276 results about "Graph query" patented technology

Embodiments of the disclosed technologies include parsing a query into a first query portion and at least one second query portion, matching an embedding of the at least one second query portion with an embedding that corresponds to a portion of a document of a document set, mapping the portion of the document to a first node of a graph; by a generative artificial intelligence model, constructing a graph query based on at least the first node, executing the graph query on the graph to identify a second node of the graph, extracting a path from the graph, and configuring the path for output at a device.

The invention provides a Monte Carlo tree search and multi-agent cooperation-based knowledge graph question and answer method, which comprises the following steps that: a plurality of agents are initialized, and each agent has an initial process, a state and a target; a graph query statement is generated through multi-agent cooperation, and a complete track with scores is obtained; and utilizing a Monte Carlo tree search method, optimizing a query track through the steps of selection, expansion, simulation and back propagation according to the query track generated through multi-agent cooperation, generating an executable graph query statement, and finally outputting an answer set of the question. According to the method, the advantages of an end-to-end method and a step-by-step reasoning method are combined, meanwhile, the defects that the knowledge graph environment perception ability is insufficient and the search space is too large are overcome, dynamic generation, evaluation and optimization of the query path are achieved by designing a division and cooperation mechanism of the expansion agent, the simulation agent and the valuing agent, and the search efficiency is improved. Therefore, the accuracy and robustness of the question answering system are improved.

The invention relates to the technical field of intelligent decision making, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a knowledge graph question and answer result generation method, device, equipment and medium, which comprises the steps of performing semantic analysis on a user question to obtain entity information and a query intention, performing logic analysis on the entity information and the query intention to obtain a question and answer result; obtaining an initial execution plan; calling a sub-graph query tool according to the initial execution plan, and querying sub-graph data by using the sub-graph query tool; performing linearization processing on the sub-graph data to obtain a sub-graph data text, extracting a candidate entity set in the sub-graph data text, and screening out a matched target entity from the candidate entity set; performing logic analysis on the target entity and the query intention to obtain a target execution plan, and screening out a matched atomic query tool from the candidate tool set; and querying a knowledge graph question-answer result by using an atomic query tool. The knowledge graph question and answer result generation accuracy can be improved.

A method is provided for caching a query submitted to a graph data storage system, including receiving a query from a query caller directed to the graph data storage system, and examining the query, via a cache suitability determination engine, to determine if the query is suitable for caching under a first graph query caching policy based on the query caller being a trusted caller and / or the query scenario being a trusted query scenario. If suitable, generating a query execution plan for the query via a graph query plan generating engine and examining the query plan to determine if the query plan is suitable for caching under the first policy. If not suitable, caching the query under a second or a third graph caching policy based on the geographic location of a memory system on which the query plan is stored, and providing the query execution plan.

The invention relates to the technical field of distributed agent collaboration and graph database query optimization, in particular to a graph query processing method and system based on multi-agent collaboration, and the method comprises the following steps: reasoning a coordination agent to analyze the semantics and intention of user query by using a large language model; a structured multi-agent execution plan is output, and an entity search agent or a relation analysis agent needing to be called is explicitly called; the agent coordinator receives the multi-agent execution plan and selects agents according to task types and task allocation; obtaining a distributed intermediate result set; starting a result aggregation mechanism, and performing typed processing, priority ranking and deduplication merging on the distributed intermediate result set to obtain an arrangement result; and outputting an answer for answering the initial question of the user. The system comprises an agent execution plan output module, a multi-task execution module and an arrangement result reasoning module. According to the method, efficient, extensible and fault-tolerant graph query processing is realized.

Embodiments of the disclosed technologies include parsing a query into a first query portion and at least one second query portion, matching an embedding of the at least one second query portion with an embedding that corresponds to a portion of a document of a document set, mapping the portion of the document to a first node of a graph; by a generative artificial intelligence model, constructing a graph query based on at least the first node, executing the graph query on the graph to identify a second node of the graph, extracting a path from the graph, and configuring the path for output at a device.

The invention discloses a new energy equipment troubleshooting method based on a knowledge graph and a large model. The method is used for solving the problems of fault positioning and troubleshooting of photovoltaic and wind power new energy equipment and the like. The method mainly comprises the following steps: performing data cleaning and preprocessing on a new energy equipment troubleshooting professional field document, and constructing a structured knowledge graph of equipment types, fault types, fault information and solution nodes in a graph database through Cypher; the big language model analyzes the user fault description, extracts and maps node attributes in the knowledge graph, and generates a graph query statement; the question-tracing type multi-round interaction is realized, the missing information is dynamically complemented, and the basic fault is accurately positioned in a step-by-step reasoning mode; according to the method, multi-modal resources such as texts, schematic diagrams and videos stored in the knowledge graph are combined, a step-by-step reasoning path and a solution with illustrative features are generated through a preset template, the whole reasoning process is visualized in a branch thinking graph form, and the accuracy, interpretability and user experience of troubleshooting are remarkably improved.

Embodiments of the disclosed technologies include parsing a query into a first query portion and at least one second query portion, matching an embedding of the at least one second query portion with an embedding that corresponds to a portion of a document of a document set, mapping the portion of the document to a first node of a graph; by a generative artificial intelligence model, constructing a graph query based on at least the first node, executing the graph query on the graph to identify a second node of the graph, extracting a path from the graph, and configuring the path for output at a device.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a root cause analysis method and device based on a space-time dependency graph, equipment and a medium. Comprising the steps of constructing a space-time dependency graph, generating a diagnosis path blueprint, identifying a fault source and a root cause entity type, generating a graph query statement, executing query and performing cause and effect verification, and outputting a root cause analysis report. And the causal reasoning and root cause positioning of the system state change are realized by fusing the graph structure information and the natural language processing capability. Through cooperative processing of a language model and a graph data structure, fault symptom information and system structured state data are deeply fused, a path is generated in the graph structure, and a causal relationship is verified, so that the ability of understanding a complex system state evolution chain is improved, and accurate identification and diagnosis of root causes are realized. And the accuracy and the automation level of root cause analysis are obviously enhanced.

The invention provides a logical reasoning-driven knowledge graph enhanced large model generation method, and belongs to the technical field of knowledge graph enhanced large model generation, and the method comprises the steps: 1, preparing a data set; step 2, extracting and screening a triple; step 3, triple conversion and knowledge graph construction; 4, obtaining an original query question; 5, extracting user question keywords; step 6, graph query engine construction and query execution; and step 7, integrating and generating knowledge. Compared with a traditional manual construction method, the method has the advantages that the manual construction cost is remarkably reduced, and the construction efficiency of the knowledge graph is also improved.

Disclosed in the present invention is a graph computing platform based on cloud-native technology. The platform comprises a software and hardware collaborative system, a graph storage system, a graph computing engine and a graph development workshop system, which are designed by using a cloud-native architecture, wherein the software and hardware collaborative system, the graph storage system and the graph computing engine run in the backend, and a graph development workshop runs in the frontend. The software and hardware collaborative system provides hardware computing resources and a software and hardware adaptation environment; the graph storage system comprises a graph division module, a distributed persistent storage module and a distributed shared memory pool module; the graph computing engine comprises a graph query engine, a graph analysis engine and a graph learning engine; and the graph development workshop system is a visual operating system which is constructed on the basis of the graph computing engine and is for the field of graph computing, and the graph development workshop system is deployed on the basis of K8s containerization technology, and is used for modularizing, streamlining and visualizing the whole development process of a graph computing algorithm. The platform in the present invention has clear architecture, strong expandability, is easy to learn to use, and has high computing efficiency.

The invention belongs to the technical field of computer-aided project management, and discloses an electric power engineering design project management system and method, and the system comprises a data integration and processing module, a multilayer dynamic knowledge graph data storage library, a graph-based automatic compliance check engine, and a graph neural network-based predictive risk engine. And an active design change impact analysis module. According to the method, a unified and intelligent project data core is constructed, heterogeneous data sources such as design files and law and regulation texts are integrated in a multi-layer dynamic knowledge graph, automatic compliance review is realized by utilizing graph query, data-driven risk prediction is realized by utilizing a graph neural network GNN, and the risk prediction efficiency is improved. And the linkage influence of design change can be simulated and quantitatively evaluated. According to the method, the problems of data islanding, passive compliance examination, subjective risk management, difficulty in change analysis and the like in the prior art are solved, and the management efficiency, accuracy and initiative of the electric power engineering design project can be remarkably improved.

The invention provides a knowledge graph enhanced retrieval application system and method. The expansion query module is arranged to expand potential query of attributes of nodes and relationships to be queried, and then query of the nodes and the relationships is carried out through the knowledge graph; lLM-agent interactive auxiliary query is carried out, after nodes and relation attributes corresponding to queried entities are obtained, interaction with the knowledge graph is carried out in an interactive mode, and graph query is completed; according to the method, knowledge graph query content and historical information are transmitted to the large model, one-time question and answer is completed, and compared with an existing scheme, semantic consistency in the data fusion process of knowledge graph construction and retrieval is guaranteed, so that the accuracy of the constructed knowledge graph is improved, and the user experience is improved. Therefore, the problem that the accuracy of the constructed knowledge graph is poor due to the fact that the reason of semantic inconsistency possibly exists in the data fusion process of knowledge graph construction and retrieval in an existing scheme is solved.

The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating function code and test code to determine that the function code satisfies an intent query. For example, the disclosed systems utilize a large language model to process an intent query and generate function code that defines one or more processes whose execution satisfies the intent query. In addition, the disclosed systems also utilize the large language model to generate test code that defines a function test for determining that the function code satisfies the intent query. Moreover, the disclosed systems can execute the test code to determine that the function code satisfies the intent query.

Described are examples for creating a graph model for data obtained from multiple data sources and stored in a common data format. A selection of columns of data stored in the common data format that are to be associated with one another in the graph model can be obtained. Based on the selection, a graph model can be created including metadata that defines a list of vertices corresponding to at least a portion of the columns and a list of edges between two or more of the vertices identifying a relationship between corresponding columns. A graph query or graph algorithm can be performed over the graph model of the data as stored in the common data format without requiring copying of the data to a separate graph database.

In one aspect, a query management module executing on a processor receives, from a large language model (LLM), a graph query generated by the LLM based on a natural language query (NLQ). A validation module identifies an error in the graph query. The query management module provides an indication of the error to the LLM. The query management module receives a modified graph query from the LLM. The validation module validates the modified graph query. Based on the validation of the modified graph query, the query management module executes the modified graph query against a knowledge graph to return a result as a response to the NLQ.

The embodiment of the invention provides a crop knowledge graph question answering method and system based on a large model and data assimilation, and the method comprises the steps: generating an initial knowledge graph based on a dynamic graph neural network; updating the initial knowledge graph based on a dynamic incremental graph updating algorithm; performing knowledge extraction and understanding on questions of a user based on a large language model LLM, processing an output result of the LLM, and generating a graph structured query template; obtaining Prompt query templates constructed by LLM, fusing the Prompt query templates with the knowledge graph, obtaining optimal information query by applying a reinforcement learning path optimization method, and integrating graph query results obtained by the Prompt query templates; and performing question and answer result generation based on the optimized and adjusted LLM fusion graph query result. According to the method, multi-modal data are fused, a high-precision qa question-answer pair is generated in combination with historical user query questions q to finely adjust the vertical field large model, and the question-answer accuracy is improved by building a multi-layer Prompt strategy.

The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating function code and test code to determine that the function code satisfies an intent query. For example, the disclosed systems utilize a large language model to process an intent query and generate function code that defines one or more processes whose execution satisfies the intent query. In addition, the disclosed systems also utilize the large language model to generate test code that defines a function test for determining that the function code satisfies the intent query. Moreover, the disclosed systems can execute the test code to determine that the function code satisfies the intent query.

The present invention provides a method for evaluating the effects of communication confrontation based on a knowledge graph, which relates to the field of communication confrontation. The present invention first constructs a knowledge graph of the communication network, converts nodes, communication relationships, and interference relationships into a graph structure, and represents the connection and interference between nodes through the edges in the graph. Then, the node connection relationship is obtained through graph query, the impact of interference and communication quality is calculated, and the shortest path between nodes is calculated using a graph algorithm to achieve an evaluation of the communication effect. The present invention aims to provide a more comprehensive and accurate evaluation of the effects of communication confrontation from the perspective of complex communication networking by analyzing the connection relationship and information transmission path between communication nodes. Through this method, the effect of communication confrontation can be evaluated from the global perspective of the communication network, thereby improving the comprehensiveness and accuracy of the evaluation.

Various examples of improving an in-memory graph query engine using a persisted storage component are provided. The method includes updating data stored in an in-memory graph query engine and, based on updating the data, converting the data to a plain text form that may be more efficiently stored in the persistent storage component. The method further includes updates to additional in-memory graph query engines from the persistent storage component such that in-memory data stored in the graph query engines is synchronized.

One or more embodiments of the invention provide a graph database system and a graph query method, nodes and edges in a graph are organized into key-value pairs, and the key-value pairs are stored in an SSTable file; the SSTable file comprises a first data block; a key in the first key-value pair comprises a node identifier, and a value comprises a node attribute; the key in the second key value pair comprises a starting point identifier and an ending point identifier of the edge, and the value comprises an edge attribute; the first key value pair and the second key value pair with the same node identifier and the same edge starting point identifier are aggregated and stored in the first data block; the method comprises the following steps: acquiring a target key for querying a graph; querying in the SSTable file based on a target node identifier in the target key to determine a target first data block for aggregating and storing a first key value pair and a second key value pair containing the target node identifier; and querying in the target first data block based on the target key to determine a target value corresponding to the target key, and determining a graph query result based on the target value.

The invention relates to the technical field of equipment control, and provides an equipment control method and system based on a natural language and electronic equipment. According to the application, the machine language vector is generated through the intention recognition result of the natural language instruction, and the graph query statement is generated according to the semantic concept after the machine language vector is deconstructed, so that the equipment knowledge graph of the original equipment is queried through the graph query statement to obtain the equipment operation path meeting the semantic concept; the knowledge graph has a mesh topology structure, the equipment knowledge graph is used for replacing a rigid relational database structure, deep requirements in natural language instructions are mined through intention recognition, machine language vectors are obtained, the machine language vectors are deconstructed, and the control of the original equipment is achieved. And generating a graph query statement corresponding to the equipment knowledge graph according to the deconstructed semantic concept, thereby accurately and comprehensively querying the complex context semantic relationship between the nodes, and improving the understanding accuracy of the natural language instruction.

The invention provides a network security dynamic early warning method and device and electronic equipment. The method comprises the following steps: constructing a network knowledge graph; defining a query target, including identifying a potential attack and searching a potential attack path; the threat features are mapped into a three-dimensional odor concentration field through a fruit fly optimization algorithm, a graph query algorithm is optimized, and the three-dimensional odor concentration field comprises threat types, emergency degrees and propagation speeds; and on the basis of a query target, searching in the network knowledge graph through the optimized graph query algorithm, searching for potential attacks and attack paths, and triggering early warning in time. According to the method, the threat features are mapped into the three-dimensional odor concentration field through the fruit fly optimization algorithm, and the graph query algorithm is optimized, so that the optimized graph query algorithm can fully consider the node concentration field and preferentially pay attention to and locate nodes with relatively high threat degrees in network knowledge graph search, and the search efficiency is greatly improved; potential attacks and attack paths can be found in the network knowledge graph more accurately and efficiently.

The invention discloses an intelligent query method for a railway construction stage operation instruction, which comprises the following steps of: S1, sorting an operation instruction text, S2, extracting a chapter number, dividing chapter content and establishing chapter association, S3, extracting a summary of the chapter, S4, creating a knowledge graph of the railway construction stage operation instruction, S5, identifying user query semantics, generating a query semantic set, and S5, storing the query semantic set into a database, s6, converting the query semantic set into a knowledge graph query statement; and S7, visually displaying a graph query result, and outputting the query result. According to the method, a large model technology is taken as a core, the natural language semantic understanding capability of a large model and the structured knowledge management capability of a knowledge graph are highly fused for the structured characteristics of the railway construction stage operation instruction, and intelligent query of the railway construction stage operation instruction based on the natural language is realized.

The invention belongs to the technical field of map construction, and discloses an online vectorization map construction method based on dynamic query management. Extracting perspective image features; generating aerial view view angle features based on the perspective image features; initializing map element query; inputting the initialized map element query and the aerial view view angle features into a map decoder based on dynamic query management, and optimizing the map element query; taking the output result of the Lth layer of the map decoder based on the dynamic query management as the final prediction of the attribute of the map element; and carrying out loss calculation and training. By optimizing and dynamically managing map queries in a map decoder, the computational consumption of low confidence map queries is effectively reduced. Under the same computing resources, the model is allowed to process a larger number of map queries, so that the construction precision of the vectorized map is improved. The research of automatic driving on-line map construction is further promoted, and the deployment of an on-line vectorization map model is facilitated.

The embodiment of the invention provides a graph query optimization execution method which is executed through a graph query engine and comprises the steps that a graph query statement is received, and the graph query statement comprises a hop count range of an indefinite hop path and a path constraint on a connecting edge; and executing a target execution plan generated for the graph query statement, including filtering candidate connection edges according to the path constraint when the connection edges of a first path are expanded, and the hop count of the first path belongs to the hop count range after expansion.

The invention discloses a dynamic knowledge graph construction and application method, system and device for energy policy influence conduction analysis and a medium, and the method comprises the steps: carrying out the multi-dimensional knowledge extraction of an energy policy text through employing a first adaptive extraction model, and outputting a structured knowledge triple; constructing a dynamic multi-subject knowledge graph; performing deduction search on the potential association path through a first influence search algorithm, and identifying a key policy influence conduction chain; an event-driven knowledge graph closed-loop self-evolution mechanism is established, policy change events are captured in real time through automatic monitoring, and incremental updating and structure correction of the dynamic multi-subject knowledge graph are triggered; and constructing a scene-oriented personalized application service interface according to the dynamic multi-subject knowledge graph, converting a user natural language query into a graph query instruction, and generating a scene response in combination with a retrieval result. According to the invention, the cost of manual arrangement and maintenance is obviously reduced, and the working efficiency and expandability of the system are improved.

The invention discloses an airport apron operation intelligent question answering system and method based on knowledge graph reasoning, and belongs to the technical field of knowledge graphs and artificial intelligence. The method comprises the following steps: constructing an airport apron operation knowledge graph by fusing multi-modal data sources; analyzing a user query intention by using a large language model, realizing alignment of the oral entity and the map standard entity, and generating a standardized query; decomposing the standardized query into an ordered subtask sequence based on thinking chain reasoning, and generating a tool calling plan; and executing the tool calling plan, obtaining a fact basis by sequentially calling a graph query tool, and generating an interpretable answer in combination with a reasoning path. According to the method, multi-modal source data can be deeply fused, a high-precision domain knowledge graph is automatically constructed, and on the basis, an interactive, reasonable, safe and credible intelligent question and answer system is provided.

In general, this disclosure describes techniques for analyzing anomalies in a network. In an example, a method comprises obtaining, by a system, a graph query and a network graph for a network, wherein the network graph includes one or more nodes having one or more properties that indicate a plurality of anomalies for the network; executing, by the system, the graph query on the network graph for the network to determine a matching subgraph of the network graph, wherein the graph query matches on the one or more nodes and the one or more properties; and based on the determination of the matching subgraph, outputting an indication of an association of the plurality of anomalies.

The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating function code and test code to determine that the function code satisfies an intent query. For example, the disclosed systems utilize a large language model to process an intent query and generate function code that defines one or more processes whose execution satisfies the intent query. In addition, the disclosed systems also utilize the large language model to generate test code that defines a function test for determining that the function code satisfies the intent query. Moreover, the disclosed systems can execute the test code to determine that the function code satisfies the intent query.