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CN116187868A - Knowledge graph-based industrial chain development quality evaluation method and device - Google Patents

Knowledge graph-based industrial chain development quality evaluation method and device Download PDF

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CN116187868A
CN116187868A CN202310468634.9A CN202310468634A CN116187868A CN 116187868 A CN116187868 A CN 116187868A CN 202310468634 A CN202310468634 A CN 202310468634A CN 116187868 A CN116187868 A CN 116187868A
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胡为民
郑喜
谢凡
黄婵娟
谢丽慧
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Shenzhen Dib Enterprise Risk Management Technology Co ltd
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Abstract

The invention relates to the technical field of knowledge graph and industry chain decision making, and discloses an industry chain development quality evaluation method and device based on the knowledge graph, wherein the method comprises the following steps: acquiring an industrial chain development entity list, and acquiring association relations between every two entities in the industrial chain development entity list; weighting the association relation between every two entities to obtain a weighted association relation between the entities; constructing an industrial chain knowledge graph according to the industrial chain development entity list and the weighted association relation between the entities; constructing an importance evaluation model of key elements of the industrial chain through a random walk strategy according to the knowledge graph of the industrial chain; and evaluating the importance of the key elements in the industrial chain knowledge graph through an industrial chain key element importance evaluation model, and acquiring the development quality score of the industrial chain to be evaluated by combining the coverage condition of the key elements of the industrial chain to be evaluated. The invention realizes the purpose of accurately and effectively evaluating the development quality of the industrial chain.

Description

Knowledge graph-based industrial chain development quality evaluation method and device
Technical Field
The invention relates to the technical field of knowledge graph and industry chain decision making, in particular to an industry chain development quality evaluation method and device based on the knowledge graph.
Background
The quality evaluation of the development of the industry chain is helpful for the digital government to make macroscopic decisions on the aspects of safety of the industry chain, layout of the industry chain and the like, and related research works at home and abroad are mostly qualitative researches, for example, key elements forming the industry chain are displayed in a visual mode such as a hierarchical graph, a network graph and the like. Research work on evaluation of the development quality of an industrial chain, particularly research work on development evaluation based on an industrial chain knowledge graph is relatively rare.
Secondly, the traditional industrial chain knowledge graph construction method is generally based on a graph database, and the industrial chain knowledge graph construction is constructed by manually inputting triples or importing structured relational data in batches. The traditional industrial chain knowledge graph construction method has low efficiency in a manual mode, and is easy to miss input or input in error when the data volume is huge, so that accurate reasoning cannot be carried out by utilizing the constructed industrial chain knowledge graph in the follow-up process.
Disclosure of Invention
Based on the above, the embodiment of the invention provides a knowledge graph-based industrial chain development quality evaluation method and device, which aim to solve the problem of how to accurately and effectively evaluate the industrial chain development quality.
In order to solve the above problems, an embodiment of the present invention provides a method for evaluating quality of industrial chain development based on a knowledge graph, including:
acquiring an industrial chain development entity list, and acquiring association relations between every two entities in the industrial chain development entity list;
weighting the association relation between every two entities to obtain a weighted association relation between the entities;
constructing an industry chain knowledge graph according to the industry chain development entity list and the weighted association relation between the entities;
according to the industrial chain knowledge graph, an industrial chain key element importance evaluation model is constructed through a random walk strategy;
and evaluating the importance of the key elements in the industrial chain knowledge graph through the industrial chain key element importance evaluation model, and acquiring the development quality score of the industrial chain to be evaluated by combining the coverage condition of the key elements of the industrial chain to be evaluated.
In addition, the embodiment of the invention also provides an industrial chain development quality evaluation device based on the knowledge graph, which comprises:
the entity and relation processing module is used for acquiring an industrial chain development entity list and acquiring association relation between every two entities in the industrial chain development entity list;
the relation weighting module is used for weighting the association relation between every two entities to obtain a weighted association relation between the entities;
the industrial chain knowledge graph construction module is used for constructing an industrial chain knowledge graph according to the industrial chain development entity list and the weighted association relation between the entities;
the importance evaluation model construction module is used for constructing an importance evaluation model of the key elements of the industrial chain through a random walk strategy according to the industrial chain knowledge graph;
the industrial chain development quality evaluation module is used for evaluating the importance of the key elements in the industrial chain knowledge graph through the industrial chain key element importance evaluation model and acquiring the development quality score of the industrial chain to be evaluated by combining the key element coverage condition of the industrial chain to be evaluated.
The industrial chain development quality evaluation method and device based on the knowledge graph provided by the embodiment of the invention have the following beneficial effects:
1) According to the embodiment of the invention, the industrial chain knowledge graph is constructed according to the weighted association relationship between the industrial chain development entity and the entity, the knowledge graph can be automatically constructed, the construction efficiency of the knowledge graph is improved, and meanwhile, accurate and error-free data support can be provided for the follow-up knowledge graph reasoning;
2) According to the embodiment of the invention, the importance of the key elements in the knowledge graph is calculated through the industrial chain key element importance evaluation model, and the development quality of the industrial chain to be evaluated is quantified by combining the key element coverage condition of the industrial chain to be evaluated, so that the development quality of the industrial chain can be effectively and accurately evaluated, and the support of the high-quality development of the industrial chain to make macroscopic decision is facilitated.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of an industrial chain development quality evaluation method based on a knowledge graph according to an embodiment of the present invention;
fig. 2 is a flowchart of step S10 in an industrial chain development quality evaluation method based on a knowledge graph according to an embodiment of the present invention;
FIG. 3 is an exemplary diagram of a knowledge graph of a real estate industry chain according to an embodiment of the present invention;
FIG. 4 is a flowchart of another method for evaluating quality of development of an industrial chain based on a knowledge graph according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an industrial chain development quality evaluation device based on a knowledge graph according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of another industrial chain development quality evaluation device based on a knowledge graph according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with aspects of the invention as detailed in the accompanying claims.
Example 1
As shown in fig. 1, a flowchart of an industrial chain development quality evaluation method based on a knowledge graph provided by an embodiment of the present invention includes the following steps:
s10, acquiring an industrial chain development entity list, and acquiring association relations between every two entities in the industrial chain development entity list.
In step S10, the industry chain development entity list includes a plurality of industry chain development entities, and the industry chain development entities are names of various key elements extracted from the industry chain development related articles and forming the industry chain, wherein the key elements include related industries, enterprises and the like.
The association relationship between every two entities can be quantified through the co-occurrence probability between the entities of the industrial chain development, and the co-occurrence probability can represent the semantic association relationship between the entities.
Preferably, as shown in fig. 2, the step S10 includes the steps of:
s101, acquiring an industrial chain development related article, extracting an industrial chain development entity from the industrial chain development related article, and constructing an industrial chain development entity list.
In step S101, an industrial chain development related article may be obtained by a crawler technology, and an industrial chain development entity may be extracted from the industrial chain development related article by a named entity recognition method to form an industrial chain development entity list, which may be expressed as
Figure SMS_1
，
Figure SMS_2
Is the number of entities.
Alternatively, named entity recognition methods may be rule and dictionary based methods or statistical based methods that are currently in common use.
S102, determining two entities which co-occur in the article related to the industrial chain development, calculating the co-occurrence probability of the two entities which co-occur and the occurrence probability of each entity, and calculating the conditional transition probability of the two entities which co-occur.
In step S102, an entity list is developed based on the industry chain
Figure SMS_5
Counting two entities
Figure SMS_7
Co-occurrence probability in industry chain development related literature>
Figure SMS_10
The co-occurrence probability represents semantic association between two entities and counts the occurrence probability of each entity in the industry chain development related literature>
Figure SMS_4
At this time, two entities are present in accordance with the co-occurrence +.>
Figure SMS_8
Co-occurrence probability>
Figure SMS_11
And the occurrence probability of each entity +.>
Figure SMS_12
Two entities +.>
Figure SMS_3
Conditional transition probability->
Figure SMS_6
The conditional transition probability->
Figure SMS_9
The calculation formula of (2) is as follows:
Figure SMS_13
。
s103, determining two non-co-occurring entities in the article related to the industrial chain development, and calculating the conditional transition probability of the two non-co-occurring entities according to a first-order Markov chain model and the conditional transition probability of the two co-occurring entities.
In step S103, for two entities that never co-occur in the industry chain development related articles
Figure SMS_14
Calculating two entities +.>
Figure SMS_15
Conditional transition probabilities of (2). Wherein the first order Markov chain model may be expressed as:
Figure SMS_16
，
in the above-mentioned method, the step of,
Figure SMS_17
for realizing entity->
Figure SMS_18
And entity->
Figure SMS_19
An associated intermediate entity. It should be noted that two entities +.>
Figure SMS_20
And two entities->
Figure SMS_21
Are commonly found in industry chain development related articles.
S104, calculating the co-occurrence probability of the two non-co-occurrence entities according to the conditional transition probability of the two non-co-occurrence entities and the occurrence probability of each entity.
I.e. based on two entities not co-occurring
Figure SMS_22
Conditional transition probability of (2) and occurrence probability of each entity +.>
Figure SMS_23
Two entities +.>
Figure SMS_24
Co-occurrence probability>
Figure SMS_25
The co-occurrence probability
Figure SMS_26
The calculation formula of (2) is as follows:
Figure SMS_27
。
s105, obtaining the association relation between every two entities in the industrial chain development entity list according to the co-occurrence probability of the two entities which co-occur and do not co-occur.
I.e. based on two entities co-occurring
Figure SMS_28
And two entities which do not co-occur +.>
Figure SMS_29
And quantifying the association relationship between every two entities.
And S20, weighting the association relation between every two entities to obtain the weighted association relation between the entities.
In step S20, a weight coefficient between every two entities is introduced, and a weighted calculation is performed on the association relationship between every two entities, so as to obtain a weighted association relationship between the entities.
Preferably, the weight coefficient between every two entities is used to represent importance of association relationship between every two entities to the field of industrial chain development. At this time, the step S20 includes the steps of:
s201, acquiring weight coefficients of every two entities in the industry chain development related literature through a TF-IDF algorithm; the calculation formula of the weight coefficient between every two entities is as follows:
Figure SMS_30
，
in the above-mentioned method, the step of,
Figure SMS_32
for every two entities->
Figure SMS_35
Weight coefficient between->
Figure SMS_37
Is the co-occurrence probability between every two entities, < ->
Figure SMS_33
For the statistical function of quantity->
Figure SMS_34
For the development of the relevant literature sets of the industry chain, +.>
Figure SMS_36
For all documents total->
Figure SMS_38
The representation comprises two entities->
Figure SMS_31
Is a total number of documents.
S202, weighting the association relation between every two entities according to the weight coefficient between every two entities to obtain the weighted association relation between the entities.
In step S202, the quantization formula of the weighted association relationship between entities is:
Figure SMS_39
，
in the above-mentioned method, the step of,
Figure SMS_40
and weighting association relations among the entities. It can be understood that in this embodiment, the association relationship between the industrial chain development entities is extracted by the markov chain model, and then weighted by the TF-IDF algorithm, so that quantification of the association relationship between the entities can be achieved, and data support is provided for construction of the industrial chain knowledge graph.
S30, constructing an industrial chain knowledge graph through a triplet expression method according to the industrial chain development entity list and the weighted association relation between the entities.
In step S30, first, determining entity nodes of a knowledge graph according to the industry chain development entities in the industry chain development entity list; then determining the relationship side of the knowledge graph and the attribute of the relationship side according to the weighted association relationship among the entities; and finally, constructing an industrial chain knowledge graph according to the entity nodes, the relation edges and the attributes of the relation edges.
S40, constructing an industrial chain key element importance evaluation model through a random walk strategy according to the industrial chain knowledge graph.
In step S40, the industrial chain key element importance assessment model, which is used to calculate the importance of the key element in the industrial chain knowledge graph, may be expressed as:
Figure SMS_41
，
in the above-mentioned method, the step of,
Figure SMS_44
is any entity in the industrial chain knowledge graph;
Figure SMS_46
For entity->
Figure SMS_50
Is a set of inbound entities;
Figure SMS_45
Is an entity
Figure SMS_49
Is a set of outbound entities;
Figure SMS_53
Is any entity in the entity set of the degree of importation;
Figure SMS_56
Is any entity in the outbound entity set;
Figure SMS_42
For entity->
Figure SMS_48
And entity->
Figure SMS_52
Is a weighted association relationship of (1);
Figure SMS_55
for entity->
Figure SMS_43
And entity->
Figure SMS_47
Is a weighted association relationship of (1);
Figure SMS_51
Is an experience coefficient;
Figure SMS_54
An operator is evaluated for key element importance.
Alternatively, empirical coefficients
Figure SMS_57
Set to 0.85.
S50, evaluating the importance of the key elements in the industrial chain knowledge graph through the industrial chain key element importance evaluation model, and acquiring the development quality score of the industrial chain to be evaluated by combining the coverage condition of the key elements of the industrial chain to be evaluated.
In step S50, the importance of all entities in the industrial chain knowledge graph is initialized and set according to the coverage condition of the key elements of the industrial chain to be evaluated, and then the importance of all entities is calculated in multiple iterations through the industrial chain key element importance evaluation model, and the development quality of the industrial chain to be evaluated is estimated according to the final iteration calculation result. The industrial chain to be evaluated refers to an industrial chain under the current development stage.
Preferably, the step S50 includes the steps of:
s501, initializing importance scores of all entities in the industrial chain knowledge graph according to the coverage condition of key elements of the industrial chain to be evaluated.
In step S501, in combination with the coverage situation of the key elements of the industrial chain to be evaluated, the importance score initialization for all covered entities in the industrial chain knowledge graph is set to 1, and the importance score initialization for all uncovered entities in the industrial chain knowledge graph is set to 0.
S502, performing multi-round iterative computation through the industrial chain key element importance evaluation model.
Preferably, the step S502 may include the steps of:
s5021, initializing and setting the maximum iteration times and a preset convergence value, and enabling the current iteration times to be initial values;
s5022, calculating importance scores of all entities in the industrial chain knowledge graph after current round iteration through the industrial chain key element importance evaluation model;
s5023, judging whether the iteration stop condition is met by judging whether the current iteration times are the maximum iteration times or whether the absolute value of the difference between the importance scores of all entities of the current iteration calculation and the importance scores of all entities of the last iteration calculation is smaller than or equal to a preset convergence value;
s5024, if yes, stopping iteration; otherwise, after the current iteration number is added by one, the step S5022 is returned.
I.e. initializing the settings mostLarge number of iterations
Figure SMS_60
And a preset convergence value +.>
Figure SMS_62
Afterwards, the importance scores of all entities in the industry chain knowledge graph initially set in step S501 are +.>
Figure SMS_65
As the input of the first iteration, calculating the importance scores of all entities after the first iteration through the industrial chain key element importance evaluation model, and then detecting the importance scores of all entities after the first iteration +.>
Figure SMS_59
Importance score for all entities with initialization settings +.>
Figure SMS_63
Absolute value of difference between +.>
Figure SMS_66
Whether or not is less than or equal to a preset convergence value
Figure SMS_67
If->
Figure SMS_58
Then enter the second round of iterative computation until the current iterative times are detected>
Figure SMS_61
Or->
Figure SMS_64
And stopping iteration and outputting importance scores of all entities in the last round of iterative computation.
And S503, after iteration is stopped, carrying out normalization and superposition processing on importance scores of all entities in the last round of iterative computation to obtain a development quality score of the industrial chain to be evaluated.
In step S503, after the iteration is stopped, obtaining importance scores of all the converged entities, normalizing the importance scores of all the converged entities to obtain normalized importance scores of each entity, and overlapping the normalized importance scores of all the entities to obtain a development quality score of the industry chain to be priced
Figure SMS_68
。
It should be noted that, in other embodiments, after the iteration is stopped, normalization and weight superposition processing may be performed on importance scores of all entities in the last iteration calculation to obtain a development quality score of the industry chain to be priced
Figure SMS_69
. Taking an industrial chain as an real estate industrial chain as an example, the industrial chain development quality evaluation method based on the knowledge graph can comprise the following steps:
firstly, acquiring articles related to real estate industry chain development, extracting real estate industry chain development entities, and extracting association relations among the real estate industry chain development entities through a Markov chain model; and then weighting the association relation between the financial industry chain development entities through a TF-IDF algorithm to obtain a weighted association relation, combining the real estate industry chain development entities, constructing a real estate industry chain knowledge graph through a triplet representation method, referring to an example graph of the real estate industry chain knowledge graph shown in FIG. 3, wherein the real estate industry chain knowledge graph entities are related industries and related enterprises, and the relationship edges are quantized weighted association relation.
Further, based on the real estate industry chain knowledge graph, a real estate industry chain key element importance evaluation model is built by combining a random walk strategy, importance scores of all key elements in the real estate industry chain knowledge graph are calculated, and real estate industry chain development quality is quantized by combining key element coverage conditions of the real estate industry chain in the current development stage.
In summary, the industrial chain development quality evaluation method based on the knowledge graph provided by the embodiment of the invention has the following beneficial effects:
1) According to the embodiment of the invention, the industrial chain knowledge graph is constructed according to the weighted association relationship between the industrial chain development entity and the entity, the knowledge graph can be automatically constructed, the construction efficiency of the knowledge graph is improved, and meanwhile, accurate and error-free data support can be provided for the follow-up knowledge graph reasoning;
2) According to the embodiment of the invention, the importance of the key elements in the knowledge graph is calculated through the industrial chain key element importance evaluation model, and the development quality of the industrial chain to be evaluated is quantified by combining the key element coverage condition of the industrial chain to be evaluated, so that the development quality of the industrial chain can be effectively and accurately evaluated, and the support of the high-quality development of the industrial chain to make macroscopic decision is facilitated.
In an alternative embodiment, as shown in fig. 4, the method for evaluating the quality of industrial chain development based on a knowledge graph further includes the following steps:
s60, after the importance initial scores are given to all entities in the industrial chain knowledge graph, performing iterative computation for a plurality of times through the industrial chain key element importance evaluation model, and obtaining the development quality scores when the industrial chain key elements are fully covered according to the iterative computation result.
In step S60, the importance initial score is set to 1.
That is, the importance score of all entities in the industry chain knowledge graph is initialized to 1, i.e
Figure SMS_70
Then, carrying out multiple iterative computation through an industrial chain key element importance evaluation model, stopping iteration when detecting that the current iteration number reaches the maximum iteration number or detecting that the iteration convergence condition is met, obtaining importance scores of all entities in the last iterative computation, and carrying out normalization and superposition processing to obtain a development quality score + & gt when the industrial chain key element is fully covered>
Figure SMS_71
。
The step S60 may be performed simultaneously with the step S50, or may be performed after the step S50. The flowchart of the industrial chain development quality evaluation method based on the knowledge graph shown in fig. 4 is only one preferred embodiment.
And S70, obtaining the development gap of the industrial chain to be evaluated by calculating the difference between the development quality score of the industrial chain to be evaluated and the development quality score of the industrial chain when the key elements of the industrial chain are fully covered.
In step S70, a development quality score of the industrial chain to be evaluated is calculated
Figure SMS_72
Development quality score +.>
Figure SMS_73
Difference between->
Figure SMS_74
Thereby quantifying the development gap of the industrial chain to be evaluated. Further, according to the development gap of the industrial chain to be evaluated, an industrial chain development decision scheme is obtained based on an expert experience reasoning method, so that a macroscopic decision of development quality evaluation development of the industrial chain is supported.
It can be appreciated that according to the industrial chain development quality evaluation method based on the knowledge graph provided by the embodiment of the invention, the development gap of the industrial chain is quantified by calculating the difference between the development quality score of the industrial chain to be evaluated and the development quality score of the industrial chain when the key elements of the industrial chain are fully covered, so that the development quality of the industrial chain can be evaluated in a multi-dimensional manner, and a richer data support is provided for the macro decision of the high-quality development of the industrial chain.
Example 2
As shown in fig. 5, a schematic structural diagram of an industrial chain development quality evaluation device based on a knowledge graph according to an embodiment of the present invention includes:
the entity and relationship processing module 110 is configured to obtain an industry chain development entity list, and obtain an association relationship between every two entities in the industry chain development entity list;
the relationship weighting module 120 is configured to weight the association relationship between the two entities to obtain a weighted association relationship between the entities;
the industry chain knowledge graph construction module 130 is configured to construct an industry chain knowledge graph according to the industry chain development entity list and the weighted association relationship between the entities;
the importance evaluation model construction module 140 is configured to construct an importance evaluation model of the industrial chain key element through a random walk strategy according to the industrial chain knowledge graph;
the industrial chain development quality evaluation module 150 is configured to evaluate importance of key elements in the industrial chain knowledge graph through the industrial chain key element importance evaluation model, and obtain a development quality score of the industrial chain to be evaluated in combination with a coverage situation of the key elements of the industrial chain to be evaluated.
In some alternative embodiments, as shown in fig. 6, the knowledge-graph-based industrial chain development quality evaluation device further includes:
the full coverage development quality evaluation module 160 is configured to perform multiple iterative computations through the industrial chain key element importance evaluation model after assigning an importance initial score to all entities in the industrial chain knowledge graph, and obtain a development quality score when the industrial chain key element is fully covered according to an iterative computation result;
the development gap quantifying module 170 is configured to obtain a development gap of the industrial chain to be evaluated by calculating a difference between the development quality score of the industrial chain to be evaluated and the development quality score when the key elements of the industrial chain are fully covered.
In some alternative embodiments, the entity and relationship processing module 110 includes:
the entity extraction sub-module is used for acquiring an industrial chain development related article, extracting an industrial chain development entity from the industrial chain development related article and constructing an industrial chain development entity list;
a co-occurrence entity processing sub-module, configured to determine two entities that co-occur in the article related to industrial chain development, calculate a co-occurrence probability of the two entities that co-occur and an occurrence probability of each entity, and calculate a conditional transition probability of the two entities that co-occur;
the non-common entity processing submodule is used for determining two non-common entities in the industrial chain development related article, and calculating the conditional transition probability of the two non-common entities according to a first-order Markov chain model and the conditional transition probability of the two common entities; calculating the co-occurrence probability of the two non-co-occurrence entities according to the conditional transition probability of the two non-co-occurrence entities and the occurrence probability of each entity;
and the entity association relation processing sub-module is used for obtaining association relation between every two entities in the industrial chain development entity list according to co-occurrence probability of two co-occurrence and non-co-occurrence entities.
In some alternative embodiments, the relationship weighting module 120 includes:
the weight coefficient acquisition sub-module is used for acquiring weight coefficients of every two entities in the industry chain development related literature through a TF-IDF algorithm;
and the weighting sub-module is used for weighting the association relation between the two entities according to the weight coefficient between the two entities to obtain the weighted association relation between the entities.
In some alternative embodiments, the industry chain development quality assessment module 150 includes:
the importance initializing sub-module is used for initializing and setting importance scores of all entities in the industrial chain knowledge graph according to the coverage condition of key elements of the industrial chain to be evaluated;
the iterative computation sub-module is used for carrying out multi-round iterative computation through the industrial chain key element importance evaluation model;
and the development quality evaluation sub-module is used for obtaining the development quality score of the industrial chain to be evaluated by carrying out normalization and superposition processing on the importance scores of all entities calculated in the last round of iteration after the iteration is stopped.
In some alternative embodiments, the iterative computation submodule includes:
the parameter initialization unit is used for initializing and setting the maximum iteration times and a preset convergence value, and enabling the current iteration times to be initial values;
the iteration calculation unit is used for calculating importance scores of all entities in the industrial chain knowledge graph after the current round of iteration through the industrial chain key element importance evaluation model;
the stopping condition detection unit is used for judging whether the iteration stopping condition is met or not by judging whether the current iteration times are the maximum iteration times or whether the absolute value of the difference between the importance scores of all entities of the current iteration calculation and the importance scores of all entities of the last iteration calculation is smaller than or equal to a preset convergence value or not;
the iteration output processing unit is used for stopping iteration if yes; otherwise, after the current iteration times are added by one, the iteration calculation unit is returned.
It can be understood that the industrial chain development quality evaluation device based on the knowledge graph provided in this embodiment is used to implement the industrial chain development quality evaluation method based on the knowledge graph in the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which is not described herein.
It should be noted that in the description of the present invention, reference to the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present invention, unless otherwise indicated, the meaning of "plurality" means at least two.
The present invention is not limited to the above embodiments, but is capable of modification and variation in detail, and other modifications and variations can be made by those skilled in the art without departing from the scope of the present invention.

Claims (10)

1. The industrial chain development quality evaluation method based on the knowledge graph is characterized by comprising the following steps of:
acquiring an industrial chain development entity list, and acquiring association relations between every two entities in the industrial chain development entity list;
weighting the association relation between every two entities to obtain a weighted association relation between the entities;
constructing an industry chain knowledge graph according to the industry chain development entity list and the weighted association relation between the entities;
according to the industrial chain knowledge graph, an industrial chain key element importance evaluation model is constructed through a random walk strategy;
and evaluating the importance of the key elements in the industrial chain knowledge graph through the industrial chain key element importance evaluation model, and acquiring the development quality score of the industrial chain to be evaluated by combining the coverage condition of the key elements of the industrial chain to be evaluated.
2. The knowledge-graph-based industrial chain development quality evaluation method according to claim 1, further comprising:
after the importance initial scores are given to all entities in the industrial chain knowledge graph, performing iterative computation for a plurality of times through the industrial chain key element importance evaluation model, and obtaining development quality scores when the industrial chain key elements are fully covered according to iteration computation results;
and obtaining the development gap of the industrial chain to be evaluated by calculating the difference between the development quality score of the industrial chain to be evaluated and the development quality score of the industrial chain when the key elements of the industrial chain are fully covered.
3. The knowledge-graph-based industrial chain development quality evaluation method according to claim 1, wherein the obtaining an industrial chain development entity list and obtaining an association relationship between every two entities in the industrial chain development entity list comprises:
acquiring an industrial chain development related article, extracting an industrial chain development entity from the industrial chain development related article, and constructing an industrial chain development entity list;
determining two entities which co-occur in the industrial chain development related article, calculating the co-occurrence probability of the two entities which co-occur and the occurrence probability of each entity, and calculating the conditional transition probability of the two entities which co-occur;
determining two non-co-occurring entities in the industrial chain development related article, and calculating the conditional transition probability of the two non-co-occurring entities according to a first-order Markov chain model and the conditional transition probability of the two co-occurring entities;
calculating the co-occurrence probability of the two non-co-occurrence entities according to the conditional transition probability of the two non-co-occurrence entities and the occurrence probability of each entity;
and obtaining the association relation between every two entities in the industrial chain development entity list according to the co-occurrence probability of the two entities which co-occur and do not co-occur.
4. The knowledge-graph-based industrial chain development quality evaluation method of claim 3, wherein weighting the association relationship between the two entities to obtain a weighted association relationship between the entities comprises:
acquiring weight coefficients of every two entities in the industry chain development related literature through a TF-IDF algorithm;
and weighting the association relation between every two entities according to the weight coefficient between every two entities to obtain the weighted association relation between the entities.
5. The knowledge-graph-based industrial chain development quality evaluation method according to claim 1, wherein the industrial chain key element importance evaluation model is as follows:
Figure QLYQS_1
，/>
in the method, in the process of the invention,
Figure QLYQS_4
is any entity in the industrial chain knowledge graph;
Figure QLYQS_6
For entity->
Figure QLYQS_10
Is a set of inbound entities;
Figure QLYQS_5
For entity->
Figure QLYQS_7
Is a set of outbound entities;
Figure QLYQS_11
Is any entity in the entity set of the degree of importation;
Figure QLYQS_14
Is any entity in the outbound entity set;
Figure QLYQS_2
For entity->
Figure QLYQS_8
And entity->
Figure QLYQS_12
Is a weighted association relationship of (1);
Figure QLYQS_15
for entity->
Figure QLYQS_3
And entity->
Figure QLYQS_9
Is a weighted association relationship of (1);
Figure QLYQS_13
Is an experience coefficient;
Figure QLYQS_16
An operator is evaluated for key element importance.
6. The knowledge-based industrial chain development quality evaluation method according to claim 1, wherein the evaluating the importance of the key elements in the industrial chain knowledge-based by the industrial chain key element importance evaluation model and combining the coverage of the key elements of the industrial chain to be evaluated to obtain the development quality score of the industrial chain to be evaluated comprises:
according to the coverage condition of key elements of an industrial chain to be evaluated, initializing importance scores of all entities in the industrial chain knowledge graph;
performing multi-round iterative computation through the industrial chain key element importance evaluation model;
and after the iteration is stopped, carrying out normalization and superposition processing on importance scores of all entities in the last round of iterative computation to obtain the development quality score of the industrial chain to be evaluated.
7. The knowledge-graph-based industrial chain development quality evaluation method according to claim 6, wherein the performing multiple rounds of iterative computation by the industrial chain key element importance evaluation model comprises:
initializing and setting the maximum iteration times and a preset convergence value, and enabling the current iteration times to be initial values;
calculating importance scores of all entities in the industrial chain knowledge graph after current round iteration through the industrial chain key element importance evaluation model;
judging whether the iteration stop condition is met by judging whether the current iteration times are the maximum iteration times or whether the absolute value of the difference between the importance scores of all entities of the current iteration calculation and the importance scores of all entities of the last iteration calculation is smaller than or equal to a preset convergence value;
if yes, iteration is stopped; otherwise, after adding one operation to the current iteration number, returning to the step: and calculating importance scores of all entities in the industrial chain knowledge graph after the current round of iteration through an industrial chain key element importance evaluation model.
8. An industrial chain development quality evaluation device based on a knowledge graph is characterized by comprising:
the entity and relation processing module is used for acquiring an industrial chain development entity list and acquiring association relation between every two entities in the industrial chain development entity list;
the relation weighting module is used for weighting the association relation between every two entities to obtain a weighted association relation between the entities;
the industrial chain knowledge graph construction module is used for constructing an industrial chain knowledge graph according to the industrial chain development entity list and the weighted association relation between the entities;
the importance evaluation model construction module is used for constructing an importance evaluation model of the key elements of the industrial chain through a random walk strategy according to the industrial chain knowledge graph;
the industrial chain development quality evaluation module is used for evaluating the importance of the key elements in the industrial chain knowledge graph through the industrial chain key element importance evaluation model and acquiring the development quality score of the industrial chain to be evaluated by combining the key element coverage condition of the industrial chain to be evaluated.
9. The knowledge-based industrial chain development quality evaluation device according to claim 8, further comprising:
the full coverage development quality evaluation module is used for carrying out multiple iterative computation through the industrial chain key element importance evaluation model after giving the importance initial score to all entities in the industrial chain knowledge graph, and obtaining the development quality score when the industrial chain key elements are fully covered according to the iterative computation result;
and the development gap quantifying module is used for obtaining the development gap of the industrial chain to be evaluated by calculating the difference between the development quality score of the industrial chain to be evaluated and the development quality score of the industrial chain when the key elements of the industrial chain are fully covered.
10. The knowledge-based industrial chain development quality evaluation device of claim 8, wherein the development quality evaluation module comprises:
the initialization sub-module is used for initializing and setting importance scores of all entities in the industrial chain knowledge graph according to the coverage condition of key elements of the industrial chain to be evaluated;
the iterative computation sub-module is used for carrying out multi-round iterative computation through the industrial chain key element importance evaluation model;
and the development quality evaluation sub-module is used for carrying out normalization and superposition processing on the importance scores of all entities in the last round of iterative computation after iteration is stopped, so as to obtain the development quality score of the industrial chain to be evaluated.
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