CN110490331A - The processing method and processing device of knowledge mapping interior joint - Google Patents
The processing method and processing device of knowledge mapping interior joint Download PDFInfo
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- CN110490331A CN110490331A CN201910785567.7A CN201910785567A CN110490331A CN 110490331 A CN110490331 A CN 110490331A CN 201910785567 A CN201910785567 A CN 201910785567A CN 110490331 A CN110490331 A CN 110490331A
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Abstract
The present invention provides a kind of processing method and processing devices of knowledge mapping interior joint, wherein, this method comprises: obtaining the attribute value of each node in the knowledge mapping, wherein, the attribute value includes: the Betweenness Centrality after normalization, the degree centrality close to centrality, after normalization after normalization, node type coefficient after localized clusters coefficient after normalization, and normalization；The attribute value is input in model trained in advance and obtains the different degree of each node in the knowledge mapping.Through the invention, solve the problems, such as in the related technology for the pitch point importance in knowledge mapping describe it is relatively simple lack it is comprehensive.
Description
Technical field
The present invention relates to computer fields, in particular to a kind of processing method and processing device of knowledge mapping interior joint.
Background technique
It is divided into entity and relationship in the data of knowledge mapping, each entity suffers from different meanings in reality scene
Justice.When carrying out the analysis of map, accurately knows the different degree of the entity node of spectrum data, there is important analysis valence
Value.
In the prior art using by Betweenness Centrality, close to centrality, centrality is spent, localized clusters coefficient four
One of attribute defines the importance of entity；But Betweenness Centrality spends centrality, localized clusters coefficient is all only close to centrality
It is that description entity different degree in some respect is gone in terms of some, with one of them as the complete description of different degree,
Lack comprehensive, poor accuracy.
In view of the above problems in the related art, not yet there is effective solution at present.
Summary of the invention
The embodiment of the invention provides a kind of processing method and processing devices of knowledge mapping interior joint, at least to solve related skill
The comprehensive problem of relatively simple shortage is described for the pitch point importance in knowledge mapping in art.
According to one embodiment of present invention, a kind of processing method of knowledge mapping interior joint is provided, comprising: obtain institute
State the attribute value of each node in knowledge mapping, wherein the attribute value includes: the Betweenness Centrality after normalization, normalization
Afterwards close to centrality, degree centrality after normalization, the node class after the localized clusters coefficient after normalization, and normalization
Type coefficient；The attribute value is input in model trained in advance and obtains the different degree of each node in the knowledge mapping.
According to another embodiment of the invention, a kind of processing unit of knowledge mapping interior joint is provided, comprising: first
Module is obtained, for obtaining the attribute value of each node in the knowledge mapping, wherein after the attribute value includes: normalization
Betweenness Centrality, after normalization close to centrality, degree centrality after normalization, the localized clusters coefficient after normalization,
And the node type coefficient after normalization；Processing module is obtained for the attribute value to be input in model trained in advance
The different degree of each node into the knowledge mapping.
According to still another embodiment of the invention, a kind of storage medium is additionally provided, meter is stored in the storage medium
Calculation machine program, wherein the computer program is arranged to execute the step in any of the above-described embodiment of the method when operation.
According to still another embodiment of the invention, a kind of electronic device, including memory and processor are additionally provided, it is described
Computer program is stored in memory, the processor is arranged to run the computer program to execute any of the above-described
Step in embodiment of the method.
Through the invention, the attribute value of each node in knowledge mapping is obtained, wherein attribute value includes: after normalizing
Betweenness Centrality, after normalization close to centrality, degree centrality after normalization, the localized clusters coefficient after normalization, with
And the node type coefficient after normalization, and then attribute value is input in advance trained model obtain it is each in knowledge mapping
The different degree of node；As it can be seen that be in this application according to the Betweenness Centrality after normalization, after normalization close to centrality,
Degree centrality after normalization, the localized clusters coefficient after normalization, and normalization after node type coefficient come determine section
Point different degree, rather than determined according to some attribute, it solves in the related technology for the node weight in knowledge mapping
It spends and describes the comprehensive problem of relatively simple shortage.
Detailed description of the invention
The drawings described herein are used to provide a further understanding of the present invention, constitutes part of this application, this hair
Bright illustrative embodiments and their description are used to explain the present invention, and are not constituted improper limitations of the present invention.In the accompanying drawings:
Fig. 1 is the hardware block diagram of the terminal of the processing method of the knowledge mapping interior joint of the embodiment of the present invention；
Fig. 2 is the flow chart of the processing method of knowledge mapping interior joint according to an embodiment of the present invention；
Fig. 3 is the structural block diagram of the processing unit of knowledge mapping interior joint according to an embodiment of the present invention.
Specific embodiment
Hereinafter, the present invention will be described in detail with reference to the accompanying drawings and in combination with Examples.It should be noted that not conflicting
In the case of, the features in the embodiments and the embodiments of the present application can be combined with each other.
Firstly, being illustrated accordingly to the term in the application；
Betweenness Centrality: refer to that a node serves as the number of the bridge of shortest path between other two nodes.One
The number that a node serves as " intermediary " is higher, its intermediary's centrad is bigger.
How the Betweenness Centrality of calculate node:
1) any 1 node A in subgroup, is taken, the shortest path quantity summation of the node and other nodes of subgroup is calculated,
It is denoted as N；
2), while by the node on each shortest path, the number passed through according to shortest path is recorded as M1, M2 ...,
There is no what path passed through to be recorded as 0；
3) the single Betweenness Centrality for, calculating each node is recorded as M1/N, M2/N ... ..；
4), flag node A, which is calculated, completes, and takes subgroup next node to calculate, repeats step 1, until all nodes calculate
It completes；
5) it, sums to the single Betweenness Centrality of each node, as the Betweenness Centrality of the node.
Degree centrality: possessing one the non-directed graph of g node, and the degree centrality of node i is i and other g-1 sections
The directly connection sum of point, calculation method are expressed as follows:
Wherein, CD (Ni) indicates the degree centrad of node i,
For calculate node i and other g-1 j nodes, (i ≠ j, exclusion i are contacted with itself；That is, main diagonal
The value of line can be ignored) between the quantity directly contacted.
Close to centrality: as soon as calculating be a point to other all the points distance summation, the smaller explanation of this summation
The path of this point to other all the points is shorter, that is to say, this bright point is closer apart from other all the points.Calculation method are as follows: 1/
(all nodes being connected with this node and).
Localized clusters coefficient: the gathereding degree in measurement figure near each node, calculation method are expressed as follows:
Nodes oriented finds out its immediate neighbor node set Ni for node vi, calculates the number of edges in the network that Ni is constituted
K gathers possible number of edges divided by Ni | Ni | * (| Ni | -1)/2.
LR algorithm: the logistic regression algorithm of machine learning；
Decision Tree algorithms: the decision Tree algorithms of machine learning.
It should be noted that description and claims of this specification and term " first " in above-mentioned attached drawing, "
Two " etc. be to be used to distinguish similar objects, without being used to describe a particular order or precedence order.
Embodiment 1
Embodiment of the method provided by the embodiment of the present application one can be filled in terminal, terminal or similar operation
Set middle execution.For running at the terminal, Fig. 1 is a kind of processing method of knowledge mapping interior joint of the embodiment of the present invention
The hardware block diagram of terminal.As shown in Figure 1, terminal 10 may include one or more (only showing one in Fig. 1) processors
102 (processing units that processor 102 can include but is not limited to Micro-processor MCV or programmable logic device FPGA etc.) and use
In the memory 104 of storing data, optionally, above-mentioned terminal can also include for communication function transmission device 106 and
Input-output equipment 108.It will appreciated by the skilled person that structure shown in FIG. 1 is only to illustrate, not to above-mentioned
The structure of terminal causes to limit.For example, terminal 10 may also include the more perhaps less component than shown in Fig. 1 or have
The configuration different from shown in Fig. 1.
Memory 104 can be used for storing computer program, for example, the software program and module of application software, such as this hair
The corresponding computer program of processing method of knowledge mapping interior joint in bright embodiment, processor 102 are stored in by operation
Computer program in memory 104 realizes above-mentioned method thereby executing various function application and data processing.It deposits
Reservoir 104 may include high speed random access memory, may also include nonvolatile memory, as one or more magnetic storage fills
It sets, flash memory or other non-volatile solid state memories.In some instances, memory 104 can further comprise relative to place
The remotely located memory of device 102 is managed, these remote memories can pass through network connection to terminal 10.The example of above-mentioned network
Including but not limited to internet, intranet, local area network, mobile radio communication and combinations thereof.
Transmission device 106 is used to that data to be received or sent via a network.Above-mentioned network specific example may include
The wireless network that the communication providers of terminal 10 provide.In an example, transmission device 106 includes a network adapter
(Network Interface Controller, referred to as NIC), can be connected by base station with other network equipments so as to
It is communicated with internet.In an example, transmission device 106 can be radio frequency (Radio Frequency, referred to as RF)
Module is used to wirelessly be communicated with internet.
A kind of processing method of knowledge mapping interior joint for running on above-mentioned terminal is provided in the present embodiment, and Fig. 2 is
The flow chart of the processing method of knowledge mapping interior joint according to an embodiment of the present invention, as shown in Fig. 2, the process includes following step
It is rapid:
Step S202, obtain knowledge mapping in each node attribute value, wherein attribute value include: normalization after in
Jie's centrality, after normalization close to centrality, degree centrality after normalization, the localized clusters coefficient after normalization, and
Node type coefficient after normalization；
Attribute value is input to and obtains the important of each node in knowledge mapping in advance trained model by step S204
Degree.
S202 and step S204 through the above steps obtains the attribute value of each node in knowledge mapping, wherein attribute value
Include: the Betweenness Centrality after normalization, after normalization close to centrality, degree centrality after normalization, after normalization
Node type coefficient after localized clusters coefficient, and normalization, and then attribute value is input in model trained in advance and is obtained
The different degree of each node into knowledge mapping；As it can be seen that being according to the Betweenness Centrality after normalization, normalization in this application
Afterwards close to centrality, degree centrality after normalization, the node class after the localized clusters coefficient after normalization, and normalization
Type coefficient determines pitch point importance, rather than determined according to some attribute, it solves in the related technology for knowledge
Pitch point importance in map describes the comprehensive problem of relatively simple shortage.
In an optional embodiment in this application, the present processes step can also include:
Node in knowledge mapping is divided into multiple subgroups according to the relationship between node by step S206；
Step S208 sums to the different degree of the node in each subgroup；
Step S210 is ranked up multiple subgroups according to the different degree of each subgroup and value.
As it can be seen that S206 to step S210 through the above steps, it can be to each subgroup (set of the point of complete connection)
In each node carry out different degree summation, obtain the different degree of entire subgroup；And then when map is shown and is recommended, root
According to the different degree of subgroup, recommendation sequence is carried out.
In another optional embodiment of the application, the present processes step can also include:
Step S212 is ranked up each node in knowledge mapping according to different degree；
Step S214 is shown the node in knowledge mapping according to sequence.
As it can be seen that can also be to each node in knowledge mapping according to different degree according to above-mentioned steps S212 and step S214
Be ranked up so show, may is that in concrete application scene be more than for different degree 0.5 node, judge that the node is
Core member's node of group, preferentially shows the node: the node amplification of core member is opened up in center
Show；Other nodes according to and core member's node path (1 degree connection, 2 degree connect etc.) be divided into displaying.The node of core member
It is placed on top layer；There is direct 1 degree of relationship with core member, is placed on the second layer；Once analogize, the displaying group of completion
Institutional framework.
In another optional embodiment of the application, the model trained in the following manner；
S1 obtains the attribute value of each node in multiple knowledge mappings；
S2, based on the attribute value of each node in multiple knowledge mappings, by logistic regression LR algorithm to preset model
In parameter be trained, obtain trained model until the parameter in preset model meets preset condition.
Below with reference to the optional embodiment of the application, the application is illustrated；
In this optional embodiment, a kind of knowledge mapping group different degree prediction technique is provided, the step of this method
Include:
Step S11 calculates separately four values: Betweenness Centrality for specified spectrum data, close to centrality, in degree
Disposition, localized clusters coefficient.
Step S21 obtains the type type of node, according to actual scene, gives different types different score value, than
Such as in public security industry, previous conviction emphasis people, non-previous conviction emphasis people, ordinary people's attention rate is completely different, gives previous conviction emphasis people 20,
Non- previous conviction emphasis people 10, ordinary people 1；By the setting of score value, the type label of node is converted to the score value that can be trained to
Data.
Step S31, to 5 class data achieved above, the data processing being normalized:
Take the maximum value MAX1 of a kind of data, minimum value MIN 1, current value X；
Normalization calculation formula is X=(X-MIN1)/(MAX1-MIN1)；
Step S41, using the spectrum data (being labelled with the highest node of different degree in figure) marked, to each
Node obtains following data respectively:
Betweenness Centrality X1 after normalization, after normalization close to centrality X2, degree centrality X3 after normalization returns
Localized clusters coefficient X4, the node type coefficient X5 after normalization after one change；Wherein, when the node is the highest section of different degree
When point, record Y is 1；Otherwise Y is 0.
Step S51 is trained the spectrum data of mark using LR algorithm, LR model is obtained, with Current Model File
Format PPML format output.
Because the highest node data of different degree is always less than other node datas, the number for being 0 to the Y data for being 1 and Y
According to separately sampling guarantees 1/6th of data acquisition system not less than the Y data acquisition system for being 0 that Y is 1.
Step S61 calculates separately each node after new spectrum data storage:
Betweenness Centrality X1 after normalization, after normalization close to centrality X2, degree centrality X3 after normalization returns
Localized clusters coefficient X4, the node type coefficient X5 after normalization after one change.
Step S71, the PMML model file before use carry out the prediction of pitch point importance, obtain pitch point importance point
Number predictScore.
Through the above description of the embodiments, those skilled in the art can be understood that according to above-mentioned implementation
The method of example can be realized by means of software and necessary general hardware platform, naturally it is also possible to by hardware, but it is very much
In the case of the former be more preferably embodiment.Based on this understanding, technical solution of the present invention is substantially in other words to existing
The part that technology contributes can be embodied in the form of software products, which is stored in a storage
In medium (such as ROM/RAM, magnetic disk, CD), including some instructions are used so that a terminal device (can be mobile phone, calculate
Machine, server or network equipment etc.) execute method described in each embodiment of the present invention.
Embodiment 2
A kind of processing unit of knowledge mapping interior joint is additionally provided in the present embodiment, and the device is for realizing above-mentioned reality
Example and preferred embodiment are applied, the descriptions that have already been made will not be repeated.As used below, term " module " may be implemented
The combination of the software and/or hardware of predetermined function.Although device described in following embodiment is preferably realized with software,
The realization for being the combination of hardware or software and hardware is also that may and be contemplated.
Fig. 3 is the structural block diagram of the processing unit of knowledge mapping interior joint according to an embodiment of the present invention, as shown in figure 3,
The device includes: the first acquisition module 32, for obtaining the attribute value of each node in knowledge mapping, wherein attribute value includes:
Betweenness Centrality after normalization, after normalization close to centrality, degree centrality after normalization, the part after normalization is poly-
Node type coefficient after collecting coefficient, and normalization；Processing module 34 is of coupled connections with the first acquisition module 34, and being used for will
Attribute value is input in model trained in advance and obtains the different degree of each node in knowledge mapping.
Optionally, the device of the application can further include: division module, for by the node root in knowledge mapping
Multiple subgroups are divided into according to the relationship between node；Summation module is carried out for the different degree to the node in each subgroup
Summation；First sorting module, for being ranked up according to the different degree and value of each subgroup to multiple subgroups.
Optionally, the device of the application can further include: the second sorting module, be used for according to different degree to knowledge
Each node in map is ranked up；Display module, for being shown according to sequence to the node in knowledge mapping.
Optionally, the device of the application can further include: second obtains module, for obtaining multiple knowledge mappings
In each node attribute value；Training module, for the attribute value based on each node in multiple knowledge mappings, by patrolling
It collects recurrence LR algorithm to be trained the parameter in preset model, be obtained until the parameter in preset model meets preset condition
Trained model.
It should be noted that above-mentioned modules can be realized by software or hardware, for the latter, Ke Yitong
Following manner realization is crossed, but not limited to this: above-mentioned module is respectively positioned in same processor；Alternatively, above-mentioned modules are with any
Combined form is located in different processors.
Embodiment 3
The embodiments of the present invention also provide a kind of storage medium, computer program is stored in the storage medium, wherein
The computer program is arranged to execute the step in any of the above-described embodiment of the method when operation.
Optionally, in the present embodiment, above-mentioned storage medium can be set to store by executing based on following steps
Calculation machine program:
S1 obtains the attribute value of each node in knowledge mapping, wherein attribute value includes: the intermediary center after normalization
Property, after normalization close to centrality, degree centrality after normalization, the localized clusters coefficient after normalization, and normalization
Node type coefficient afterwards；
Attribute value is input in model trained in advance and obtains the different degree of each node in knowledge mapping by S2.
Optionally, in the present embodiment, above-mentioned storage medium can include but is not limited to: USB flash disk, read-only memory (Read-
Only Memory, referred to as ROM), it is random access memory (Random Access Memory, referred to as RAM), mobile hard
The various media that can store computer program such as disk, magnetic or disk.
The embodiments of the present invention also provide a kind of electronic device, including memory and processor, stored in the memory
There is computer program, which is arranged to run computer program to execute the step in any of the above-described embodiment of the method
Suddenly.
Optionally, above-mentioned electronic device can also include transmission device and input-output equipment, wherein the transmission device
It is connected with above-mentioned processor, which connects with above-mentioned processor.
Optionally, in the present embodiment, above-mentioned processor can be set to execute following steps by computer program:
S1 obtains the attribute value of each node in knowledge mapping, wherein attribute value includes: the intermediary center after normalization
Property, after normalization close to centrality, degree centrality after normalization, the localized clusters coefficient after normalization, and normalization
Node type coefficient afterwards；
Attribute value is input in model trained in advance and obtains the different degree of each node in knowledge mapping by S2.
Optionally, the specific example in the present embodiment can be with reference to described in above-described embodiment and optional embodiment
Example, details are not described herein for the present embodiment.
Obviously, those skilled in the art should be understood that each module of the above invention or each step can be with general
Computing device realize that they can be concentrated on a single computing device, or be distributed in multiple computing devices and formed
Network on, optionally, they can be realized with the program code that computing device can perform, it is thus possible to which they are stored
It is performed by computing device in the storage device, and in some cases, it can be to be different from shown in sequence execution herein
Out or description the step of, perhaps they are fabricated to each integrated circuit modules or by them multiple modules or
Step is fabricated to single integrated circuit module to realize.In this way, the present invention is not limited to any specific hardware and softwares to combine.
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.It is all within principle of the invention, it is made it is any modification, etc.
With replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (11)
1. a kind of processing method of knowledge mapping interior joint characterized by comprising
Obtain the attribute value of each node in the knowledge mapping, wherein the attribute value includes: the intermediary center after normalization
Property, after normalization close to centrality, degree centrality after normalization, the localized clusters coefficient after normalization, and normalization
Node type coefficient afterwards；
The attribute value is input in model trained in advance and obtains the different degree of each node in the knowledge mapping.
2. the method according to claim 1, wherein further include:
Node in the knowledge mapping is divided into multiple subgroups according to the relationship between node；
It sums to the different degree of the node in each subgroup；
The multiple subgroup is ranked up according to the different degree of each subgroup and value.
3. the method according to claim 1, wherein further include:
Each node in the knowledge mapping is ranked up according to different degree；
The node in the knowledge mapping is shown according to the sequence.
4. the method according to claim 1, wherein the model trained in the following manner；
Obtain the attribute value of each node in multiple knowledge mappings；
Based on the attribute value of each node in the multiple knowledge mapping, by logistic regression LR algorithm in preset model
Parameter is trained, and obtains trained model until the parameter in the preset model meets preset condition.
5. the method according to claim 1, wherein normalized mode are as follows:
X=(X-MIN1)/(MAX1-MIN1)；
Wherein, the maximum value MAX1 of a kind of data, minimum value MIN 1, current value X are taken.
6. a kind of processing unit of knowledge mapping interior joint characterized by comprising
First obtains module, for obtaining the attribute value of each node in the knowledge mapping, wherein the attribute value includes:
Betweenness Centrality after normalization, after normalization close to centrality, degree centrality after normalization, the part after normalization is poly-
Node type coefficient after collecting coefficient, and normalization；
Processing module obtains each node in the knowledge mapping for the attribute value to be input in model trained in advance
Different degree.
7. device according to claim 6, which is characterized in that described device further include:
Division module, for the node in the knowledge mapping to be divided into multiple subgroups according to the relationship between node；
Summation module is summed for the different degree to the node in each subgroup；
First sorting module, for being ranked up according to the different degree and value of each subgroup to the multiple subgroup.
8. device according to claim 6, which is characterized in that described device includes:
Second sorting module, for being ranked up according to different degree to each node in the knowledge mapping；
Display module, for being shown according to the sequence to the node in the knowledge mapping.
9. device according to claim 6, which is characterized in that described device further include:
Second obtains module, for obtaining the attribute value of each node in multiple knowledge mappings；
Training module passes through logistic regression LR algorithm for the attribute value based on each node in the multiple knowledge mapping
Parameter in preset model is trained, until the parameter in the preset model meet preset condition obtain it is trained
Model.
10. a kind of storage medium, which is characterized in that be stored with computer program in the storage medium, wherein the computer
Program is arranged to execute method described in any one of claim 1 to 5 when operation.
11. a kind of electronic device, including memory and processor, which is characterized in that be stored with computer journey in the memory
Sequence, the processor are arranged to run the computer program to execute side described in any one of claim 1 to 5
Method.
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| CN111813951A (en) * | 2020-06-18 | 2020-10-23 | 国网上海市电力公司 | Key point identification method based on technical map |
| CN112000718A (en) * | 2020-10-28 | 2020-11-27 | 成都数联铭品科技有限公司 | Attribute layout-based knowledge graph display method, system, medium and equipment |
| CN112214616A (en) * | 2020-10-20 | 2021-01-12 | 北京明略软件系统有限公司 | Knowledge graph smooth display method and device |
| CN113836244A (en) * | 2021-09-27 | 2021-12-24 | 天弘基金管理有限公司 | Sample acquisition method, model training method, relation prediction method and device |
| CN114254132A (en) * | 2022-03-01 | 2022-03-29 | 北京理工大学 | Retrieval method of material data |
| CN114265526A (en) * | 2021-12-10 | 2022-04-01 | 北京金堤科技有限公司 | Map scaling method and device, storage medium and electronic equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111813951A (en) * | 2020-06-18 | 2020-10-23 | 国网上海市电力公司 | Key point identification method based on technical map |
| WO2021253758A1 (en) * | 2020-06-18 | 2021-12-23 | 国网上海市电力公司 | Key node identification method based on technology graph |
| CN112214616A (en) * | 2020-10-20 | 2021-01-12 | 北京明略软件系统有限公司 | Knowledge graph smooth display method and device |
| CN112214616B (en) * | 2020-10-20 | 2024-02-23 | 北京明略软件系统有限公司 | Knowledge graph fluency display method and device |
| CN112000718A (en) * | 2020-10-28 | 2020-11-27 | 成都数联铭品科技有限公司 | Attribute layout-based knowledge graph display method, system, medium and equipment |
| CN112000718B (en) * | 2020-10-28 | 2021-05-18 | 成都数联铭品科技有限公司 | Attribute layout-based knowledge graph display method, system, medium and equipment |
| CN113836244A (en) * | 2021-09-27 | 2021-12-24 | 天弘基金管理有限公司 | Sample acquisition method, model training method, relation prediction method and device |
| CN113836244B (en) * | 2021-09-27 | 2023-04-07 | 天弘基金管理有限公司 | Sample acquisition method, model training method, relation prediction method and device |
| CN114265526A (en) * | 2021-12-10 | 2022-04-01 | 北京金堤科技有限公司 | Map scaling method and device, storage medium and electronic equipment |
| CN114254132A (en) * | 2022-03-01 | 2022-03-29 | 北京理工大学 | Retrieval method of material data |
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Application publication date: 20191122 |