100 results about "Graph query" patented technology

Systems and processes for managing graph queries on navigational relationships of database entities are described. A graph index may be used to store a class of navigational relationships to expand query capabilities and improve query efficiency of a traditional relational database. Entity relationships stored in the graph index may be dynamically defined and made accessible to query writers. In addition, users can specify particular entity attributes to be replicated in the graph index from the corresponding entities stored in the traditional relational database. Query performance may be improved by leveraging the graph index data and querying both the database and the graph index. Queries of the underlying database may be narrowed by first searching the graph index. Queries of the graph index may also be narrowed by first searching the underlying database. Historic query data may be stored and used to improve subsequent query execution plans.

In order to facilitate the requesting of desired information from a graph database that stores a graph, a system may translate an initial query into a query that can be executed against the graph database. In particular, using primitives, the system may translate a query associated with a type of database (such as a relational database) into the query. The graph may include nodes, edges between the nodes, and predicates to represent and store data with index-free adjacency. Moreover, the primitives may include: a rule based on edges in the graph that expresses a relational schema in the type of database, and information associated with a compound key that specifies a relationship between nodes, edges and predicates in the graph corresponding to a table in the type of database. Then, the system may execute the query against the graph database, and may receive a result that includes a subset of the graph.

The invention discloses a method and a system for constructing and inquiring a knowledge graph in the field of science and technology, and belongs to the technical field of science and technology management information. According to the method and system for constructing and inquiring a knowledge graph in the field of science and technology, a user sorts data in a relational database, sorts out object, relation and attribute information to be defined, defines the object, relation and attribute information through data modeling, and defines mapping information from a relational database data table to objects, relations and attributes; and the relational database data into is extracted and converted into objects, relations and attribute instances, and the objects, the relations and the attribute instances are stored in a graph database, and graph query analysis is carried out through attribute value matching. According to the method and system for constructing and inquiring a knowledge graph in the field of science and technology, the value exploration and information discovery requirements of the user on the big data gold mine can be better met, and the popularization and application value is very good.

The invention discloses an OLAP query processing method based on array storage and vector processing. In the method, array subscript mapping is carried out on major keys and foreign keys according to referential integrity constraint in a data warehouse model, the major keys of a dimension table and the foreign keys of a fact table are directly mapped to be array subscript addresses, the complex model is converted into a virtual materialized table, and a unified data access view is provided for different queries. The OLAP query is simplified to be three stages of predicate processing, grouping processing and gathering processing. In the stage of predicate processing, selection vectors are used for processing a selection result, on the fact table, of a plurality of predicates many times; in the stage of grouping processing, grouping vectors are used for distributing grouping array subscripts for each fact table record for meeting the predicate condition; in the stage of gathering processing, gathering vectors are used for providing an aggregator based on an array for an aggregate function. The OLAP query processing method based on array storage and vector processing simplifies the OLAP query processing process under a complex mode, eliminates the connection operation between the tables, and improves the code execution efficiency when the OLAP query processing is carried out.

The invention discloses a graph query method facing a relational database. The method is mainly applied in scenarios that common users query a relational database. The method comprises: in an offline processing stage, converting the relational database to graph data, establishing relative index on the graph data; in an online query stage, allowing a data querier to use own vocabulary to establish graph query, then, preliminarily matching nodes of graph query, finally, performing Top-k inaccurate subgraph matching, performing graph matching on the graph query established by the user and graph data obtained by offline conversion. Through the matching, the user can obtain a plurality of subgraphs satisfying query requirement of the user, and the subgraphs are returned to the user as a query result.

The present application relates to a graph database structure based on an external memory, a graph data storage method, a graph data storage device and an electronic device. The graph data storage method comprises the following steps of storing each vertex data in the graph data and the input-side data and the output-side data related to the vertex in a data storage node respectively; specifying different key values corresponds to different data storage nodes, wherein the key values represent unique identifiers used to characterize different data storage nodes, and storing all data storage nodes in a data tree, thereby optimizing the storage efficiency of the graph database and reducing the number of accesses to external storage devices during graph database sub-graph query operations.

The invention relates to the technical field of computers, mainly relates to a natural language processing technology in artificial intelligence, and particularly relates to a data query method and device, an electronic device and a storage medium. The method comprises: performing semantic analysis on the data query statement to obtain a semantic analysis result, and the semantic analysis result at least comprising a query field and a query intention; according to the query field and the query intention, obtaining a graph query statement from a preset graph strategy platform in a matched mode,and the preset graph strategy platform at least storing a graph query statement template associated with the query field and the query intention; querying from a preset knowledge graph according to the obtained graph query statement to obtain a corresponding query result; and returning the query result to the terminal, so that an accurate semantic analysis result is obtained through semantic analysis, the query result is obtained through the graph strategy platform and the knowledge graph, the search dimension is improved, and the query accuracy is improved.

The invention discloses a text data statistical analysis system based on natural language processing. The system comprises a natural language information extraction subsystem, a knowledge graph management subsystem, a data analysis subsystem and a chart visualization subsystem. The natural language information extraction subsystem comprises a corpus preprocessing module and a knowledge extractionmodule. The knowledge graph management subsystem comprises an ontology construction module and a data configuration module; the data analysis subsystem comprises a problem understanding module, a graph query calculation module and a semantic matching module, and the data visualization subsystem is used for generating and visually displaying a data graph. The invention further provides a data statistical analysis method based on natural language processing. According to the method, a knowledge base is established by utilizing the natural language processing technology and the knowledge graph technology, then text data statistical analysis is realized through the intelligent data analysis and visualization technology, and a text document data analysis result is automatically generated into achart to be visually displayed, so enterprise production and operation are guided.

The invention provides a graph search algorithm applied to large-scale irregular structure data. The algorithm includes a data preprocessing method and an inquiry executing method. The data preprocessing method includes the steps of unifying the formats of the irregular structure data, establishing a neighbor label vector table for original image points of all images, and establishing an expansion image with property points. The inquiry executing method includes the steps of screening out candidate matching points corresponding to key points in original image data points through a neighbor label, calculating the matching degree of the candidate matching points and selecting the central point of a local area, dividing the local area on the periphery of the central point, and inquiring about sub-images to be matched with similar images of local images. By means of the algorithm, search accuracy is ensured, calculation complexity is greatly decreased, and feasible and effective image search of large-scale irregular structure data can be achieved.

The invention provides a knowledge-driven business operation graph construction method. The knowledge-driven business operation graph construction method comprises two parts of knowledge generation and domain knowledge graph construction based on multi-source heterogeneous business data and whole-network domain operation graph network construction based on operation mode evolution. Knowledge generation and domain knowledge graph construction based on multi-source heterogeneous business data comprise knowledge extraction and association processing based on multi-mode full-link multi-dimensionalbusiness data, collaborative modeling and graph generation based on multi-source extraction knowledge and knowledge graph expansion based on business domain knowledge autonomous discovery. Accordingto the method, the construction problem of the business operation atlas is converted into the problem of searching related operation knowledge atlas contents from the business data knowledge atlas according to the business operation process and the required data, so that the subgraph query problem of related operation businesses is searched from the huge public business data knowledge atlas.

The invention relates to a semantic query method and device based on measurement knowledge, computer equipment and a storage medium. The method comprises the following steps: acquiring a query question inputted by a user; repeating and transferring the query question to generate a synonymous question set; the synonymous question set comprises a plurality of synonymous questions matched with the semantics of the query question; based on a pre-established question template library, respectively generating target question templates corresponding to all synonymous questions in the synonymous question set, and determining target query statements respectively matched with all the target question templates according to keywords in all the synonymous questions; and based on a pre-established measurement knowledge graph, performing graph query by utilizing the target query statement to obtain a target graph element matched with the target query statement, and taking the target graph element as an answer to the query question. By adopting the method, user semantics can be accurately understood, and mixed retrieval of knowledge and documents is realized.

A method and system are provided. The method includes storing, in a memory, temporal data for a temporal graph. The memory includes a temporal graph storage structure having a set of buckets. The temporal data stored in the buckets includes respective data segments implemented using graph edges. Each of the graph edges has a start time and an end time associated therewith. The method further includes the methods, performed by a processor, of: forming an index that categorizes the graph edges based on the end times of the graph edges; positioning the graph edges within respective ones of the buckets for storage using the index such that the graph edges are positioned in the respective ones of the buckets in a chronological order that is based on the end times; and accessing the temporal graph storage structure using the index responsive to a temporal graph query.

The invention relates to a big data technology, and discloses a knowledge graph construction method. The method comprises the following steps of: extracting entity information and correlations in an original data set, and creating node attributes and node relations according to the entity information and the correlations; analyzing a service data table corresponding to the original data set to obtain a header information set; enabling the header information set to respectively correspond to the node attributes and the node relations through entity links to obtain node attribute values and noderelation values, and summarizing the node attribute values and the node relation values to obtain an analysis value set; and obtaining a standard graph query statement according to the analysis valueset and a pre-constructed graph query statement template, and obtaining a knowledge graph according to the standard graph query statement. In addition, the invention also relates to a blockchain technology, and the original data set can be from nodes of a blockchain. The invention further provides a knowledge graph construction device, electronic equipment and a storage medium. According to the knowledge graph construction method and device, the electronic equipment and the storage medium of the invention, the threshold of knowledge graph construction can be reduced.

The invention provides a graph query method and device and a storage medium, and the method comprises the steps: obtaining a to-be-queried sub-graph; performing querying in an abstract graph corresponding to an original graph according to the to-be-queried sub-graph, wherein the original graph represents a graph containing graph information corresponding to a to-be-queried sub-graph, the originalgraph is obtained by connecting nodes with attribute relations through edges, the edges in the original graph are marked with the attribute relations between the two connected nodes, and the abstractgraph represents a graph obtained after node combination is conducted on the original graph; and according to the to-be-queried sub-graph and the target query sub-graph, performing query in the original graph of the target query sub-graph to obtain a graph query result. By implementing the method and the device, the increasingly large graph scale caused by current development along with the timescan be better adapted, the query efficiency is high, the accuracy of the returned result is high, and the requirement of quickly and efficiently querying the sub-graphs in massive graph data can be met.

The invention relates to a graph data query task processing method and device, computer equipment and a storage medium. The method comprises the following steps of: obtaining a graph data set and a graph data query task set, wherein the graph data set is a set of graph data structures stored in a plurality of machine nodes in a distributed manner; identifying a graph data type of the graph data set; depending on graph data types, calculating the similarity or the difference between every two query tasks in the graph data query task set, dividing the graph data query task set into a plurality of graph data query task subsets based on the similarity or the difference between every two query tasks, and inputting the graph data query task subsets into a preset distributed graph query system toobtain corresponding query results. By adopting the scheme, the problem of load balancing when batch tasks are processed in a distributed system is solved, meanwhile, the problems of low efficiency of serial execution of query tasks and low utilization rate of parallel resources are optimized, the execution efficiency of the query tasks is improved, and the real-time requirement of query is relieved.

The invention relates to an expert domain knowledge graph query method based on natural language questions, and the method comprises the steps: carrying out the word segmentation of an obtained queryquestion through a HanLP Chinese language processing package, obtaining a feature word of the query question, and obtaining a keyword and a part-of-speech; replacing the keywords with the part-of-speech of the corresponding keywords, and performing abstract representation of the query questions; carrying out question classification on the converted abstracted sentences based on a naive Bayes classifier, firstly defining a query question template set, training the question template set by utilizing the naive Bayes classifier, then carrying out probability prediction on the input question sentences, and returning defined question category labels so as to match a question template; restoring the keywords before abstraction by using the matched question template, and returning classification labels and keyword entity names of the question template; and generating a Cypher statement according to the classification label and the keyword entity name to carry out Neo4j query, and returning a result to the user.

The embodiment of the invention discloses a data processing method and device. The method may comprise the steps of querying a first associated user identity associated with the target user identity when a relationship graph query request which is sent by a terminal about the target user identity is received; obtaining target user information corresponding to the target user identity, and obtaining associated user information corresponding to the first associated user identity; determining a first association mode between a target user and a first associated user; generating a data structure about the target user according to the target user information, the associated user information and the first association mode; and generating indication information comprising the data structure to the terminal in response to the relationship graph query request. Through adoption of the embodiment of the invention, the terminal is facilitated to visually display a queried user and other information such the information of the other users related to the queried user.

Systems, methods, and computer readable devices embodying instructions are provided herein for reducing latencies and / or improving computational efficiency when traversing data stored in a relationalgraph by caching subgraphs and enabling the utilization thereof. More specifically, after a user performs a graph query, the resulting subgraphs of the graph query are cached in a reusable form. Subsequent graph queries are able to identify cached subgraphs based on the graph query. Further, the subsequent graph query is operable to integrate the cached subgraphs as part of the result of subsequent graph query, which may include a portion or the entire result of the subsequent graph query being composed from cached subgraphs, thereby improving the computational efficiency and performance of querying relational graphs, reducing the query execution cost required to traverse the relational graphs, and improving the functionality of the computing devices hosting the relational graphs and running the queries.

The invention discloses a graph spectrum and reachable path number-based sub-graph query method for an undirected weighted graph. The method comprises the steps of 1, calculating codes of node marks of a query graph and graphs in a known graph data set, codes of weighted adjacent edge marks and a code of a reachable path number; 2, generating graph spectrums of the query graph and the graphs in the known graph data set; 3, by using the codes of the node marks of the graphs in the known graph data set, the codes of the weighted adjacent edge marks, the code of the reachable path number and thegraph spectrums, constructing an index tree; and 4, comparing the query graph and the node graphs of the index tree layer by layer from top to bottom, and meanwhile, performing screening, until the bottommost layer is screened out, wherein the obtained graphs are candidate graphs similar to the query graph. According to the method, the topological information of the graph is better described. Meanwhile, the features are coded, so that the storage is easy, the operation is simple, the comparison between the features can be accelerated, and the query speed of the whole sub-graph can be increased.

The invention provides a graph query expenditure method for complete historic records. The method comprises the following steps: Step 1, during loading of original data, servers collect data required for expenditure predictions and carry out statistics of the data; Step 2, each server receives a query request from a client and analyzes query statements in the query request; Step 3, triple query orders are selected, and whether the query orders are reasonable or not is judged; and Step 4, query expenditures are predicted. The method provided by the invention is used for graph query expenditure predictions for the complete historic records, and has the advantages that characteristics of a complete historic record query mode is fully utilized, the query expenditures are accurately estimated, the efficient query orders are selected, and characteristics of a high-performance network are fully utilized; effects of communication of the high-performance network in a distributed environment on query delays are further considered; and a delay of a specific query request can be predicted according to a model, and the triple query order with the lowest expenditures is selected, so that the overall performance of a graph query system is improved.

The invention relates to a graph query method and device, electronic equipment and a computer readable storage medium, and belongs to the technical field of graph query, the method comprises the stepsof performing graph embedding mapping on a first graph to a vector space to obtain a first vector space; performing graph embedding mapping on the second graph to the vector space to obtain a secondvector space; calculating and clustering the second vector space to obtain a node clustering cluster of the second graph; based on the first vector space and the node clustering cluster, calculating acandidate node set similar to the node of the second graph in the nodes of the first graph; calculating the maximum connected subgraph of each node in the candidate node set to obtain a plurality ofconnected subgraphs; and obtaining graphs with similar topological structures of the second graph in the first graph from the plurality of connected sub-graphs, and taking the graphs as query resultsof the second graph in the first graph. According to the embodiment of the invention, the graph query efficiency is effectively improved, and the generalization ability of the query result is improved.

The invention relates to a distributed real-time graph construction and query method and a system based on Spark and Ignite. The method comprises the steps of starting Spark real-time graph construction and graph query tasks, loading fields of points and edges of a to-be-queried graph into an Ignite data set from a database, caching the fields, and creating indexes for the fields; generating a corresponding request parameter theme from the request parameter in the request initiated by the front end by using an encryption algorithm, and monitoring the request parameter theme; putting the request parameter theme into the request parameter, sending the request parameter theme to a request theme, and monitoring the request theme; obtaining data for calculation, and sending a calculation resultto the request parameter theme; and returning the obtained result data to the front end. By means of the distributed graph construction and storage functions of the Spark and the Ignite, graph construction and graph query can be conducted on hundred million-level or even ten billion-level data in real time, and the graph query time can be effectively shortened.

A graph processing system for concurrent property graph queries of a property graph implemented in a distributed network computes on respective nodes a subgraph shard represented as edge-sets containing vertices within a certain range. Each node stores data for a subgraph shard that contains a range of local vertices that are a subset of all vertices of the property graph. Each subgraph shard alsohas boundary vertices having edges that connect the subgraph shard to boundary vertices of another subgraph shard. Upon receipt of concurrent queries of the property graph, a query of the subgraph shards is scheduled in accordance with an initial vertex for each concurrent user query. The property graph is traversed by traversing edge-sets within a subgraph shard on each node and during traversalmessaging is used to send values of boundary vertices to at least one other node having another subgraph shard sharing the boundary vertices.

The invention provides a financial knowledge query service interface design and implementation method based on a knowledge graph. The method comprises the steps of querying the financial entities andthe related attributes from the autonomously constructed financial knowledge graph; according to the knowledge of the financial field, defining the financial significance of three query interfaces, wherein the three query interfaces include the stock right association query, the multi-layer stock right query and the annular stock holding query; providing the algorithms of the three query interfaces; and realizing the three query interfaces by utilizing a knowledge graph related technology and a related graph query algorithm. According to the invention, the financial field and the knowledge graph are tightly combined, the complex financial relations are expressed in a knowledge graph form, and the advantages of the respective fields are exerted; the method is suitable for the query servicesof various financial fields and provides the sufficient knowledge association for the implementation of intelligent applications, the field knowledge is organized by taking the intelligent application requirements as cores, and the accurate and efficient field query services are provided.

Disclosed herein are systems, methods, and software for redirecting relational data queries to previously generated graphs. In one example, a method of operating a graph redirect system includes identifying a relational data query and identifying whether at least one previously generated graph in a group of one or more previously generated graphs relates to the relational data query based on similarity data. The method further provides, in response to identifying that at least one previously generated graph relates to the relational data query, directing the relational data query to the at least one previously generated graph, and responding to the relational data query using the at least one previously generated graph.

Reductions in latencies and improvements in computational efficiency when analyzing data stored in a relational graph by integrating analytical capabilities into graph queries. Instead of a user having to run a graph query and then perform analytics on the resulting subgraph via separate requests, the user is enabled to run analytics at the time the graph query is run via a single request to the database maintaining the relationship graph, which improves the computationally efficiency of analyzing relational graphs and thereby improves the functionality of the computing devices hosting the relational graphs and running the queries and analytics.

The embodiment of the invention relates to the technical field of artificial intelligence, and particularly discloses a knowledge graph-based question and answer processing method, device and equipment, and the method comprises the steps: receiving question information which is sent by terminal equipment and is represented by a natural language; generating a graph query statement of at least one execution unit which is executed in sequence and corresponds to the question information; accessing a pre-constructed knowledge graph based on the graph query statement to obtain an execution result of the execution unit, and storing the execution result to enable the graph query statement of the next execution unit to access the knowledge graph based on the execution result; and determining the answer information corresponding to the question information based on the execution result of the last execution unit, so that the answer information is fed back to the terminal equipment. And the answer information generation accuracy can be greatly improved.