CN109597856A - A kind of data processing method, device, electronic equipment and storage medium - Google Patents
A kind of data processing method, device, electronic equipment and storage medium Download PDFInfo
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Abstract
The present invention relates to data processing method, device, electronic equipment and storage mediums.This method comprises: the attribute information of each node in object knowledge map network to be converted to the space vector feature of numerical value expression, entity attribute eigenmatrix is obtained；Obtain the Laplacian Matrix that the entity relationship diagram of each node is characterized in object knowledge map network；Determine that the final vector space of each node in object knowledge map network indicates according to entity attribute eigenmatrix and Laplacian Matrix；Calculate the final vector similarity in object knowledge map network between every two node；Vector similarity calculated result is greater than the node of preset threshold to merging.This method passes through the study using entity attributes information in knowledge mapping and adjacency information progress entity vector space expression, more comprehensive and accurate vector, which can be obtained, to be indicated, it avoids because entity attribute lacks, attribute value changes and bring entity similarity calculation inaccuracy problem.
Description
Technical field
The invention belongs to technical field of data processing, and in particular to a kind of data processing method, device, electronic equipment and deposit
Storage media.
Background technique
The construction needs of knowledge mapping are updated with the continuous renewal of knowledge, for example, it is desired to update the category of original entity
Property perhaps relationship or needs to increase new entity and relationship etc..Whether this needs to determine the entity newly increased in original map
In have existed, an a unique entity that new entity link permeates to original entity is needed if having existed,
Update the entity attributes and relationship.
It is to determine whether entity from different sources can carry out using entity attributes information that existing entity, which merges common method,
Alignment can be sentenced if Existence and uniquenss mark in entity attributes by the unique identification between two entities
It is fixed, if there is no unique identification's attribute, then entity attributes information can be subjected to vectorization expression, calculate two vectors
Similarity.
Summary of the invention
In consideration of it, the embodiment of the present invention provides a kind of data processing method, device, electronic equipment and storage medium, to have
Effect ground improves because attribute information is not complete or attribute information variation influences the calculating of similarity, and then influences the accurate of entity fusion
Property.
The embodiment of the present invention is achieved in that
In a first aspect, the embodiment of the invention provides a kind of data processing methods, comprising: obtain object knowledge map net
Network；The space vector feature that the attribute information of each node in the object knowledge map network is converted to numerical value expression, obtains
To entity attribute eigenmatrix；Obtain the La Pula that the entity relationship diagram of each node is characterized in the object knowledge map network
This matrix；It is determined according to the entity attribute eigenmatrix and the Laplacian Matrix each in the object knowledge map network
The final vector space of a node indicates；Calculate the final vector phase in the object knowledge map network between every two node
Like degree；Vector similarity calculated result is greater than the node of preset threshold to merging.
In the embodiment of the present application, by carrying out entity vector using entity attributes information in knowledge mapping and adjacency information
The study of space representation, can obtain more comprehensive and accurate vector indicates, avoids because entity attribute lacks, attribute value becomes
Change and bring entity similarity calculation inaccuracy problem, and then improves the accuracy and reliability of entity fusion.
A kind of possible embodiment of embodiment with reference to first aspect, it is described to obtain in the object knowledge map network
Characterize the Laplacian Matrix of the entity relationship diagram of each node, comprising: obtain in the object knowledge map network and characterize respectively
The degree matrix of the degree of a node；Obtain the adjacency matrix that each node connecting object is characterized in the object knowledge map network；
The Laplacian Matrix is determined according to the degree matrix and the adjacency matrix.
Another possible embodiment of embodiment with reference to first aspect, the object knowledge map network include n
Node, n are the integer greater than 1；It is described that the mesh is determined according to the entity attribute eigenmatrix and the Laplacian Matrix
The final vector space for marking each node in knowledge mapping network indicates, comprising:
I-th of node and n node in the object knowledge map network are calculated based on the entity attribute eigenmatrix
In each node vector similarity, obtain similarity matrix, wherein the i-th row in the similarity matrix indicates i-th of section
The vector similarity of point and each node in n node, i are more than or equal to 1, are less than or equal to n；According to the similarity matrix and institute
It states Laplacian Matrix and determines that the final vector space of each node in the object knowledge map network indicates.
Another possible embodiment of embodiment with reference to first aspect, it is general according to the similarity matrix and the drawing
Lars matrix determine each node in the object knowledge map network final vector space indicate, comprising: according to finally to
Quantity space representative function, the similarity matrix and the Laplacian Matrix determine each in the object knowledge map network
The final vector space of node indicates, wherein the final vector space representative function are as follows:
WhereinS represents the similarity matrix, and W represents the La Pula
This matrix, H represent final vector space representing matrix, and λ is adjustment factor, and being more than or equal to 0 and being less than or equal to 1, h is each node
Final vector space indicates.
Another possible embodiment of embodiment with reference to first aspect calculates every in the object knowledge map network
Vector similarity between two nodes, comprising: by clustering algorithm to each node pair in the object knowledge map network
The vector characteristics answered are clustered；Calculate the final vector similarity belonged in a cluster between every two node.
Second aspect, the embodiment of the invention also provides a kind of data processing equipments, comprising: first obtains module, conversion
Module, second obtain module, determining module, computing module and Fusion Module；First obtains module, for obtaining object knowledge
Map network；Conversion module, for the attribute information of each node in the object knowledge map network to be converted to numerical tabular
The space vector feature shown, obtains entity attribute eigenmatrix；Second obtains module, in the object knowledge map network
Characterize the Laplacian Matrix of the entity relationship diagram of each node；Determining module, for according to the entity attribute eigenmatrix
Determine that the final vector space of each node in the object knowledge map network indicates with the Laplacian Matrix；Calculate mould
Block, for calculating the vector similarity in the object knowledge map network between every two node；Fusion Module, for will be to
Amount similarity calculation result is greater than the node of preset threshold to merging.
In conjunction with a kind of possible embodiment of second aspect embodiment, described second obtains module, is also used to: obtaining institute
State the degree matrix that the degree of each node is characterized in object knowledge map network；It obtains in the object knowledge map network and characterizes respectively
The adjacency matrix of a node connecting object；The Laplacian Matrix is determined according to the degree matrix and the adjacency matrix.
In conjunction with another possible embodiment of second aspect embodiment, the object knowledge map network includes n
Node, n are the integer greater than 1；The determining module, is also used to: calculating the target based on the entity attribute eigenmatrix
The vector similarity of i-th of node and each node in n node, obtains similarity matrix, wherein institute in knowledge mapping network
Stating the i-th row in similarity matrix indicates that the vector similarity of i-th of node with each node in n node, i are more than or equal to 1,
Less than or equal to n；It is determined according to the similarity matrix and the Laplacian Matrix each in the object knowledge map network
The final vector space of node indicates.
In conjunction with another possible embodiment of second aspect embodiment, the determining module is also used to: according to final
Vector space representative function, the similarity matrix and the Laplacian Matrix determine each in the object knowledge map network
The final vector space of a node indicates, wherein the final vector space representative function are as follows:
WhereinS represents the similarity matrix, and W represents the La Pula
This matrix, H represent final vector space representing matrix, and λ is adjustment factor, and being more than or equal to 0 and being less than or equal to 1, h is each node
Final vector space indicates.
In conjunction with another possible embodiment of second aspect embodiment, the computing module is also used to: passing through cluster
Algorithm clusters the corresponding vector characteristics of node each in the object knowledge map network；Calculating belongs in a cluster
Final vector similarity between every two node.
The third aspect, the embodiment of the invention also provides a kind of electronic equipment, comprising: memory and processor, it is described to deposit
Reservoir is connected with the processor；The memory is for storing program；The processor is stored in the storage for calling
Program in device, to execute first aspect embodiment and/or with reference to first aspect any possible embodiment of embodiment
The method of offer.
Fourth aspect, the embodiment of the invention also provides a kind of storage medium, the storage medium includes computer program,
Any of first aspect embodiment and/or embodiment with reference to first aspect is executed when the computer program is run by computer
The method that possible embodiment provides.
Other features and advantages of the present invention will be illustrated in subsequent specification, also, partly be become from specification
It is clear that being understood by implementing the embodiment of the present invention.The objectives and other advantages of the invention can be by written
Specifically noted structure is achieved and obtained in specification, claims and attached drawing.
Detailed description of the invention
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the invention
Example, for those of ordinary skill in the art, without creative efforts, can also obtain according to these attached drawings
Obtain other attached drawings.By the way that shown in attached drawing, above and other purpose of the invention, feature and advantage will be more clear.In whole
Identical appended drawing reference indicates identical part in attached drawing.Attached drawing, emphasis deliberately are not drawn by actual size equal proportion scaling
It is to show the gist of the present invention.
Fig. 1 shows the structural schematic diagram of a kind of electronic equipment provided in an embodiment of the present invention.
Fig. 2 shows a kind of flow diagrams of data processing method provided in an embodiment of the present invention.
Fig. 3 shows the schematic diagram of object knowledge map network provided in an embodiment of the present invention.
Fig. 4 shows a kind of module diagram of data processing equipment provided in an embodiment of the present invention.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.The present invention being usually described and illustrated herein in the accompanying drawings is implemented
The component of example can be arranged and be designed with a variety of different configurations.
Therefore, the detailed description of the embodiment of the present invention provided in the accompanying drawings is not intended to limit below claimed
The scope of the present invention, but be merely representative of selected embodiment of the invention.Based on the embodiments of the present invention, this field is common
Technical staff's every other embodiment obtained without creative efforts belongs to the model that the present invention protects
It encloses.
It should also be noted that similar label and letter indicate similar terms in following attached drawing, therefore, once a certain Xiang Yi
It is defined in a attached drawing, does not then need that it is further defined and explained in subsequent attached drawing.
In the description of the present invention, it should be noted that term " first ", " second ", " third " etc. are only used for distinguishing and retouch
It states, is not understood to indicate or imply relative importance.Furthermore term "and/or" in the application, only a kind of description is closed
Join the incidence relation of object, indicates may exist three kinds of relationships, for example, A and/or B, can indicate: individualism A is deposited simultaneously
In A and B, these three situations of individualism B.
As shown in Figure 1, Fig. 1 shows the structural block diagram of a kind of electronic equipment 100 provided in an embodiment of the present invention.The electricity
Sub- equipment 100 includes: data processing equipment 110, memory 120, storage control 130 and processor 140.
The memory 120, storage control 130, each element of processor 140 directly or indirectly electrically connect between each other
It connects, to realize the transmission or interaction of data.For example, these elements can pass through one or more communication bus or signal between each other
Line, which is realized, to be electrically connected.The data processing equipment 110 includes at least one can be in the form of software or firmware (firmware)
It is stored in the memory 120 or is solidificated in the operating system (operating system, OS) of the electronic equipment 100
Software function module.The processor 140 is for executing the executable module stored in memory 120, such as the data
The software function module or computer program that processing unit 110 includes.
Wherein, memory 120 may be, but not limited to, random access memory (Random Access Memory,
RAM), read-only memory (Read Only Memory, ROM), programmable read only memory (Programmable Read-
Only Memory, PROM), erasable read-only memory (Erasable Programmable Read-Only Memory,
EPROM), electricallyerasable ROM (EEROM) (Electric Erasable Programmable Read-Only Memory,
EEPROM) etc..Wherein, memory 120 is for storing program, and the processor 140 is after receiving and executing instruction, described in execution
Program, method performed by the electronic equipment 100 for the flow definition that aftermentioned any embodiment of the embodiment of the present invention discloses can answer
It is realized in processor 140, or by processor 140.
Processor 140 may be a kind of IC chip, the processing capacity with signal.Above-mentioned processor can be
General processor, including central processing unit (Central Processing Unit, CPU), network processing unit (Network
Processor, NP) etc.；It can also be digital signal processor (DSP), specific integrated circuit (ASIC), field-programmable gate array
Arrange (FPGA) either other programmable logic device, discrete gate or transistor logic, discrete hardware components.It may be implemented
Or disclosed each method, step and logic diagram in the execution embodiment of the present invention.General processor can be microprocessor
Or the processor is also possible to any conventional processor etc..
Wherein, in embodiments of the present invention, the electronic equipment 100 may be, but not limited to, network server, database
Server, cloud server etc..
Referring to Fig. 2, being a kind of data processing side applied to above-mentioned electronic equipment 100 provided in an embodiment of the present invention
Method, the step of including to it below in conjunction with Fig. 2, are illustrated.
Step S101: object knowledge map network is obtained.
When analyzing some knowledge mapping network, using the network as object knowledge map network, to be analyzed.Its
In, knowledge mapping network is a kind of graph data structure of presentation-entity relationship, and each node in figure indicates to deposit in real world
" entity ", " relationship " of each edge between entity and entity.It simply says, knowledge mapping network is exactly to use network of personal connections
The mode of network links together entities various in the world by its correlation, and help is analyzed between personnel's progress entity
Association analysis and reasoning.
Step S102: the attribute information of each node in the object knowledge map network is converted to the sky of numerical value expression
Between vector characteristics, obtain entity attribute eigenmatrix.
It is indicated by the vector space that network representation learns entity in study object knowledge map network.Wherein, net list
Dendrography practises (Network Representation Learning), and also referred to as internet startup disk learns (Network Embedding
Learning), figure insertion study (Graph Embedding Learning), network representation study is a kind of distributed expression
Learning art, the social networks knot vector for learning low dimensional indicate.It assists in many analysis tasks and carries out chain
Connect prediction, node clustering.Under normal circumstances, network representation indoctrination session is believed using the adjacent of node in static leveling network
Breath study node is indicated in the vector of vector space, but in knowledge mapping network, each node in figure is often with rich
Rich attribute information, for example, the node that entity type is Person may include name, date of birth, native place, the attributes such as occupation
Information.Adjacency information of the node in figure is used alone can not learn node in the expression of cyberspace comprehensively, this is also to cause
The reason of similarity calculation result inaccuracy under existing amalgamation mode.
Wherein, it should be noted that existing entity, which merges common method, to be determined not using entity attributes information
Whether source entities can be aligned, can be by between two entities if Existence and uniquenss mark in entity attributes
Unique identification determines, if there is no unique identification's attribute, then entity attributes information can be carried out vectorization
It indicates, calculates the similarity of two vectors.Present inventor has found during invention the application: due in Project Realization
May not be able to comprehensive collection entity attributes information so that entity attribute certain dimensions missing and cause to be believed according to entity attribute
Breath carries out similarity calculation result inaccuracy.Further, since entity attributes are that dynamic changes over time, only according to entity
Attribute information carry out entity disambiguate probably due to same entity possess different attribute in different time and cause to be mistaken for difference
Entity.
For defect present in existing scheme, be inventor being obtained after practicing and carefully studying as a result,
Therefore, the discovery procedure of the above problem and the solution that hereinafter embodiment of the present invention is proposed regarding to the issue above, all
It should be the contribution that inventor makes the present invention in process of the present invention.
Therefore, pass through the attribute information and adjacency information of each node (entity) in learning knowledge map in the embodiment of the present application
Vector space indicate, to solve defect existing for existing amalgamation mode.
Wherein it is possible to pass through the vector space table of the attribute information of each node (entity) in following steps learning knowledge map
Show, e.g., the attribute information of each node in the object knowledge map network is converted into numerical value expression using Feature Engineering
Space vector feature obtains the entity attribute eigenmatrix of knowledge mapping.Wherein, the text information in attribute can be used
The mode of word2vec is converted to numerical characteristics vector, and the classification information in attribute can be used one-hot and be encoded to numerical value spy
Sign.For example, the entity attribute information of each node as shown in Table 1 can be converted into through the above way as shown in Table 2
Entity attribute eigenmatrix.Wherein, the name of each entity is learnt using the mode (e.g., word2vec) that character is embedded in, native place
It is encoded with occupation using one-hot mode.
Table 1
| Entity | Name | Height | Weight | Native place | Occupation |
| 1 | Li Gang | 173 | 56 | Henan | Doctor |
| 2 | Li Jing | 168 | 72 | Henan | Doctor |
| 3 | Li Gang | 166 | 77 | Henan | Doctor |
| 4 | Li Gang | 179 | 63 | Hebei | Doctor |
| 5 | Li Yugang | 180 | 66 | Hebei | Teacher |
| 6 | Zhang Xin | 172 | 64 | Hubei | Engineer |
| 7 | Li Gang | 177 | 63 | Hunan | Civil servant |
| 8 | Wang Gui | 185 | 69 | Hunan | Civil servant |
Table 2
| [0.2,0.3] | 173 | 56 | [0,0,0,1] | [0,0,0,1] |
| [0.4,0.3] | 168 | 72 | [0,0,0,1] | [0,0,0,1] |
| [0.2,0.3] | 166 | 77 | [0,0,0,1] | [0,0,0,1] |
| [0.2,0.3] | 179 | 63 | [0,0,1,0] | [0,0,0,1] |
| [0.3,0.3] | 180 | 66 | [0,0,1,0] | [0,0,1,0] |
| [0.2,0.3] | 172 | 64 | [0,1,0,0] | [0,1,0,0] |
| [0.2,0.3] | 177 | 63 | [1,0,0,0] | [1,0,0,0] |
| [0.5,0.3] | 185 | 69 | [1,0,0,0] | [1,0,0,0] |
Step S103: the Laplce that the entity relationship diagram of each node is characterized in the object knowledge map network is obtained
Matrix.
After getting object knowledge map network to be analyzed, obtains in the object knowledge map network and characterize each section
The Laplacian Matrix of the entity relationship diagram of point.Wherein, Laplacian Matrix can be according to the degree square of object knowledge map network
Battle array and adjacency matrix determine.Therefore, the entity relationship diagram that each node is characterized in the object knowledge map network is obtained
Laplacian Matrix, comprising: obtain the degree matrix that the degree of each node is characterized in the object knowledge map network；Described in acquisition
The adjacency matrix of each node connecting object is characterized in object knowledge map network；According to the degree matrix and the adjacency matrix
Determine the Laplacian Matrix.Wherein, the calculation formula of Laplacian Matrix is as follows:
L=D-C, wherein L matrix is the Laplacian Matrix to be calculated, and D matrix is the degree matrix of figure, and C is the adjoining of figure
Matrix.
For example, the degree matrix of object knowledge map network shown in Fig. 3, adjacency matrix, Laplacian Matrix can be with such as
Under table indicate:
Table 3 (the degree matrix of Fig. 3)
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 |
| 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 |
| 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 |
| 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
Table 4 (adjacency matrix of Fig. 3)
| 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 |
| 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
Table 5 (Laplacian Matrix of Fig. 3)
| 1 | 0 | - 1 | 0 | 0 | 0 | 0 | 0 |
| 0 | 1 | - 1 | 0 | 0 | 0 | 0 | 0 |
| - 1 | - 1 | 5 | 1 | - 1 | - 1 | 0 | 0 |
| 0 | 0 | - 1 | 1 | 0 | 0 | 0 | 0 |
| 0 | 0 | - 1 | 0 | 2 | 0 | 0 | - 1 |
| 0 | 0 | - 1 | 0 | 0 | 3 | 0 | - 1 |
| 0 | 0 | 0 | 0 | 0 | - 1 | 1 | 0 |
| 0 | 0 | 0 | 0 | - 1 | - 1 | 0 | 2 |
Wherein, table 3 is the degree matrix of Fig. 3, and table 4 is the adjacency matrix of Fig. 3, and table 5 is the Laplacian Matrix of Fig. 3.Wherein,
Each row in table corresponds to each node in Fig. 3, e.g., the first row in table 3, the node 1 in corresponding diagram 3, the in table 3
Two rows, the node 2 in corresponding diagram 3, remaining situation are similar therewith.
Wherein, for node 1, only a line, therefore, the value in corresponding degree matrix is 1；Similarly, for section
For point 2, also only a line, therefore, the value in corresponding degree matrix is 1；Similarly, for node 3, there are 5 sides, because
This, the value in corresponding degree matrix is 5, remaining situation is similar therewith.
Wherein, for node 1, the node being attached thereto is 3, and therefore, the value of the 3rd column is in corresponding adjacency matrix
1；Similarly, for node 2, the node being attached thereto is 3, and therefore, the value of the 3rd column is 1 in corresponding adjacency matrix；Together
Reason, for node 3, the node being attached thereto is 1,2,4,5,6, therefore, the 1st, 2,4,5,6 column in corresponding adjacency matrix
Value be 1, remaining situation is similar therewith.
Step S104: the object knowledge figure is determined according to the entity attribute eigenmatrix and the Laplacian Matrix
The final vector space for composing each node in network indicates.
After learning the entity attribute eigenmatrix and Laplacian Matrix to object knowledge map network, according to the two
Matrix is that can determine that the final vector space of each node in object knowledge map network indicates.As a kind of optional embodiment party
Formula, for example, being indicated by the final vector space that formula V=f (T, L) calculates each node in object knowledge map network.Its
Middle T represents entity attribute eigenmatrix, and L represents the Laplacian Matrix of entity relationship diagram, and V represents entity in object knowledge map
Final vector space indicate that f be the function for calculating the expression of final vector space, usually a convolutional neural networks.For example,
When the final vector space of calculate node 1 indicates, by the entity attribute feature and La Pu of entity attribute eigenmatrix interior joint 1
The relationship characteristic of Lars matrix interior joint 1 inputs convolutional neural networks, and the final vector space that the node 1 can be obtained indicates,
The acquisition process that the final vector space of remaining node indicates is similar therewith.
As another optional embodiment, the entity attribute eigenmatrix of object knowledge map network is arrived in study
Afterwards, the vector similarity of each node in each node and all nodes in the object knowledge map network is further calculated,
Obtain similarity matrix.In order to make it easy to understand, assuming that the object knowledge map network includes n node, n is the integer greater than 1.
At this point, the above process is to calculate i-th of node in the object knowledge map network based on the entity attribute eigenmatrix
With the vector similarity of node each in n node, similarity matrix is obtained, wherein the i-th row table in the similarity matrix
Show that the vector similarity of i-th of node with each node in n node, i are more than or equal to 1, is less than or equal to n.That is, one includes
The object knowledge map network of n node calculates the phase of the available n*n dimension of similarity by attribute feature vector two-by-two
Like matrix.In order to make it easy to understand, being illustrated by taking object knowledge map network shown in Fig. 3 as an example, namely obtaining Fig. 3's
After entity attribute eigenmatrix, for node 1, the vector similarity of calculate node 1 Yu node 1, node 1 and node are needed
2 vector similarity, the vector similarity of node 1 and node 3, the vector similarity of node 1 and node 4, node 1 and node 5
Vector similarity, the vector similarity of node 1 and node 6, the vector similarity of node 1 and node 7, node 1 and node 8
Vector similarity.Similarly, for node 2, the vector similarity of calculate node 2 Yu node 1, node 2 and node 2 are needed
Vector similarity, the vector similarity of node 2 and node 3, the vector similarity of node 2 and node 4, node 2 and node 5 to
Measure similarity, the vector similarity of node 2 and node 6, the vector similarity of node 2 and node 7, the vector of node 2 and node 8
Similarity.The calculated case of remaining each node is similar therewith.It can be obtained by the similar matrix of n*n dimension in this way.
Wherein, the calculation formula of vector similarity is as follows:
Wherein, the attributive character of a node is indicated with vector A, and the attribute of another node is indicated with vector B.Attribute to
Each in amount is the corresponding characteristic value of each attribute of entity, includes 5 attributes, e.g., (surname for node 1 e.g.
Name, height, weight, native place, occupation).
After obtaining similarity matrix, determine that the target is known according to the similarity matrix and the Laplacian Matrix
The final vector space for knowing each node in map network indicates.For example, it may be according to final vector space representative function, institute
It states similarity matrix and the Laplacian Matrix determines that the final vector of each node in the object knowledge map network is empty
Between indicate, wherein the final vector space representative function are as follows:
,
WhereinS represents the similarity matrix, and W represents the drawing
This matrix of pula, H represent final vector space representing matrix, and λ is adjustment factor, and being more than or equal to 0 and being less than or equal to 1, h is each section
The final vector space of point indicates.By solving above-mentioned representative function, can be indicated in the hope of the final vector space of each entity
h。
Wherein, calculating the expression of final vector space should meet, and (1) is originally two similar in the attribute space attribute value
Entity, in the vector space finally acquired also the same close (first condition), (2) were originally adjacent in knowledge mapping network
Two entities, in the vector space finally acquired also the same close (second condition).That is, in above-mentioned representative function
First item corresponds to first above-mentioned condition, and the Section 2 in representative function corresponds to second above-mentioned condition.
Step S105: the final vector similarity in the object knowledge map network between every two node is calculated.
In obtaining object knowledge map network after the final vector space expression of each node, the object knowledge is calculated
Final vector similarity in map network between every two node obtains the final vector similarity of each node pair, such as
To the final vector similarity of node 1 and node 2, the final vector similarity of node 1 and node 3, node 1 and node 4 are most
Whole vector similarity, the final vector similarity of node 1 and node 5, the final vector similarity of node 1 and node 6, node 2
With the final vector similarity of node 3 etc..
As an alternative embodiment, can be to reduce difficulty in computation and first pass through clustering algorithm to the mesh
The corresponding vector characteristics of each node are clustered in mark knowledge mapping network, in this way can will be each in object knowledge map network
For a node division at multiple and different clusters, the nodal community association inside each cluster is very strong, and the nodal community association between cluster is very
It is weak.In this way in the final vector similarity in calculating between every two node, so that it may which calculating belongs to every two in a cluster
Final vector similarity between a node can reduce difficulty in computation and save the time.
Wherein, clustering algorithm can be currently used clustering algorithm, such as K-Means clustering algorithm.
Step S106: vector similarity calculated result is greater than the node of preset threshold to merging.
After obtaining the final vector similarity of each node pair, vector similarity calculated result is filtered out greater than default
The node pair of threshold value, by the node for the condition that meets to merging namely attribute merges.For example, node 1 and node 2
Vector similarity calculated result is greater than preset threshold, then merge by node 1 and node 2, at this point, becoming a new section
Point, when fusion, the attribute of the two is merged.
In conclusion data processing method provided by the embodiments of the present application, by will be each in object knowledge map network
The attribute information of node is converted to the space vector feature of numerical value expression, obtains entity attribute eigenmatrix, and obtain the mesh
The Laplacian Matrix that the entity relationship diagram of each node is characterized in mark knowledge mapping network, then further according to entity attribute feature
Matrix and Laplacian Matrix determine that the final vector space of each node in object knowledge map network indicates, are finally based on again
The final vector space of each node indicates that the final vector calculated between every two node is similar in object knowledge map network
Degree, so that vector similarity calculated result to be greater than to the node of preset threshold to merging.Pass through the map that turns one's knowledge to advantage
Middle entity attributes information and adjacency information carry out the study of entity vector space expression, can obtain it is more comprehensive and accurate to
Amount indicates, avoids because entity attribute lacks, attribute value changes and bring entity similarity calculation inaccuracy problem.This
Outside, by using clustering algorithm, the space vector of entity is divided into different subspaces, is carried out finally by subspace
The calculating of similarity two-by-two avoids full figure entity and calculates bring performance issue under the conditions of big data quantity two-by-two.
The embodiment of the present application also provides a kind of data processing equipments 110, as shown in Figure 4.The data processing equipment 110 packet
Include: the first acquisition module 111, conversion module 112, second obtain module 113, determining module 114, computing module 115 and melt
Mold block 116.
First obtains module 111, for obtaining object knowledge map network.
Conversion module 112, for the attribute information of each node in the object knowledge map network to be converted to numerical value
The space vector feature of expression, obtains entity attribute eigenmatrix.
Second obtains module 113, for characterizing the entity relationship diagram of each node in the object knowledge map network
Laplacian Matrix.
Determining module 114, for determining the mesh according to the entity attribute eigenmatrix and the Laplacian Matrix
The final vector space for marking each node in knowledge mapping network indicates.
Computing module 115, for calculating the vector similarity in the object knowledge map network between every two node.
Fusion Module 116, for vector similarity calculated result to be greater than to the node of preset threshold to merging.
It should be noted that all the embodiments in this specification are described in a progressive manner, each embodiment weight
Point explanation is the difference from other embodiments, and the same or similar parts between the embodiments can be referred to each other.
The technical effect of data processing equipment 110 provided by the embodiment of the present invention, realization principle and generation and aforementioned
Embodiment of the method is identical, and to briefly describe, Installation practice part does not refer to place, can refer to corresponding in preceding method embodiment
Content.
The embodiment of the present application also provides a kind of storage medium, the storage medium includes computer program, the calculating
Machine program executes above-mentioned data processing method when being run by computer.
In several embodiments provided herein, it should be understood that disclosed device and method can also pass through
Other modes are realized.The apparatus embodiments described above are merely exemplary, for example, flow chart and block diagram in attached drawing
Show the device of multiple embodiments according to the present invention, the architectural framework in the cards of method and computer program product,
Function and operation.In this regard, each box in flowchart or block diagram can represent the one of a module, section or code
Part, a part of the module, section or code, which includes that one or more is for implementing the specified logical function, to be held
Row instruction.It should also be noted that function marked in the box can also be to be different from some implementations as replacement
The sequence marked in attached drawing occurs.For example, two continuous boxes can actually be basically executed in parallel, they are sometimes
It can execute in the opposite order, this depends on the function involved.It is also noted that every in block diagram and or flow chart
The combination of box in a box and block diagram and or flow chart can use the dedicated base for executing defined function or movement
It realizes, or can realize using a combination of dedicated hardware and computer instructions in the system of hardware.
In addition, each functional module in each embodiment of the present invention can integrate one independent portion of formation together
Point, it is also possible to modules individualism, an independent part can also be integrated to form with two or more modules.
It, can be with if the function is realized and when sold or used as an independent product in the form of software function module
It is stored in a computer readable storage medium.Based on this understanding, technical solution of the present invention is substantially in other words
The part of the part that contributes to existing technology or the technical solution can be embodied in the form of software products, the meter
Calculation machine software product is stored in a storage medium, including some instructions are used so that a computer equipment (can be a
People's computer, laptop, server or network equipment etc.) execute the whole of each embodiment the method for the present invention
Or part steps.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only
Memory), random access memory (RAM, Random Access Memory), magnetic or disk etc. are various can store journey
The medium of sequence code.It should be noted that, in this document, relational terms such as first and second and the like are used merely to one
A entity or operation with another entity or operate distinguish, without necessarily requiring or implying these entities or operation it
Between there are any actual relationship or orders.Moreover, the terms "include", "comprise" or its any other variant are intended to
Cover non-exclusive inclusion, so that the process, method, article or equipment for including a series of elements not only includes those
Element, but also including other elements that are not explicitly listed, or further include for this process, method, article or setting
Standby intrinsic element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that
There is also other identical elements in the process, method, article or apparatus that includes the element.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made any to repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of data processing method characterized by comprising
Obtain object knowledge map network；
The space vector feature that the attribute information of each node in the object knowledge map network is converted to numerical value expression, obtains
To entity attribute eigenmatrix；
Obtain the Laplacian Matrix that the entity relationship diagram of each node is characterized in the object knowledge map network；
It is determined according to the entity attribute eigenmatrix and the Laplacian Matrix each in the object knowledge map network
The final vector space of node indicates；
Calculate the final vector similarity in the object knowledge map network between every two node；
Vector similarity calculated result is greater than the node of preset threshold to merging.
2. the method according to claim 1, wherein described obtain in the object knowledge map network characterizes respectively
The Laplacian Matrix of the entity relationship diagram of a node, comprising:
Obtain the degree matrix that the degree of each node is characterized in the object knowledge map network；
Obtain the adjacency matrix that each node connecting object is characterized in the object knowledge map network；
The Laplacian Matrix is determined according to the degree matrix and the adjacency matrix.
3. n is the method according to claim 1, wherein the object knowledge map network includes n node
Integer greater than 1；It is described that the object knowledge figure is determined according to the entity attribute eigenmatrix and the Laplacian Matrix
The final vector space for composing each node in network indicates, comprising:
Based on each in i-th of node in the entity attribute eigenmatrix calculating object knowledge map network and n node
The vector similarity of a node, obtains similarity matrix, wherein the i-th row in the similarity matrix indicate i-th of node with
The vector similarity of each node in n node, i are more than or equal to 1, are less than or equal to n；
Each node in the object knowledge map network is determined according to the similarity matrix and the Laplacian Matrix
Final vector space indicates.
4. according to the method described in claim 3, it is characterized in that, according to the similarity matrix and the Laplacian Matrix
Determine that the final vector space of each node in the object knowledge map network indicates, comprising:
The object knowledge is determined according to final vector space representative function, the similarity matrix and the Laplacian Matrix
The final vector space of each node indicates in map network, wherein the final vector space representative function are as follows:
,
WhereinS represents the similarity matrix, and W represents the Laplce
Matrix, H represent final vector space representing matrix, and λ is adjustment factor, be more than or equal to 0 be less than or equal to 1, h be each node most
Whole vector space indicates.
5. the method according to claim 1, wherein calculating every two node in the object knowledge map network
Between vector similarity, comprising:
The corresponding vector characteristics of node each in the object knowledge map network are clustered by clustering algorithm；
Calculate the final vector similarity belonged in a cluster between every two node.
6. a kind of data processing equipment characterized by comprising
First obtains module, for obtaining object knowledge map network；
Conversion module, for the attribute information of each node in the object knowledge map network to be converted to the sky of numerical value expression
Between vector characteristics, obtain entity attribute eigenmatrix；
Second obtains module, the Laplce of the entity relationship diagram for characterizing each node in the object knowledge map network
Matrix；
Determining module, for determining the object knowledge figure according to the entity attribute eigenmatrix and the Laplacian Matrix
The final vector space for composing each node in network indicates；
Computing module, for calculating the vector similarity in the object knowledge map network between every two node；
Fusion Module, for vector similarity calculated result to be greater than to the node of preset threshold to merging.
7. device according to claim 6, which is characterized in that described second obtains module, is also used to: obtaining the target
The degree matrix of the degree of each node is characterized in knowledge mapping network；
Obtain the adjacency matrix that each node connecting object is characterized in the object knowledge map network；
The Laplacian Matrix is determined according to the degree matrix and the adjacency matrix.
8. device according to claim 6, which is characterized in that the object knowledge map network includes n node, and n is
Integer greater than 1；The determining module, is also used to:
Based on each in i-th of node in the entity attribute eigenmatrix calculating object knowledge map network and n node
The vector similarity of a node, obtains similarity matrix, wherein the i-th row in the similarity matrix indicate i-th of node with
The vector similarity of each node in n node, i are more than or equal to 1, are less than or equal to n；
Each node in the object knowledge map network is determined according to the similarity matrix and the Laplacian Matrix
Final vector space indicates.
9. a kind of electronic equipment characterized by comprising memory and processor, the memory are connected with the processor；
The memory is for storing program；
The processor is for calling the program being stored in the memory, to execute such as any one of claim 1-5 institute
The method stated.
10. a kind of storage medium, which is characterized in that the storage medium includes computer program, and the computer program is counted
Calculation machine executes the method according to claim 1 to 5 when running.
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