CN114090834A

CN114090834A - Graph searching method, device and equipment

Info

Publication number: CN114090834A
Application number: CN202111317516.5A
Authority: CN
Inventors: 任陶瑞
Original assignee: Alipay Hangzhou Information Technology Co Ltd
Current assignee: Alipay Hangzhou Information Technology Co Ltd
Priority date: 2021-11-09
Filing date: 2021-11-09
Publication date: 2022-02-25

Abstract

The embodiment of the specification discloses a graph searching method, a graph searching device and graph searching equipment. The scheme comprises the following steps: acquiring first graph data input by a user; the first graph data comprises point data and edge data; performing decomposition operation on the first graph data to obtain corresponding units; generating a word set corresponding to the first graph data according to the attribute information of the units; based on the set of words, a search operation related to the first graph data is performed.

Description

Graph searching method, device and equipment

Technical Field

The present application relates to the field of internet technologies, and in particular, to a graph search method, device, and apparatus.

Background

With the rapid development of various industries in the society, a huge and complex relationship network is organized in the real society, and the traditional database is difficult to process the relationship operation. The relation between data needing to be processed in the big data industry increases in a geometric progression along with the data volume, and a database supporting massive complex data relational operation is urgently needed. Wherein a graph consists of two elements: nodes and relationships. Each node represents an entity (person, place, thing, category or other data) and each relationship represents the way two nodes are associated. A Graph database (Graph database) does not refer to a database storing pictures, but stores and queries data in a Graph data structure, and is an online database management system having operations of creating, reading, updating, and deleting (CRUD) a Graph data model.

Graph databases can be applied to the social field, the retail field, the financial field, the internet of things field and the like, and are widely applied, so that a more reliable graph search scheme is urgently needed to be provided.

Disclosure of Invention

The embodiment of the specification provides a graph search method, a graph search device and graph search equipment, so as to solve the problem that graph search cannot be performed based on a graph in the prior art.

In order to solve the above technical problem, the embodiments of the present specification are implemented as follows:

an embodiment of the present specification provides a graph search method, including:

acquiring first graph data input by a user; the first graph data comprises point data and edge data;

performing decomposition operation on the first graph data to obtain corresponding graph data units;

generating a word set corresponding to the first graph data according to the attribute information of each graph data unit;

searching for graph data related to the first graph data based on the set of words.

An embodiment of this specification provides a graph search apparatus, including:

the first graph data acquisition module is used for acquiring first graph data input by a user; the first graph data comprises point data and edge data;

the decomposition module is used for executing decomposition operation on the first graph data to obtain a corresponding graph data unit;

the word set generating module is used for generating a word set corresponding to the first graph data according to the attribute information of each graph data unit;

and the graph data searching module is used for searching graph data related to the first graph data based on the word set.

An embodiment of the present specification provides a graph search apparatus, including:

at least one processor; and the number of the first and second groups,

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to:

Embodiments of the present specification provide a computer readable medium having stored thereon computer readable instructions executable by a processor to implement a graph search method.

At least one embodiment of the present description can achieve the following advantageous effects: acquiring first graph data input by a user; the first graph data comprises point data and edge data; performing decomposition operation on the first graph data to obtain corresponding units; generating a word set corresponding to the first graph data according to the attribute information of the units; based on the set of words, a search operation related to the first graph data is performed. By the method, the blank that the conventional graph database cannot support graph searching can be solved, the functions of similar graph searching and subgraph searching can be supported, extra training cost and model storage cost are not needed, and a large amount of server computing resources and time cost are saved.

Drawings

In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.

FIG. 1 is an overall system block diagram of a graph search method provided by an embodiment of the present disclosure;

FIG. 2 is a flowchart illustrating a graph searching method provided by an embodiment of the present disclosure;

FIG. 3 is an exploded exemplary diagram of a graph searching method provided by an embodiment of the present specification;

fig. 4 is a schematic structural diagram of a graph search apparatus provided in an embodiment of the present specification;

fig. 5 is a schematic structural diagram of a graph search apparatus provided in an embodiment of this specification.

Detailed Description

To make the objects, technical solutions and advantages of one or more embodiments of the present disclosure more apparent, the technical solutions of one or more embodiments of the present disclosure will be described in detail and completely with reference to the specific embodiments of the present disclosure and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present specification, and not all embodiments. All other embodiments that can be derived by a person skilled in the art from the embodiments given herein without making any creative effort fall within the protection scope of one or more embodiments of the present disclosure.

The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.

With the increasing number of scenarios of map-based intelligent analysis, it is more and more favored to use maps to solve algorithm and engineering problems. In one application scenario, in medical analysis, drugs with the same or similar functional groups may have similar efficacy. If the relationship graph of the functional groups of the medicaments with known efficacies is disassembled in advance and stored into a graph database, the functional group relationship graph of the new medicament is input by utilizing the graph searching function, and the medicaments with known similar structures and corresponding medicament data in the database are output, so that a medicament developer can be helped to judge the possible efficacies of the new medicament.

In another application scenario, in the field of program analysis, a call graph during program operation may be stored in a graph database. Before a program is online or released, a program case is required to be adopted to test the program to be online, and the program is online only after the test is successful. Because different inputs exist during the running of the program, a plurality of call graphs are generated after the service of the program with the same version is on line. The different call graphs represent the differences in the execution logic within the program, i.e., carry the business attributes of the program. After the code of the program is changed, the changed influence case can be obtained through analysis, so that the change test is accurately carried out, and the time is saved.

In the prior art, the current graph database mainly provides functions of graph storage and graph query. The query here refers to describing the condition of finding the graph through a special language, and the graph database engine helps the user find the corresponding graph or subgraph. For example, in a business relationship lookup, the existing graph database may be used to find the relationship network of people in front of company A and company B. Current graph databases, however, do not address searching for associated graphs with a given graph.

In order to solve the defects in the prior art, the scheme provides the following embodiments:

next, specific description will be made for the embodiments of the specification with reference to the accompanying drawings:

fig. 1 is an overall system block diagram of a graph search method provided in an embodiment of the present specification. As shown in FIG. 1, the system includes a graph database interface 101, a graph query module 103, a graph data add, delete and modify module 105, a graph search module 107, a graph node and edge data storage module 109, and a graph vector and word data storage module 111. The system not only can provide a common graph query function for a user, but also can provide a graph search function for the user, wherein graph query mainly obtains data of a graph or a subgraph according to condition data of nodes and edges; the graph searching is mainly based on graph searching, namely similar graphs or subgraphs are obtained based on graph searching.

The system may receive requests sent by users through the graph database interface 101, such as: if a graph query request, a data addition and deletion modification request or a graph search request is received, the graph query module 103 may interact with the graph node and edge data storage module 109, specifically, the graph query module 103 is mainly responsible for receiving a graph query service, and may interact with the graph node and edge data storage module 109 based on node and/or edge data input by a user, or a graph matching the node and/or edge data input by the user. If the system receives a data adding, deleting and modifying request, the graph data adding, deleting and modifying module 105 can interact with the graph node and edge data storage module 109 or the graph vector and word data storage module 111, and the graph data adding, deleting and modifying module 105 is mainly responsible for the work of adding, deleting and modifying original data. If the system receives a graph search request, the graph search module 107 may interact with the graph vector and word data storage module 111, wherein the graph search module 107 integrates functions of similar graph search, sub-graph search, and the like, and the graph vector and word data storage module 111 may mainly store preprocessing information of a graph, including an encoding vector of the graph and preprocessed word information. Meanwhile, a vector retrieval function and a word reverse index function can be provided.

Next, the scheme provided by the embodiments of the present specification may be described with reference to the following embodiments:

fig. 2 is a schematic flowchart of a graph searching method provided in an embodiment of the present disclosure. From the viewpoint of a program, the execution subject of the flow may be a program installed in an application server or an application client. In this embodiment, the main execution body of the flow may be a server providing a graph search function, the server may also provide a graph query function, a graph storage function, and the like at the same time, and a result graph corresponding to an input graph may be found from a graph database according to a graph input by a user.

As shown in fig. 2, the process may include the following steps:

step 210: acquiring first graph data input by a user; the first graph data includes point data and edge data.

The first graph data may represent data corresponding to a graph input by a user, and the graph may be composed of a finite and non-empty set of vertices and a set of edges between the vertices. In the existing graph query scheme, when a user needs to search for corresponding graph data, the user generally inputs self attribute information of a certain point and/or edge to query, for example, in enterprise relationship search, a relationship network of people in front of a company a and a company B needs to be found, and the query can be completed by using the existing graph database, but the existing graph database cannot meet the requirement of searching for an associated graph by using a given graph. In this step, when the graph search is performed, the data input by the user is graph data corresponding to a graph.

Step 220: and executing decomposition operation on the first graph data to obtain a corresponding unit.

The performing a decomposition operation on the first graph data to obtain a corresponding unit may specifically include:

decomposing the first graph data according to a preset rule to obtain a corresponding unit; one of the cells includes one node data in the first graph data, or one of the cells includes edge data in the first graph data and node data to which the edge is connected.

The decomposition operation may be understood as an operation of decomposing points and edges in the graph according to a preset rule, for example: the node itself can become a unit, the connection relationship between nodes can become a unit, and more than one node and the edge between the node and the node can become a unit.

Step 230: and generating a word set corresponding to the first graph data according to the attribute information of the units.

In step 220, after the first graph data is decomposed to obtain units, all decomposed units in the graph may be traversed, and HASH encoding may be performed to form a word using the attribute information of the units. There may be a one-to-one correspondence between units and words (HASH values). That is, one or more points and edges may be included in a unit, and a word corresponding to the unit may be generated according to the attribute information of the unit.

The attribute information of a unit may include the attribute information of a node itself and the attribute information of an edge itself in the unit, and may also include the attribute information of a node or an edge in a graph, for example: relationship information between certain two or certain points in a unit, etc.

Step 240: searching for graph data related to the first graph data based on the set of words.

Optionally, the searching for graph data related to the first graph data based on the word set may specifically include:

determining target graph data in a graph database that matches the first graph data based on the set of words; the incidence relation between the target graph data and the first graph data meets a preset condition; the association relation meeting the preset condition comprises the following steps: the similarity between the target graph data and the first graph data meets a first preset condition or the inclusion degree between the target graph data and the first graph data meets a second preset condition.

The word set may represent a set of words corresponding to each unit after decomposition in the inputted graph. The search operation may represent an operation of determining target graph data in the graph database that matches the first graph data. Specifically, an operation of determining target map data similar to the first map data may be included, and an operation of determining target map data including the first map data may be included. Further, when target map data similar to the first map data is determined, map data having a similarity greater than a first preset condition may be determined as the target map data of the first map data; when the target map data including the first map data is determined, the map data whose inclusion degree satisfies the second preset condition may be determined as the target map data. The first preset condition and the second preset condition may be set according to actual application requirements, and the embodiment of this specification is not specifically limited.

It should be understood that the order of some steps in the method described in one or more embodiments of the present disclosure may be interchanged according to actual needs, or some steps may be omitted or deleted.

The method of fig. 2, by obtaining first graph data input by a user; the first graph data comprises point data and edge data; performing decomposition operation on the first graph data to obtain corresponding units; generating a word set corresponding to the first graph data according to the attribute information of the units; based on the set of words, a search operation related to the first graph data is performed. By the method, the blank that the conventional graph database cannot support graph searching can be solved, the functions of similar graph searching and subgraph searching can be supported, extra training cost and model storage cost are not needed, and a large amount of server computing resources and time cost are saved.

Based on the method of fig. 2, the present specification also provides some specific embodiments of the method, which are described below.

The generating a word set corresponding to the first graph data according to the attribute information of the unit may specifically include:

for any one unit, determining node data and edge data in the unit;

determining attribute information of the node data and the edge data;

performing Hash calculation on the attribute information to obtain a word corresponding to the unit,

determining the word as one element in the set of words.

One unit may include one node, or may include a plurality of nodes, or may include one edge, or may include an edge and a node to which the corresponding edge is connected; each node has its own attribute information, such as: when a certain node represents a user, the attribute information of the node may be the attribute information of the user itself, for example: asset information, identity information of the user, etc. Each edge also has its own attribute information, such as: in the company relationship network diagram, the edge may represent position membership, department relationship, and the like. Of course, the attribute information may also include information of nodes or edges in the graph, such as: a node acts as an intermediate node in the graph connecting other nodes. The node A is connected with the node B, the node B is connected with the node C, the node D is connected with the node B, the node B is connected with the node E, a plurality of nodes are connected with the node B, and at the moment, information such as the number of the nodes connected with the node B or the type of the nodes can also be used as attribute information of the node B.

And carrying out Hash coding on the attribute information of each unit to form a word, obtaining a plurality of words corresponding to a plurality of units obtained by decomposing the graph input by the user, and forming a word set.

Since in the graph database in the embodiments of the present specification, both graph nodes and edges are stored for graph queries; the graph vectors and words are also stored for graph searching, so that when graph searching is performed, the graph can be decomposed into the form of words and vectors to facilitate graph searching.

The graph search function may include a similar graph search function and a sub graph search function, which may be discussed separately below:

function one, similar graph search function

If the incidence relation meets a preset condition, the similarity between the target graph data and the first graph data meets a first preset condition; the determining, based on the word set, target graph data in a graph database that matches the first graph data may specifically include:

carrying out standardization processing on vectors corresponding to the words in the word set to obtain first graph vectors corresponding to the first graph data;

determining a candidate graph data set corresponding to the first graph data from the graph database based on the first graph vector;

and determining the target graph data with the similarity meeting a first preset condition with the first graph data from the candidate graph database set based on the word set.

The normalizing the vectors corresponding to the words in the word set may include: and summing vectors corresponding to the words in the word set, and performing normalization processing to obtain a first graph vector corresponding to the first graph data.

After the graph is decomposed into a word set, for each word, the word is encoded into a vector according to the HASH value of the word as a seed, specifically, the vector of each word is added, the addition is performed by adding each dimension to a bit, and the normalization is performed so that the modulus of the vector is 1.

When searching for the similar graph, firstly, on the basis of the graph vector of the first graph data, the first M candidate graph data sets with the vector distance meeting the distance threshold are screened from the graph database in advance, then the similarity is calculated on the basis of the word list, and the target graph data is determined from the candidate graph data sets according to the similarity.

The determining, from the candidate graph database set, the target graph data whose similarity with the first graph data satisfies a first preset condition based on the word set may specifically include:

calculating a similarity between words in the set of words and words in the candidate graph database;

and determining the target graph data of which the similarity is greater than or equal to a preset similarity threshold in the candidate graph database.

And if a plurality of target image data with the similarity greater than or equal to a preset similarity threshold exist in the candidate image database, determining the image data with the maximum similarity in the candidate image data as the target image data.

In a graph database, a set of words may be stored in the form of a list of words.

In performing the graph search, first, the graph a is given, along with other search conditions. Decomposing the graph A according to a preset rule, determining N graphs which meet other searching conditions and have vector distances meeting preset distances from a graph database through calculation according to graph vectors corresponding to the graph A, simultaneously taking out word lists of the N graphs, calculating word similarity of the N graphs and the graph A by using the word similarity, and taking the graph with the similarity meeting the preset conditions as a target graph according to the similarity.

In addition, the word similarity values can be reordered from large to small according to the obtained similarity results, then the graph with the maximum similarity is input according to a preset condition or ordered according to the similarity, and the top M graphs are output, wherein M is less than or equal to N. And then outputs the search result.

In the specific calculation of the similarity, the following formula may be used for calculation:

wherein, the two word lists wordsA and wordsB, and the word list with repeated two words is wordsAB. The function len is defined as the number of list words, max is the maximum, the numerator represents the two A, B repeated word lists in the two graphs, and the denominator represents the maximum of the number of two list words.

After determining the target graph data in the graph database that matches the first graph data based on the set of words, the method may further include: and sending the ID of the target graph data and the similarity value to a terminal of the user.

After the corresponding target graph is found based on the graph input by the user, the ID and the similarity value of the target graph can be sent to the user, so that the user can view the searched target graph and the similarity degree.

Functional two, sub graph search

determining a candidate graph data set containing the word set according to an inverted index method based on the word set;

calculating the inclusion degree of each graph data in the word set and the candidate graph data set;

and determining the graph data with the inclusion degree greater than or equal to a preset inclusion degree threshold value in the candidate graph data set as target graph data.

If a plurality of image data with the inclusion degree larger than or equal to a preset inclusion degree threshold exist in the candidate image data set, determining the image data with the maximum inclusion degree in the candidate image data as target image data.

In calculating the subgraph inclusion degree, the following formula can be adopted:

the sub-graph word list subwordsA and the word list subwordb corresponding to the graph in the graph database are word lists with repeated two words. Defining function len to tabulate the number of words, the numerator can represent the number of words that coincide and the denominator can represent the number of words in the sub-graph.

Subgraph search, it is understood that a graph containing subgraphs with a high probability is searched, specifically subgraph a is given first, and other search conditions. And decomposing the sub-graph a according to a preset rule, obtaining a word list corresponding to the sub-graph a based on Hash coding after decomposing the sub-graph a to obtain units of the sub-graph a, and searching M graphs which meet other searching conditions and contain the most word lists corresponding to the word list graph a in a database by using a module with an inverted index function. And further calculating sub-graph inclusion degrees of the M graphs and the sub-graph a, reordering the results according to the sub-graph inclusion degrees from large to small, then outputting the search results, wherein the input search results can contain information such as the ID of the graphs and the sub-graph inclusion degrees, and sending the ID of the target graph data and the inclusion value to the terminal of the user.

In one embodiment, a user may modify data stored in a graph database, and as with a general graph database operation flow, the user may provide an ID of a graph, a node, an edge of the graph, and attribute information of other graphs to be inserted, deleted, or modified through an interface, modify information in the graph according to a user instruction if a storage or modification command is issued, and extract the modified graph, and if a deletion command directly deletes data corresponding to an original graph in the graph database, perform graph decomposition according to the modified graph, where a node itself may become a unit, and a connection relationship between a node and a node may also become a unit. Traversing all decomposed units in the graph, and performing HASH coding to form a word by using the attribute information of the units. The probability of one-to-many occurrence of a unit and a word (HASH value) is small, and it can be determined that there is a one-to-one correspondence between the unit and the word. For each word, it can be encoded as a vector, e.g.: a 64-bit vector or a 128-bit vector, etc. The vector of the sum of all word vectors of one graph after normalization is used as the graph vector of the whole graph. If the original command is to store or modify the graph vector and all the word HASH values into or modify the corresponding data of the original graph in the graph database, and if the original command is to delete the graph, the data of the original graph in the graph database is deleted. Specifically, the following steps can be adopted:

modifying the corresponding original graph data in the graph database based on the modification instruction to obtain second graph data; the second graph data comprises an ID of the original graph data, an ID of the second graph data, attribute information of node data in the second graph data and attribute information of edge data in the second graph data; the second graph data comprises an ID of the original graph data, an ID of the second graph data, attribute information of node data in the second graph data and attribute information of edge data in the second graph data;

and processing the second graph data according to the preset rule and then storing the second graph data into the graph database.

The implementation steps can also include:

and deleting the original graph data in the graph database.

When the graph is decomposed in units, the decomposition may be performed according to a preset rule, or may be performed based on attribute information of nodes or edges in the graph, for example: when the graph is subjected to unit decomposition, if the node itself is set to 0-order decomposition, and the node and the edge contained in the node itself through the K-step path are K-order decomposition, the unit of 0-order + 1-order decomposition is adopted to code into words, and in practical application, a single 1-order decomposition, or 0-order decomposition + 1-order decomposition + 2-order decomposition, or a combination of other decomposition modes can be adopted according to the scale of the graph per se and storage. When the attribute of the node or the edge is added to perform the unit decomposition of the graph, the following description can be made with reference to fig. 3: fig. 3 is an exploded exemplary diagram in a diagram searching method provided in an embodiment of the present disclosure, and fig. 3 is a schematic structural diagram of two aldehydes, which is considered as an example of diagram data, for decomposition:

where each atom can be taken as a node and the chemical bond as an edge.

The nodes are not particularly marked as having carbon elements of

The hydrogen element is

Oxygen element is

Chemical bonds are classified into single bonds, double bonds, and triple bonds according to the number of common electron pairs. The decomposition method may not consider the node property, i.e. the influence of the isotope, and may also consider the influence of the isotope.

In the following, the influence of isotopes and chemical bond attributes is taken into consideration, and assuming that the words are decomposed in a manner of nodes themselves (0 th order) and in a relationship (1 st order) in which the nodes are directly connected with the nodes, the graph a can be decomposed into C, H, C, O, H, C-C. Decompose graph b into

H，H，H，C，O，H，

C＝O，C-H，

If the influence of isotopes is not considered, but only the influence of the nature of chemical bonds, graph a can be decomposed into C, H, C, O, H, C-H, C ═ O, C-H, C-C. And (3) decomposing the graph b into C, H, H, C, O, H, C-H, C-O, C-H and C-C.

In the case of performing cell decomposition on a graph, a specific decomposition method may be set according to an actual application scenario, or decomposition may be performed based on attribute information of a point or an edge, and this is not particularly limited in the embodiments of the present specification.

Of course, the solution in the embodiment of the present specification retains the functions of storing the graph database and querying the graph, in addition to the graph search. Namely, the scheme in the embodiment of the specification can enable the graph database to have the same storage function, the graph query function and the graph search function.

The scheme in the embodiment of the specification can at least achieve the following technical effects:

1) when the words are subjected to vector coding, a random coding mode is adopted, infinite vectors can be coded in a one-to-one correspondence mode according to a hash theory, a better effect is obtained, and the problems that a skip-word model (skip-word) can be coded by using the mode only when a limited number of words are needed and the skip-word model can be used only when data are taken in advance for training are solved. The method adopted in the embodiment of the specification can improve the precision, does not need extra training cost and model storage cost, and saves a large amount of server computing resources and time cost.

2) The method in the embodiment of the specification can provide the service of graph search while storing the graph, can be conveniently used by research and development personnel in many application scenes, and fills the blank that the existing graph database cannot support graph search, and can support the functions of similar graph search and subgraph search.

3) The calling subgraphs of the changed part can be searched to obtain the calling graph containing the subgraphs, and when the program is changed, the program analysis can analyze the static graph before and after the change point. The case influenced by the change is analyzed through the static diagram of the change points before and after the change, so that the influence surface of each change can be obtained, the test case is further constructed for the influenced part, and compared with the test of the full case, a large amount of test time is saved. For example: assuming that 1 ten thousand test cases exist for a user, once a program is changed, the 1 ten thousand test cases need to be used for one-to-one test, and the test needs to be performed for 1 ten thousand times, similar cases can be searched in advance by using the method of the embodiment of the specification, duplication is removed, and the test cases are reduced. After the program is changed, it can be determined which use cases are affected by the program change based on the relationship included in the subgraph, for example: the change only affects the test cases of scenario a. Only the test case in the scene A needs to be adopted for testing.

In the above embodiment, the interaction subject may include a server and a graph database, it should be noted that the server may be a server independent from the graph database, and data interaction may be performed between the server and the graph database, and of course, the server may also be a server in the graph database and serves as a certain functional interface or a data processing module in the graph database. In the following steps, the server and the graph database are taken as two main bodies, which are only for the convenience of describing the interaction of the schemes, and the server and the graph database are not limited to be independent. The scheme can be divided into a graph storage phase, a graph query phase and a graph search phase.

And (3) a graph storage stage: in the stage, the server may be a functional interface of the graph database and is responsible for receiving the graph input by the user, decomposing the graph input by the user according to a preset rule into units, performing hash calculation on attributes of the units to obtain words corresponding to the units, finally determining word lists and graph vectors corresponding to the graph, and storing the word lists and the graph vectors in the graph database.

And (3) a graph query stage: the server may receive a graph query request from a user, where the graph query request may include attribute information of nodes and/or edges, and search a corresponding target graph from the graph database based on information input by the user.

And (3) a graph searching stage: if the graph is similar to the graph, the server receives the graph input by the user, decomposes the graph according to a decomposition rule of a storage stage, obtains a word list and a graph vector corresponding to the input graph, screens the first M candidate graph data sets with vector distances meeting a distance threshold value from a graph database in advance based on the graph vector, then calculates the similarity based on the word list, and determines target graph data from the candidate graph data sets according to the similarity. And if the graph is a subgraph search, directly solving the inclusion degree between the word list corresponding to the input graph and the word list corresponding to the graph in the graph database, and determining the target graph based on the inclusion degree.

Based on the same idea, the embodiments of the present specification further provide an apparatus corresponding to the method in the foregoing embodiments. Fig. 4 is a schematic structural diagram of a graph search apparatus provided in an embodiment of the present specification. As shown in fig. 4, the apparatus may include:

a first graph data acquiring module 410, configured to acquire first graph data input by a user; the first graph data comprises point data and edge data;

a decomposition module 420, configured to perform a decomposition operation on the first graph data to obtain a corresponding graph data unit;

a word set generating module 430, configured to generate a word set corresponding to the first graph data according to attribute information of each graph data unit;

a graph data search module 440 configured to search for graph data related to the first graph data based on the word set.

The examples of this specification also provide some specific embodiments of the apparatus based on the apparatus of fig. 4, which is described below.

Optionally, the apparatus may further include:

optionally, the graph data searching module 440 may specifically include:

and the similar graph data searching unit is used for determining target graph data, the similarity of which with the first graph data meets a first preset condition, in the graph database on the basis of the word set.

Optionally, the graph data searching module 440 may specifically include:

and the sub-graph data searching unit is used for determining target graph data, the inclusion degree of which between the graph database and the first graph data meets a second preset condition, on the basis of the word set.

Optionally, the decomposition module 420 may specifically include:

decomposing the first graph data according to a preset rule to obtain a corresponding unit; one of the units includes one node data in the first graph data, or one of the units includes edge data in the first graph data and node data to which the edge is connected;

optionally, the word set generating module 430 may be specifically configured to:

for any one unit, determining node data and edge data in the unit;

determining attribute information of the node data and the edge data;

determining the word as one element in the set of words.

Optionally, the similarity graph data searching unit may specifically include:

the first graph vector calculating subunit is used for carrying out standardization processing on vectors corresponding to the words in the word set to obtain first graph vectors corresponding to the first graph data;

a candidate map data set subunit configured to determine, from the map database, a candidate map data set corresponding to the first map data based on the first map vector;

and the similarity map data determining subunit is used for determining the target map data, of which the similarity with the first map data meets a first preset condition, from the candidate map database set based on the word set.

Optionally, the similarity graph data determining subunit may be specifically configured to:

Optionally, if a plurality of target map data with the similarity greater than or equal to a preset similarity threshold exist in the candidate map database, determining the map data with the maximum similarity in the candidate map data as the target map data.

Optionally, the apparatus may further include:

and the first result sending module is used for sending the ID of the target graph data and the similarity value to the terminal of the user.

Optionally, the sub-graph data searching unit may specifically include:

a candidate graph data set determining subunit, configured to determine, based on the word set, a candidate graph data set including the word set according to an inverted index method;

the inclusion degree calculation subunit is used for calculating the inclusion degree of each graph data in the word set and the candidate graph data set;

and the sub-graph data determining subunit is used for determining the graph data with the inclusion degree greater than or equal to a preset inclusion degree threshold value in the candidate graph data set as the target graph data.

Optionally, if a plurality of map data with the inclusion degree greater than or equal to a preset inclusion degree threshold exist in the candidate map data set, the map data with the maximum inclusion degree in the candidate map data set is determined as the target map data.

Optionally, the apparatus may further include:

and the second result sending module is used for sending the ID of the target graph data and the inclusion value to the terminal of the user.

Optionally, the apparatus may further include:

the second image data acquisition module is used for acquiring second image data input by the user; the second graph data is obtained by modifying original graph data contained in the graph database; the second graph data comprises an ID of the original graph data, an ID of the second graph data, attribute information of node data in the second graph data and attribute information of edge data in the second graph data;

and the second graph data storage module is used for processing the second graph data according to the preset rule and then storing the second graph data into the graph database.

Optionally, the apparatus may further include:

and the graph data deleting module is used for deleting the original graph data in the graph database.

Based on the same idea, the embodiment of the present specification further provides a device corresponding to the above method.

Fig. 5 is a schematic structural diagram of a graph search apparatus provided in an embodiment of this specification. As shown in fig. 5, the apparatus 500 may include:

at least one processor 510; and the number of the first and second groups,

a memory 530 communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory 530 stores instructions 520 that are executable by the at least one processor 510. The instructions are executable by the at least one processor 510 to enable the at least one processor 510 to:

Based on the same idea, the embodiment of the present specification further provides a computer-readable medium corresponding to the above method. The computer readable medium has computer readable instructions stored thereon. The computer readable instructions are executable by a processor to implement a method of:

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital character system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate a dedicated integrated circuit chip. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: the ARC625D, AtmelAT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices. For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information which can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A graph search method, comprising:

2. The method according to claim 1, wherein the searching for graph data related to the first graph data specifically comprises:

and determining target graph data, the similarity of which to the first graph data in the graph database meets a first preset condition, on the basis of the word set.

3. The method according to claim 1, wherein the searching for graph data related to the first graph data specifically comprises:

and determining target graph data, the inclusion degree of which with the first graph data meets a second preset condition, in the graph database based on the word set.

4. The method according to claim 1, wherein the performing a decomposition operation on the first graph data to obtain a corresponding unit specifically includes:

5. The method according to claim 1, wherein the generating a word set corresponding to the first graph data according to the attribute information of the unit specifically includes:

for any one unit, determining node data and edge data in the unit;

determining attribute information of the node data and the edge data;

determining the word as one element in the set of words.

6. The method according to claim 2, wherein the determining, based on the word set, target graph data in a graph database whose similarity to the first graph data satisfies a first preset condition specifically includes:

7. The method according to claim 6, wherein the determining, based on the set of words, the target graph data from the set of candidate graph databases whose similarity to the first graph data satisfies a first preset condition includes:

8. The method according to claim 7, wherein if there are a plurality of target map data in the candidate map database having the similarity greater than or equal to a predetermined similarity threshold, determining the map data with the greatest similarity in the candidate map data as the target map data.

9. The method according to claim 2, after determining target map data in a map database whose similarity to the first map data satisfies a first preset condition based on the set of words, further comprising:

and sending the ID of the target graph data and the similarity value to a terminal of the user.

10. The method according to claim 3, wherein the determining, based on the set of words, target graph data in a graph database whose inclusion degree with respect to the first graph data satisfies a second preset condition includes:

11. The method of claim 10, wherein if there are a plurality of map data with the inclusion degree greater than or equal to a predetermined inclusion degree threshold in the candidate map data set, the map data with the highest inclusion degree in the candidate map data set is determined as the target map data.

12. The method according to claim 3, after determining target map data in a map database whose inclusion degree with the first map data satisfies a second preset condition based on the set of words, further comprising:

and sending the ID of the target graph data and the inclusion value to a terminal of the user.

13. The method of claim 1, further comprising:

acquiring a modification instruction input by the user;

modifying the corresponding original graph data in the graph database based on the modification instruction to obtain second graph data; the second graph data comprises an ID of the original graph data, an ID of the second graph data, attribute information of node data in the second graph data and attribute information of edge data in the second graph data;

14. The method of claim 13, further comprising:

and deleting the original graph data in the graph database.

15. A graph search apparatus comprising:

16. A graph searching apparatus comprising:

at least one processor; and the number of the first and second groups,

17. A computer readable medium having computer readable instructions stored thereon which are executable by a processor to implement the method of any one of claims 1 to 14.