enhanced.

According to the artificial intelligence-based data analysis system and method provided by the invention, a set of novel data analysis system architecture is designed, and key technical components such as natural language processing, a large language model, database query optimization, multi-modal visualization and a semantic knowledge graph are fused; data transmission between modules is realized through unified intermediate data objects such as a structured query intention, an analytic tree and a structured query language template object, and asynchronous collaboration is realized through event driving and a message queue mechanism, so that a non-professional user can input an analysis request through a natural language; semantic analysis, structured query language query statement generation, data query and result visualization presentation are automatically completed, the method can be widely applied to data analysis scenes in the industries of government affairs, traffic, finance, education and the like, the data use efficiency is improved, the technical threshold is reduced, and the digital decision-making ability is enhanced.

The invention provides a data query method and system for converting a natural language into a database query language, and the method comprises the steps: analyzing the natural language input of a user through a multi-modal understanding agent on the basis of constructing a dynamic knowledge graph based on metadata, combining a historical session with a business term table, eliminating ambiguity, and generating a standardized Query, a retrieval routing agent selects a query strategy according to Query complexity, simple query directly matches a cache template, complex query traverses a knowledge graph, and related tables, fields and service constraints are returned; then, an expert committee agent generates an SQL (Structured Query Language) by adopting multi-stage collaboration, executes plan pre-evaluation, and selects a version with the highest comprehensive score; the test agent simulates and executes the SQL in the isolation environment, and verifies the grammar legality and the field permission; and finally, executing the detected SQL, and processing a result. According to the method, the accuracy of converting the natural language into the SQL (NL2SQL) in a complex database scene can be effectively improved.

The invention belongs to the technical field of equipment knowledge engineering and natural language processing, and discloses an LLM-based constraint and self-repairing uniformization equipment knowledge graph automatic construction method. The method comprises the following steps: firstly, acquiring equipment related document data through network collection, document arrangement and database query; then, utilizing an equipment domain ontology and constraints as preposed soft and hard constraints, driving LLM to generate semantic intermediate representation, and obtaining candidate triples through structured compiling; then, a self-repairing closed loop is formed through semantic unit testing, logic consistency detection and evidence binding verification, and triples which violate constraints and have conflicts or illusions are automatically recognized and repaired; and finally, entity standard identification, cross-document duplicate removal combination and conflict resolution are realized through cross-segment unification and incremental alignment. According to the method, the fragile path that the LLM directly generates the triple and blindly stores the triple is avoided, the illusion and inconsistency problems are effectively inhibited, and the correctness, interpretability and maintainability of the equipment knowledge graph are remarkably improved.

The present application discloses a database query method and apparatus, an electronic device, and a non-volatile storage medium. The method comprises: determining a knowledge graph corresponding to a database to be queried, wherein the knowledge graph is used for representing a logical structure and an association relationship of data in said database; determining similarity scores between user question text and graph nodes in the knowledge graph, and determining a target node from among the graph nodes of the knowledge graph on the basis of the similarity scores, wherein the similarity scores are used for representing the degree of association between the graph nodes and the user question text; and on the basis of the target node, generating database schema information corresponding to said database, and using a large language model to generate, on the basis of the database schema information, a structured query language statement corresponding to the user question text.

A method for querying a graph database using natural language queries comprises: receiving a natural language user query; identifying one or more node types from a type graph in the natural language user query, wherein the one or more node types correspond to one or more words or phrases in the natural language user query; generating, using a large language model, a graph database query based on the one or more node types identified in the natural language user query; querying a graph database using the graph database query generated by the large language model; receiving results of the graph database query; and generating, using the large language model, a natural language response to the natural language user query based on the results of the graph database query.

The invention discloses an intelligent SQL (Structured Query Language) generation method and system based on multistage intention recognition. The method comprises the following steps: receiving a natural language query of a user; vector matching: carrying out vector matching based on a knowledge base to obtain Top-K candidate query objects, the knowledge base containing multilayer structure description information; a large language model intention understanding step: determining a final query object in combination with the candidate query object description; loading a branch workflow, analyzing a field and a table structure, and dynamically configuring a database table and field information; sQL generation: generating SQL statements conforming to grammatical rules, including automatic field addition, time condition conversion and field priority matching operation; and executing the SQL statement and returning a result, and if the result fails, recording a log to prompt correction. By combining a natural language processing technology with a database knowledge base, an efficient, accurate and user-friendly database query solution is provided.

A system and method for generating a database query based on a natural language query is presented. The method includes receiving an unstructured natural language query directed to a security database, wherein the security database includes a representation of a computing environment; selecting a group of database queries from a plurality of preexisting database queries based on a similarity to the unstructured natural language query; generating a context for processing by a language model, the context including the selected group of database queries, an identified technology, and a schema of the computing environment; processing a prompt and the generated context utilizing the language model to generate a second database query; and executing the second database query on the security database.

A system and method for querying, processing, and ranking governmental data across heterogeneous databases may include integrating large language models (LLMs) and multi-agent orchestration to enhance accuracy and mitigate bias. The system receives a natural language query, processes the query using an agent orchestration LLM to generate search query instructions, and executes specialized data record processing agents to retrieve data from diverse sources, including relational, vector, and graph databases. A plurality of candidate natural language responses is generated and validated by data verification LLMs based on relevance and accuracy metrics to reduce hallucinations. The system further employs cross-source validation, statistical and linguistic consistency analyses, and weighted scoring to refine responses, which may include textual summaries, tables, or downloadable files presented via a graphical user interface. Accordingly, the approach ensures precise and contextually relevant results, particularly for applications involving sensitive or regulated data such as legislative and governmental records.

A database query processing method includes receiving a natural language request for information contained within a database from a user in an application session, prompting a large language model to generate a SQL request, and receiving a particular SQL request from the large language model that is parsed to identify a command to access one or more database structures. A security predicate is appended to the command, creating a modified SQL request, to enforce one or more database access constraints constraining a user-authenticated client device that submitted the request that is not enforced in a database session between the application and a database. The modified SQL request is used to access data in the database session, and a visualization of the accessed data is caused to be displayed in the application session.

The invention discloses a Text-to-SQL (Structured Query Language) generation method and device, a medium and a product, and relates to the field of geographic information technology application, the method comprises the following steps: analyzing a natural language query text input by a user to obtain a source field set and a target field set; determining corresponding nodes of each field in the source field set and the target field set in the river-lake long-system database mode knowledge graph to obtain a source node set and a target node set; the nodes in the source node set serve as starting points, the nodes in the target node set serve as terminal points, the knowledge graph is processed through the shortest path algorithm, and a path set is obtained; decomposing the natural language query text into a plurality of sub-problems by adopting a large language model and a thinking chain technology, and obtaining a dependency relationship among the sub-problems; according to the dependency relationship and the target field set, a public table expression is obtained, and then the SQL statement is obtained, and the accuracy of database query language generation under the river and lake long-term system scene can be improved.

The invention provides a natural language intelligent analysis and data query instruction generation method based on a large model, which relates to the technical field of natural language processing and comprises the steps of performing semantic analysis by pre-training a large-scale language model, constructing a dynamically enhanced query semantic graph, performing semantic enhancement in combination with an external knowledge graph, and generating a data query instruction. And extracting a structured feature vector and mapping the structured feature vector into a database query syntax tree, and finally optimizing an execution plan based on deep reinforcement learning to generate a standardized query instruction. According to the method, intelligent conversion from natural language query to efficient database instructions is realized, and query precision and execution efficiency are improved.

The present disclosure provides an artificial intelligence-based alarm management based on structured time series data and unstructured contextual information where the conventional methods fails to do. Initially, the system receives a user query associated with an industry automation system and classifies into one of a) data request query b) an anomaly detection and diagnosis query c) a log request query and d) feedback for previous response using a query classifier. Simultaneously, the user query is converted into an associated timeseries query representation. Furthermore, a plurality of database queries is generated based on the timeseries query representation using query generator. Further, data pertaining to the user query is retrieved from an associated database based on database queries using Deep Learning models, thereby identifying a critical alarm, and to take appropriate actions if there is any anomaly. Finally, the retrieved data is displayed to the user in a user readable format.

A multimodal content management system having a block-based data structure can include a query engine configured to perform automatic data discovery for natural language queries. The system can generate and render, at a computing device, a page comprising a graphical user interface (GUI) with a displayable item from a first block of a block-based data structure. The system can generate and bind, to the page, a schema definition comprising a first reference to the first block and a second reference to a set of blocks, wherein the first block is relationally linked to the set of blocks via the second reference. The system can use at least a portion of a natural language prompt, received at the GUI, to generate an input feature for a large language model, the input feature having a schema-question pair that includes at least a portion of the schema definition. The large language model can generate a query configured to operate on the block-based data structure.

Generating a natural language context from a ticket management system includes receiving an unstructured natural language query from a client device; generating a first prompt for a first large language model (LLM) based on: a predefined template and the unstructured natural language query, the first prompt when processed by the first LLM outputs a structured database query; executing the structured database query on a database, the database including a representation of a cloud computing environment; generating a second prompt for a second LLM based on a result of executing the structured database query, the second prompt when processed by the second LLM outputs a natural language response; and sending the natural language response to the client device.

The invention discloses a database query statement generation method and device, equipment, a medium and a product. The method comprises the following steps: acquiring a user input statement and a knowledge graph; performing information extraction on the user input statement to obtain key information; screening the knowledge graph based on the key information to obtain a candidate entity set and a mode sub-graph; the prompt information, the user input statement, the mode subgraph and the candidate entity set are input into a large language model, and a plurality of candidate database query statements are obtained; and according to the mode subgraph and the execution result corresponding to each candidate database query statement, screening the plurality of candidate database query statements to obtain a target database query statement, through the technical scheme of the invention, the database query statement meeting semantic requirements can be automatically generated.

The invention relates to the technical field of natural languages, in particular to a method and system for improving SQL generation accuracy of an LLM large model based on the CoT concept. The method comprises the following steps: firstly, preprocessing database metadata, and extracting a table structure and field information; the method comprises the steps that firstly, a complex natural language query task is divided into a plurality of sub-problems with definite semantics on the basis of the Chain-of-Though concept, SQL sub-query statements capable of being independently executed are sequentially generated in a multi-pass SQL mode, and input of each statement is based on an output result or original data summary of the previous statement. A verification mechanism is introduced in the generation process, the grammar and logic validity of the SQL statement is checked, and if a grammar error or no problems of fields and table names are found, the system automatically recognizes and prompts the error, so that the executable performance of the SQL is guaranteed. According to the method, the accuracy and the stability of the large model in the relational database query task are effectively improved, the illusion phenomenon is reduced, and the practicability and the reliability of an intelligent agent system are enhanced.

The invention provides an RAG retrieval enhanced SQL generation method based on a multi-dimensional knowledge base, and the method comprises the steps: respectively constructing a technical metadata knowledge base, a business knowledge base and an experience knowledge base based on a target database; respectively slicing knowledge contents of the technical metadata knowledge base, the business knowledge base and the experience knowledge base; receiving a query request of a user, and simultaneously performing parallel retrieval from the technical metadata knowledge base, the business knowledge base and the experience knowledge base to obtain a retrieval result; a large language model is called based on the retrieval result, SQL is output in a staged SQL generation mode, and the large language model adopts a multi-model collaborative architecture and adopts a gradient lifting algorithm for iterative optimization. Through the organic combination of the technical metadata knowledge base, the business knowledge knowledge base and the experience knowledge knowledge base, a comprehensive database query knowledge system is constructed, and the problem that knowledge coverage is not comprehensive in a traditional method is effectively solved.

Described herein is an apparatus and method for generating a medical database query. An apparatus may include at least a processor; and a memory communicatively connected to the at least processor, wherein the memory contains instructions configuring the at least processor to receive a first natural language database query; input the first natural language database query into a large language model (LLM); receive from the LLM a feature set; using a medical database query map, generate a first medical database query as a function of the feature set; and generate, using the LLM, an aggregated output by querying a medical database interfaced with the LLM using the first medical database query.

The invention is suitable for the technical field of data processing, and provides a database query method, system and device based on a natural language and a medium. Based on the natural language text and the field library, calling a root matching strategy, a semantic matching strategy and a fuzzy matching strategy through a hierarchical collaboration mechanism to perform field mapping, and determining a target field; splicing the target fields to obtain an initial semantic query statement; loading and executing the plurality of modifiers in sequence through a service discovery mechanism to obtain a target semantic query statement; converting the target semantic query statement to obtain a target physical query statement; and executing the target physical query statement to obtain a target query result and returning the target query result to the query party. Through hierarchical cooperation of the root matching strategy, the semantic matching strategy and the fuzzy matching strategy, the expandability and adaptability of the matching strategy are improved, the field recognition accuracy is improved, the field matching range is expanded, and the matching efficiency is optimized.

The embodiment of the invention provides a data analysis method and system based on retrieval enhancement, and the method can obtain query information in a natural language text form, and retrieves enhancement information in a current domain knowledge base according to the query information, the enhanced information comprises one or more combinations of data definition language annotations, query engine grammar documents, business statement cases and custom domain indexes. And inputting the query information and the enhanced information into a statement generation model, and generating a query statement based on a retrieval result of the statement generation model. And executing the query statement through the database query engine, and obtaining the target data from the database. According to the method, semantic mapping of table names or fields can be established by constructing data definition language statements with annotations and query statement question and answer pairs, and the efficiency and accuracy of natural language query are improved.

A contextual natural language query response system (contextual system) leverages contextual attribute-identifier mappings to improve large language model (LLM) responses to natural language queries. The contextual system replaces identifiers in natural language queries with attributes according to a contextual mapping table between identifiers and attributes to generate attribute-based natural language queries. The contextual system then uses retrieval-augmented generation with the attributes-based natural language queries to prompt an LLM to generate attribute-based database queries. The contextual system uses the mappings from the contextual mapping table to convert the attribute-based database queries to identifier-based database queries and queries a database with the identifier-based database queries. The contextual system responses to the natural language queries using results from querying the database.

A database query processing method includes receiving a natural language request for information contained within a database, categorizing the natural language request in a particular category from a plurality of categories, and selecting a large language model and one or more database structures for which a SQL request is to be directed based in part on the particular category. The selected large language model is prompted to generate a SQL request directed to the selected set of one ore more database structures and the received SQL request is validated for the particular category. The validated particular SQL request is used to access data within the database and one or more visualizations is caused to be displayed based at least in part on a result of the validated particular SQL request.

The invention belongs to the technical field of database query and natural language processing, and provides an NL2SQL method based on foreign key completion and a knowledge graph, and the method comprises the steps: extracting a candidate foreign key relationship from database metadata and data content, and trimming and completing the candidate foreign key relationship through a three-layer funnel verification mechanism, a Schema knowledge graph is constructed; based on the natural language query, determining a seed table set directly related to the query intention; on the basis of the Schema knowledge graph, calculating a connection path between nodes of a seed table, complementing an intermediate table on the path, and expanding the seed table into a sub-graph set containing a complete connection relationship; generating a plurality of candidate SQL statements based on the sub-chart set; and executing the plurality of candidate SQL statements, and voting according to the consistency of execution results to select a final SQL statement.

The invention discloses a natural language structured query generation method and system based on pattern reasoning, and relates to the technical field of natural language processing and database query. The method comprises the steps of mode knowledge graph construction in an offline stage and three-step reasoning generation in an online stage. In the offline stage, through structure analysis, semantic enrichment and data portrait processing, a database mode is converted into a knowledge graph Schema-KG rich in structure and semantic information. The online stage adopts three-step reasoning: firstly, decomposing a natural language of a user into an intention component through LLMParser, and linking the intention component to a graph entity; secondly, reasoning an optimal JOIN path on the Schema-KG based on a graph search algorithm, and generating a mode perception intermediate representation SA-IR; finally, the SA-IR is compiled into an SQL, and pre-verification is executed. According to the method, the accuracy of complex query is remarkably improved through a structured reasoning mechanism, errors during operation are effectively avoided through pre-execution verification, a clear reasoning process is provided through SA-IR intermediate representation, and the interpretability and debugging efficiency of the system are greatly improved.

The invention discloses a database query system and method for converting a natural language into an SQL (Structured Query Language) statement based on a Transform architecture, and the system captures a long-distance dependency relationship and complex semantic information in the natural language based on the Transform architecture, so that the semantic accuracy is improved, the wrong and repeated operation of generating the SQL statement is reduced, and the data acquisition efficiency is improved. On the other hand, the problems of grammar errors, semantic inconsistency and the like are verified, recognized and corrected through the SQL error correction module, database query failures are reduced, the user operation experience is improved, meanwhile, the error correction module can conduct compensation through reverse verification and correction, the dependence of the system on the precision of a single module is reduced, and the overall stability is improved; and for similar related problems, SQL statements can be directly extracted from the mapping table, so that time-consuming processing links such as Transform semantic analysis and SQL generation are omitted, and the waiting time and the computing power consumption of a user are remarkably reduced.

The invention discloses a BIM model data query method and system, a product and a medium. The method comprises the steps that after a natural language query of a user is received, analysis is conducted according to a preset structured semantic dictionary, and a target component and a limiting condition are analyzed; generating a group of detailed candidate query paths for each concept by utilizing a BIM inherent relationship defined in a dictionary, and not omitting any possible search dimension; logically combining the candidate query paths into a comprehensive database query instruction and executing the comprehensive database query instruction; after the target component is retrieved, the found specific path is marked for each component to serve as a source basis, merging, duplicate removal and classification are carried out, and finally a structured data query result with clear classification and a clear source is presented to a user. By implementing the technical scheme provided by the invention, the accuracy of carrying out data query on the building BIM model is improved.

The invention provides a multi-round dialogue database query method and device. The method comprises the steps that a natural language question currently input by a user is acquired, and input information is generated based on the natural language question and dialogue historical records; determining a rewriting reason and a rewriting problem based on the input information; determining a reference pair corresponding to the rewriting problem from a preset vector library; in response to the instruction for chain thinking, determining an SQL reason and an SQL query statement based on the rewriting reason, the rewriting problem, the mode information of the to-be-queried target database and the reference pair; and executing the SQL query statement on the target database to perform data query to obtain a query result. In the mode, the understanding accuracy of multiple rounds of dialogues can be remarkably improved, the generation quality of the complex SQL is greatly improved, the robustness and the self-repairing capability of the system are enhanced, and the interpretability and the user experience of the system are improved.

A computing device (800) configured to: receive (701) a database query (302) in a natural language and database information (303) comprising a schema; receive (702) a preliminary query (307) in structured query language; select (703) schema elements; select (704) examples each comprising a respective pair of natural language and structured query language queries, in dependence on respective structural similarities between the preliminary query and the structured query language query of a respective pair represented as normalized abstract syntax trees; form (705) a prompt (312) based on the selected schema elements and examples; and input (706) the prompt to a large language model (313) to form an output query (314) in structured query language. This may allow output queries to be formed using examples in the prompt that have semantic similarity with respect to the input query and the expected SQL, with the ability to support new domains with limited data.

The invention discloses a dynamic SQL (Structured Query Language) optimization method based on consanguinity dependency, and relates to the technical field of database query optimization. The method comprises the following steps: analyzing all indexes in a database, and dividing the indexes into atomic indexes and composite indexes; a blood relationship dependency tree is constructed according to the mathematical relationship between the indexes, each node represents one index, and each directed edge represents the dependency relationship; receiving a to-be-queried index set, and recursively searching all dependent target atomic indexes based on the graph structure; grouping the target atomic indexes according to data sources, and generating corresponding SQL query statements; and executing the SQL statement, and calculating a final query index result after obtaining an atomic index value. According to the invention, through unified management of the index dependence structure, repeated calculation across indexes is eliminated, the query performance is improved, the resource consumption is reduced, and the index development and maintenance efficiency is improved.