CN116415564A - Functional point amplification method and system based on knowledge graph - Google Patents

Functional point amplification method and system based on knowledge graph Download PDF

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CN116415564A
CN116415564A CN202310685252.1A CN202310685252A CN116415564A CN 116415564 A CN116415564 A CN 116415564A CN 202310685252 A CN202310685252 A CN 202310685252A CN 116415564 A CN116415564 A CN 116415564A
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樊志强
胡贝贝
刘禹
夏晓凯
杨晓
牛婵
孙悦
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Beihang University
CETC Information Science Research Institute
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Abstract

The invention belongs to the technical field of software analysis, and provides a functional point amplification method and a system based on a knowledge graph, wherein the method is used for forming functional point triples to construct the functional point knowledge graph by extracting functional point entities and knowledge relations from the existing functional point analysis text; and when receiving the text to be processed, identifying the contained functional points, determining the starting node for performing traversal according to the identified functional points so as to determine whether to perform traversal of the relation path between the directed entity nodes in the functional point knowledge graph, establishing a knowledge graph node queue based on a BFS searching algorithm in the process of traversing the functional point knowledge graph, updating each entity node in the knowledge graph node queue, and further performing functional point amplification according to the established knowledge graph node queue. The invention realizes a more effective automatic amplification process of the function points and effectively avoids the problem of missing the function points.

Description

Functional point amplification method and system based on knowledge graph
Technical Field
The invention relates to the technical field of software analysis, in particular to a functional point amplification method and system based on a knowledge graph.
Background
The function point analysis method is one method for measuring the cost of software. The function points generally refer to five kinds of function points in the function point analysis method. Currently, it is usually extracted manually by an expert. However, in recent years, some automatic extraction technologies of function points are presented, and the function points can be automatically extracted from the requirement analysis text. However, there are certain cases of missing functional points for both expert manual extraction and existing automatic extraction methods for functional points implicitly described in the demand analysis text. In addition, the existing automatic extraction method also has the problem that functional points are lost due to limitation of model capacity. In addition, there is still a great room for improvement in how to more effectively promote the automatic extraction of function points and the expansion of function points.
Therefore, it is necessary to provide a functional point amplification method based on a knowledge graph to solve the above problems.
Disclosure of Invention
The invention aims to provide a functional point amplification method and a system based on a knowledge graph, which are used for solving the technical problems of functional point deficiency in the existing manual extraction method and the existing automatic extraction method in the prior art, and how to more effectively improve the technical problems of automatic functional point extraction, functional point expansion and the like.
The first aspect of the invention provides a functional point amplification method based on a knowledge graph, which comprises the following steps: extracting functional point entities from the existing functional point analysis text according to the functional point category and scene parameters, wherein the functional point entities comprise multi-class entities containing verbs and/or nouns; extracting knowledge relation from the existing function point analysis text to form a function point triplet to construct a function point knowledge graph, wherein the function point triplet comprises entity nodes corresponding to the function point entities and unidirectional relations or bidirectional relations between adjacent entity nodes; identifying the contained function points when receiving the text to be processed; determining a starting node for performing traversal according to the identified functional points to perform the step of traversing the relation paths among the directed entity nodes in the functional point knowledge graph, searching all reachable entity nodes based on a BFS searching algorithm in the process of traversing the functional point knowledge graph to establish a knowledge graph node queue, wherein the method specifically comprises the following steps of: updating entity nodes in the knowledge graph node queue; and performing functional point amplification according to the established knowledge graph node queue.
According to an alternative embodiment, determining a starting node for performing traversal according to the identified functional points, so as to perform traversal of a relationship path between directed entity nodes in the functional point knowledge graph, including: according to the identified function points, the original entity or the synonymous entity is queried from the name dictionary to determine the original entity corresponding to the identified function points, and the step of traversing the function point knowledge graph is started to be executed by further taking the determined original entity as a starting node until all relevant relation paths are traversed.
According to an alternative embodiment, the updating the entity node in the knowledge-graph node queue includes: and determining scene parameters in real time, and updating the credibility threshold corresponding to each entity node in the functional point knowledge graph to be used for updating the entity nodes in the knowledge graph node queue.
According to an alternative embodiment, the establishing a knowledge-graph node queue includes: starting from the initial node, the adjacent entity node pointed by the initial node and the adjacent entity node pointed by the adjacent entity node until all the access of the reachable entity nodes are completed, judging whether each accessed entity node can be added to a knowledge graph node queue one by one.
According to an alternative embodiment, the following expression is used to calculate the cumulative weight value of each accessed entity node, and the calculated cumulative weight value is compared with the corresponding trusted threshold to determine whether to add each accessed entity node to the knowledge-graph node queue, so as to establish the knowledge-graph node queue;
Figure SMS_1
wherein ,
Figure SMS_2
indicating which entity node N n Is added to the accumulated weight value of the (a); n represents the number of nodes passing by;
Figure SMS_3
representing slave entity node N 1 Start to reach the entity node N n The weight product of the n-1 paths experienced; w (W) 1 Representing slave entity node N 1 Starting the experienced weight of the 1 st edge; w (W) 2 Representing slave entity node N 1 Starting the experienced weight of the 2 nd edge; w (W) 3 Representing slave entity node N 1 Starting the experienced weight of the 3 rd edge; w (W) n-1 Representing slave entity node N 1 The weight of the n-1 th edge experienced, i.e. the weight of the last edge, starts.
According to an alternative embodiment, in the process of traversing the functional point knowledge graph, calculating the accumulated weight value of each accessed node, and determining the maximum relation path with the maximum accumulated weight value so as to determine all entity nodes on the maximum relation path; adding entity nodes which are not in the knowledge graph node queue and are on the maximum relation path into the knowledge graph node queue so as to update related entity nodes in the knowledge graph node queue in real time.
According to an alternative embodiment, a new function point set is obtained according to the updated knowledge graph node queue, and the function point set is output.
According to an alternative embodiment, before extracting the functional point entity from the existing functional point analysis text, using a pre-established automatic extraction model to extract the functional point from the existing functional point analysis text, wherein the automatic extraction model is constructed based on a Bert-BiLSTM-CRF algorithm, and the model construction specifically comprises optimizing model parameters in a plurality of model verification processes and optimizing model parameters in a model test process.
According to an optional embodiment, the extracting knowledge relation from the existing function point analysis text to form a function point triplet to construct a function point knowledge graph includes: the method comprises the steps of extracting the knowledge relation of the internal relation between function points representing different kinds and different operations in the existing function point analysis text, and obtaining the following various relations for representing unidirectional or bidirectional edges between entity nodes: dependency, inheritance, aggregation, action, generalization, synonym, trigger, parallelism, interaction, coexistence.
The second aspect of the present invention provides a functional point amplification system based on a knowledge graph, and the functional point amplification method based on the knowledge graph according to the first aspect of the present invention includes: the entity extraction module is used for extracting functional point entities from the existing functional point analysis text according to the functional point category and the scene parameter, wherein the functional point entities comprise multi-class entities containing verbs and/or nouns; the relation extraction module is used for extracting knowledge relation from the existing function point analysis text to form a function point triplet to construct a function point knowledge graph, wherein the function point triplet comprises entity nodes corresponding to the function point entities and one-way relations or two-way relations between adjacent entity nodes; the receiving processing module is used for identifying the contained function points when receiving the text to be processed; the construction module determines a starting node for performing traversal according to the identified functional points so as to perform traversal of a relation path among the directed entity nodes in the functional point knowledge graph, and searches all reachable entity nodes based on a BFS search algorithm in the process of traversing the functional point knowledge graph so as to establish a knowledge graph node queue, wherein the construction module specifically comprises the following steps: updating entity nodes in the knowledge graph node queue; and the amplification module is used for carrying out functional point amplification according to the established knowledge graph node queue.
A third aspect of the present invention provides an electronic apparatus, comprising: one or more processors; a storage means for storing one or more programs; the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of the first aspect of the present invention.
A fourth aspect of the invention provides a computer readable medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements the method according to the first aspect of the invention.
The embodiment of the invention has the following advantages:
compared with the prior art, the method and the device have the advantages that the functional point entity extraction and the knowledge relation extraction are carried out on the existing functional point analysis text, the functional point triples are formed to construct the functional point knowledge graph, and the functional point knowledge graph which is more accurate and comprises the functional point triples can be obtained; when a text to be processed is received, the contained functional points are identified, the starting node for performing traversal is determined according to the identified functional points, so that the step of traversing the relation path between the directed entity nodes in the functional point knowledge graph is performed, a knowledge graph node queue is established in the process of traversing the functional point knowledge graph, further, the functional point amplification is performed according to the established knowledge graph node queue, the automatic functional point amplification process can be realized more quickly and more effectively, and the problem of functional point missing can be effectively avoided.
In addition, based on a BFS searching algorithm, all reachable entity nodes are searched, and the entity nodes in the knowledge-graph node queue are updated by determining updating parameters in real time, so that the knowledge-graph node queue with higher reliability can be obtained, and the function point expansion method can be further optimized.
In addition, an automatic extraction model is built based on the Bert-BiLSTM-CRF algorithm, and the function points can be extracted more quickly and more effectively by using the automatic extraction model to automatically extract the function points of the existing function point analysis text, so that the function point expansion method can be further optimized.
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FIG. 1 is a flow chart of steps of an example of a knowledge-based functional point amplification method of the present invention;
FIG. 2 is a schematic diagram of an example of a knowledge-based functional point amplification method to which the present invention is applied;
FIG. 3 is a flowchart of an example of a step of performing a traversal of a relationship path between directed entity nodes in a knowledge-graph based functional point augmentation method according to the present invention;
FIG. 4 is a flow chart of another example of performing a step of traversing the functional point knowledge graph in a knowledge-based functional point augmentation method according to the present invention;
FIG. 5 is a block diagram of a knowledge-based functional point amplification system of the present invention;
FIG. 6 is a schematic structural diagram of an embodiment of an electronic device according to the present invention;
fig. 7 is a schematic diagram of an embodiment of a computer readable medium according to the present invention.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.
In view of the above problems, the present invention provides a functional point amplification method based on a knowledge graph, which performs functional point entity extraction and knowledge relation extraction on an existing functional point analysis text to form functional point triples so as to construct a functional point knowledge graph, so that a more accurate functional point knowledge graph containing the functional point triples can be obtained; when a text to be processed is received, the contained functional points are identified, the starting node for performing traversal is determined according to the identified functional points, so that the step of traversing the relation path among the directed entity nodes in the functional point knowledge graph is performed, in the process of traversing the functional point knowledge graph, a knowledge graph node queue is established based on a BFS searching algorithm, each entity node in the knowledge graph node queue is updated, further, the functional point amplification is performed according to the established knowledge graph node queue, the automatic amplification process of the functional points can be realized more quickly and effectively, and the problem of functional point missing can be effectively avoided.
Fig. 1 is a flowchart illustrating steps of an exemplary knowledge-based functional point amplification method of the present invention.
The following describes the present invention in detail with reference to fig. 1 to 4.
First, in step S101, extracting functional point entities from the existing functional point analysis text according to the functional point category and the scene parameter, where the functional point entities include multi-class entities including verbs and/or nouns.
Specifically, the extracting of the functional point entity according to the functional point category includes extracting a first type entity, a second type entity, a third type entity, a fourth type entity and a fifth type entity, where the entities are all functional point entities.
For example, a hash tree is used to identify each entity in the function point text, and a first type of entity related to the internal logic file in the data function is extracted, wherein the first type of entity is a noun related to the internal logic file. In particular, the tables and files in the database shown in table 1 below are referred to as "order table", "user table", "commodity table", etc.
Then, a second kind of entity related to the external logic file in the data function is extracted, wherein the second kind of entity is noun related to the external interface file. Such as "pay bill".
TABLE 1
Figure SMS_4
Table 1 shows the classification of function points, names, and meanings indicated by the respective kinds of function points.
Next, a third class of entities related to the external input is extracted, the third class of entities being a combination of verbs and nouns related to the external input (i.e., related to the function point operation), and class label tags of the entities related to the external input are characterized using EI. Such as "add order", "modify merchandise", "delete user".
In addition, a fourth class of entities related to the external query is extracted, the fourth class of entities being a combination of verbs and nouns related to the external query (i.e., related to the function point operation), class label tags of the entities related to the external query being characterized using EQ. For example, "query orders".
In addition, a fifth class of entities related to the external output is extracted, which is a combination of verbs and nouns related to the external output (i.e., related to the function point operation), and class label tags of the entities related to the external output are characterized using EO. Such as "generate order number".
Further, extracting functional point entities from the existing functional point analysis text according to scene parameters.
In particular, scene parameters are determined according to the applied domain. Taking e-commerce fields as examples, for example, scene parameters related to orders, scene parameters related to commodities, scene parameters related to users. More specifically, for example, order form, order number, payment, inventory, refund, merchandise form, user form, and the like, see table 2 for details.
TABLE 2
Figure SMS_5
Table 2 shows an example of correspondence of scene parameters to respective entities.
In an alternative embodiment, the entity recognition algorithm is used for performing "entity disambiguation" (solving the problems of multi-word meaning and the like) on the words corresponding to the extracted various entities, so that synonyms with the same or similar semantics as the words can be recognized, and the words and the synonyms thereof are mapped to the same entity.
It should be noted that, due to the difference of the text expressions, different vocabulary expressions may have the same semantics, such as "add" and "increase", and then "add" and "increase" represent the same entity. For synonyms with the same semantics, see in particular table 3.
TABLE 3 Table 3
Figure SMS_6
Table 3 is an example showing the relationship between each entity (each original entity) and its synonymous entities.
Optionally, for the function point categories, creating a name dictionary (e.g., expressed using synonym, where keys in the name dictionary represent synonymous entities and values of the name dictionary represent original entities) corresponding to each function point category is further included, see table 3 above.
Specifically, the name dictionary includes two columns of key columns and value columns, and has a corresponding entity relationship, wherein the key columns are synonymous entities, the value columns are original entities (original entities corresponding to synonymous entities belonging to the same row in the key columns, see table 3 for details).
In a specific embodiment, the original entity is queried according to the synonymous entity in the name dictionary, and then the original entity in the functional point knowledge graph constructed later is amplified.
It should be noted that the foregoing is merely illustrative of the present invention and is not to be construed as limiting thereof.
Next, in step S102, knowledge relation extraction is performed on the existing function point analysis text, and a function point triplet is formed to construct a function point knowledge graph, where the function point triplet includes a one-way relation or a two-way relation between an entity node and an adjacent entity node corresponding to the function point entity.
Specifically, knowledge relationships are extracted, for example, by using a regular matching method, all the extracted knowledge relationships are subjected to statistical analysis, the number of relationship categories in a specified time period is calculated, and the relationship categories ranked a certain number before, for example, ten digits are taken.
For example, according to scene parameters, extraction rules are configured, and according to the extraction rules, knowledge relation extraction is performed on the intrinsic relations between function points representing different types and different operations and between various operations and different function points in the existing function point analysis text, so that the following various relations are obtained to be used for representing the side relations between two adjacent entity nodes, and the side relations are unidirectional or bidirectional. That is, different edge relationships are represented using relationship categories.
For example, scene parameters are shopping, orders, merchandise, payments, etc. Extraction rules are, for example, extraction of intrinsic relations between various operations and different function points, etc. In a shopping scenario, the implementation of the "Add order" function necessarily relies on the implementation of two functional points, "Add Commodity" and "Payment". Thus "dependencies" can be extracted. While "add merchandise" and "pay" may be combined to form an "add order" function, so that "aggregate relationships" may be extracted.
The specific relationship category comprises a dependency relationship, an inheritance relationship, an aggregation relationship, an action relationship, a generalization relationship, a synonymous relationship, a triggering relationship, a parallel relationship, an interaction relationship and a coexistence relationship, and the relationships are respectively represented by alpha, mu, phi, delta, epsilon, theta, omega, zeta, eta and lambda. See in particular table 4 below.
TABLE 4 Table 4
Figure SMS_7
Table 4 shows the names of the various relationships (i.e., relationship categories), the meanings of the various relationships, and the representation symbols.
Next, according to the various entities extracted in step S102, the extracted entities and the knowledge relationships (including the unidirectional relationships and the bidirectional relationships) between the entities, a function point triplet (entity, relationship, entity, see table 5 for details) is formed, so as to construct a function point knowledge graph.
TABLE 5
Figure SMS_8
Table 5 is an example of triples showing different edge relationships.
Specifically, the relationship graph in the constructed functional point knowledge graph is composed of entity nodes and directed relationship edges, and specifically comprises functional point entity nodes (also called entity nodes for short), edge relations and functional point entity nodes (also called entity nodes for short).
It should be noted that the functional point knowledge graph is an instantiation representation of functional points, and represents the inherent links between different functional point instances of a specific type of system (systems corresponding to different application fields).
Next, in step S103, upon receiving the text to be processed, the contained function points are identified.
In one embodiment, the function points contained in the text to be processed are identified, all original entities are queried from the name dictionary according to the identified function points, and whether the identified function points are the original entities is determined.
When the identified function point is completely matched with the original entity in the name dictionary, or when the identified function point is completely matched with the synonymous entity in the name dictionary, the original entity corresponding to the identified function point is obtained.
And taking the entity node corresponding to the obtained original entity as an initial node for executing the traversing function point knowledge graph.
It should be noted that the foregoing is merely illustrative of the present invention and is not to be construed as limiting thereof.
Next, in step S104, determining a starting node for performing traversal according to the identified function points, so as to perform a step of traversing a relationship path between the directed entity nodes in the function point knowledge graph, and searching all reachable entity nodes (i.e. the function point entity nodes) based on a BFS search algorithm in the process of traversing the function point knowledge graph, so as to establish a knowledge graph node queue, where establishing the knowledge graph node queue includes: and updating the entity nodes in the knowledge graph node queue.
Determining a starting node for performing traversal according to the identified functional points so as to perform the step of traversing the relation paths among the directed entity nodes in the functional point knowledge graph, wherein the step specifically comprises the following steps.
Step S301: a starting node for performing the traversal is determined based on the identified function points.
Step S302: and repeatedly executing traversing the related relation paths from the starting point in the functional point knowledge graph until all the related relation paths are traversed.
Specifically, starting from the starting node, the adjacent entity node pointed by the starting node and the adjacent entity node pointed by the adjacent entity node until all the access of the reachable entity nodes is completed, judging whether each accessed entity node can be added to the knowledge graph node queue one by one so as to establish the knowledge graph node queue.
In order to further optimize the functional point amplification method of the present invention, all the functional points with high reliability can be obtained even in the case that the number of paths passed from one functional point to the other functional points (functional point entity nodes in the functional point knowledge graph) is large. Starting from the credibility and application scene aspect of each relation path passed by the function points obtained by starting the function point amplification, the function point amplification method is further optimized.
Fig. 2 is a schematic diagram of an example of a functional point amplification method based on a knowledge-graph to which the present invention is applied.
As shown in fig. 2, the text to be processed is exemplified as "add commodity".
When receiving the added commodity, determining whether the added commodity contains a function point, wherein the added commodity contains the function point added, matching the function point added with all original entities and synonymous entities in a name dictionary, and determining an original entity corresponding to the function point added, namely the original entity added. And starting traversing the functional point knowledge graph shown in fig. 2 from the starting point according to the entity node corresponding to the original entity 'adding' as the starting node.
In an example, based on a BFS search algorithm, the step of traversing a relationship path between directed entity nodes in the functional point knowledge graph is performed. As shown in fig. 4, the following steps are specifically included.
Step S401: based on the BFS search algorithm, all reachable entity nodes are searched.
For determining all reachable entity nodes, each entity node in the functional point knowledge graph corresponds to a trusted threshold, wherein the trusted threshold is used for determining whether the accessed entity node is a reachable entity node, the accumulated weight value of each accessed entity node is calculated by using the following expression (1), and the calculated accumulated weight value of each entity node is compared with the trusted threshold of each entity node to determine whether each accessed entity node is a reachable entity node so as to establish a knowledge graph node queue.
Figure SMS_9
wherein ,
Figure SMS_10
representing entity node N n Is added to the accumulated weight value of the (a); n represents the number of edge relationships, in this example n is 10; />
Figure SMS_11
Representing slave entity node N 1 Start to reach the entity node N n The weight product of the n-1 paths experienced; w (W) 1 Representing slave entity node N 1 Starting the experienced weight of the 1 st edge; w (W) 2 Representing slave entity node N 1 Starting the experienced weight of the 2 nd edge; w (W) 3 Representing slave entity node N 1 Start to go through 3 rd edgeWeighting; w (W) n-1 Representing slave entity node N 1 The weight of the n-1 th edge experienced, i.e. the weight of the last edge, starts.
It should be noted that, the BFS algorithm is adopted, and specifically, under the limitation of the accumulated weight value (i.e., the trusted threshold), all the reachable entity nodes are searched and can be used as new function points. The trusted threshold is obtained, for example, by statistical analysis of historical data for each specified area, or by expert settings, etc.
Step S402: and determining an updating parameter in real time for updating the entity nodes in the knowledge graph node queue.
The method comprises the steps of specifically calculating the accumulated weight value of each entity node in each relation path, comparing the accumulated weight value of each entity node with a preset threshold (i.e. a credible threshold), determining one relation path with the largest accumulated weight value (i.e. the largest relation path), specifically adding entity nodes with the calculated accumulated weight value larger than or equal to the preset threshold to a knowledge graph node queue, deleting entity nodes smaller than the preset threshold, adding entity nodes which are on the largest relation path and are not in the knowledge graph node queue to the knowledge graph node queue, and updating relevant entity nodes in the knowledge graph node queue in real time to further obtain the knowledge graph node queue comprising a plurality of entity nodes (i.e. comprising a function point set).
In the traversal process based on the BFS algorithm, a certain entity node may have multiple accesses, and when the node is currently accessed, if the accumulated weight value of the same entity node when the node is currently accessed is greater than the accumulated weight value when the node is accessed for the first time (or when the node is accessed last time), the accumulated weight value of the node is updated.
The cumulative weight value of each entity node is calculated using the expression (1) described above. For example, a weight value such as (0, 1) is given to the start node in each relation path, and the accumulated weight value of each entity node is the product of the weight values of the edge relations passing through all paths from the start node to each entity node.
It should be noted that, the cumulative weight value of a certain entity node represents the weight product (i.e., the cumulative weight value) of the relationship path traversed (accessed or searched) from the start node to the certain entity node (e.g., the entity node "modifies ILF"), and for the function point represented by the entity node (e.g., the entity node "modifies ILF"), the cumulative weight value represents the reliability degree of the function point, and the greater the cumulative weight value, the higher the reliability degree. When a certain entity node starts from the initial node and has two relation paths, two accumulated weight values are corresponding, and the relation path with the largest accumulated weight value is taken, namely the entity node on the largest relation path is taken, because the reliability of the functional point represented by the entity node on the largest relation path is high.
In an alternative embodiment, the scene parameters (i.e. the update parameters) are determined based on the text to be processed and the function points contained therein. Specifically identifying whether the text to be processed contains a scene identifier, and determining to update entity nodes in the knowledge graph node queue when determining that the text to be processed contains the scene identifier.
For example, according to the scene parameters (in particular, parameters related to electronic commerce, social media, game entertainment, etc., such as orders, user accounts, etc.), the credibility threshold of each entity node in the functional point knowledge graph is updated in real time, and values corresponding to the side relations corresponding to the scene parameters (in particular, scene identifications) (i.e., values corresponding to the side relations represented by α, μ, Φ, δ, ε, θ, ω, ζ, η, λ) are determined in real time, for example, by expert guidance, or determined according to the average value of historical data in a specified period of time, etc.). And then, judging whether the accessed related entity nodes are added to the knowledge graph node queue one by using the trust threshold value of each entity node updated in real time so as to establish the knowledge graph node queue.
It should be noted that, in other embodiments, the application scenario parameters and the number of all relationship paths related to the start node (i.e., the update parameters) are determined in real time. The foregoing is illustrative only and is not to be construed as limiting the invention.
In an alternative embodiment, before performing the step of traversing the relationship path between the directed entity nodes in the functional point knowledge graph, the triplet file corresponding to each triplet in the functional point knowledge graph is preprocessed to obtain all entity node sets (for example, using nodes to represent), and class labels corresponding to each entity class, and a name dictionary is constructed, specifically, see table 3.
In the example shown in fig. 2, for example, starting with "delete user", the following relationship paths are reachable to reach "user table": deleting user, inquiring user, user management, user table, deleting user, adding user, user management, user table, deleting user, inquiring user, modifying user, user management and user table.
By repeatedly executing the traversing of the related relation paths from the starting point in the functional point knowledge graph until all the related relation paths are traversed, the traversing step can be more effectively completed, and all the related relation paths can be obtained more quickly.
Specifically, starting from the starting node, the adjacent entity node pointed by the starting node and the adjacent entity node pointed by the adjacent entity node until all the access of the reachable entity nodes is completed, judging that the accessed related entity nodes are added to the knowledge graph node queue one by one, and establishing the knowledge graph node queue. For example, a knowledge graph node queue [ "delete user", "query user", "add user", "modify user", "user management", "user table" ] is obtained.
It should be noted that the foregoing is merely illustrative of the present invention and is not to be construed as limiting thereof.
Next, in step S105, functional point amplification is performed according to the established knowledge-graph node queue.
And obtaining a new function point set according to the established knowledge graph node queue, and outputting the new function point set.
In the above examples from "delete user" to "user table", a new set of function points [ "delete user", "query user", "add user", "modify user", "user management", "user table" ] may be sequentially obtained from the knowledge graph node queue.
Then, the obtained new function point set is output to complete the function point amplification.
In an alternative embodiment, the function point amplification is performed according to the updated knowledge-graph node queue.
In another example, the function points are extracted from the existing function point analysis text using a pre-established automatic extraction model prior to the function point entity extraction of the existing function point analysis text.
Based on the Bert-BiLSTM-CRF algorithm, an automatic extraction model is constructed, and the constructed automatic extraction model is used for automatically extracting the function points of the existing function point analysis text.
First, a demand analysis text, a software design text, and a system design text (for example, about 900 pieces) are acquired from public channels such as github, gitlab, a bloggery, and a chinese knowledge network, and a data set is obtained by acquiring function points known in the prior art.
Next, a set of function point labels is established, according to the set of function point labels, sample data (for example, 112 ten thousand-word requirement analysis text) with a specified proportion (for example, 112:243,2:5,1:2, etc.) in the data set is subjected to function point labeling, a labeled data set (also referred to as a first data set) is obtained, and sample data (for example, 243 ten-thousand-word) remaining in the data set is used for generating a pseudo label, and an unlabeled data set (also referred to as a second data set) is obtained, wherein the set of function point labels comprises the following function point labels: a first type of tag using ILF, a second type of tag using EIF, a third type of tag using EI, a fourth type of tag using EQ, and a fifth type of tag using EO.
And dividing the data set demand analysis text into a training set, a verification set and a test set according to a specific proportion.
Next, the Bert layer, the BiLSTM layer, and the CRF layer are constructed to construct the automatic extraction model.
According to the length n of the sample data in the training set, determining the dimension of the word vector to be generated, specifically, inputting the sample data with the length n into the Bert layer, generating a first vector with the dimension of n being a specific value, wherein the specific value ranges from 700 to 800, the specific value is preferably 768, and the range of n is more than 0 and less than or equal to 512.
Inputting the first vector into a BiLSTM layer to establish context connection among n vectors, and obtaining sequence semantic information corresponding to the text to be processed. And inputting the sequence semantic information obtained by the BiLSTM layer into a CRF layer, and outputting the function points contained in the text to be processed and the category of the function points to which each function point belongs.
Preferably, the training and retraining passes are determined based on the number of annotated data sets (i.e., first data sets) and unlabeled data sets (i.e., second data sets). According to the determined training round, training the automatic extraction model by using the training set divided by the first data set to obtain a preliminary automatic extraction model. Based on the determined retraining round, the preliminary automatic extraction model is additionally trained using the unlabeled dataset (second dataset).
Optimizing model parameters in the process of multiple model verification, specifically comprises updating training rounds and retraining rounds according to the change conditions of accuracy and loss values in the training process so as to optimize the model parameters in the process of multiple model verification.
Optimizing model parameters in the model test process specifically comprises updating retraining rounds according to the change conditions of accuracy and loss values in the retraining process so as to optimize the model parameters in the model test process.
By constructing an automatic extraction model and extracting the function points by using the automatic extraction model after optimizing model parameters, the function point extraction is more rapidly and effectively carried out, and the function point expansion method can be further optimized.
Furthermore, the drawings are only schematic illustrations of processes involved in a method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It will be readily understood that the processes shown in the figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
Compared with the prior art, the method and the device have the advantages that the functional point entity extraction and the knowledge relation extraction are carried out on the existing functional point analysis text, the functional point triples are formed to construct the functional point knowledge graph, and the functional point knowledge graph which is more accurate and comprises the functional point triples can be obtained; when a text to be processed is received, the contained functional points are identified, the starting node for performing traversal is determined according to the identified functional points, so that the step of traversing the relation path between the directed entity nodes in the functional point knowledge graph is performed, a knowledge graph node queue is established in the process of traversing the functional point knowledge graph, further, the functional point amplification is performed according to the established knowledge graph node queue, the automatic functional point amplification process can be realized more quickly and more effectively, and the problem of functional point missing can be effectively avoided.
In addition, based on a BFS searching algorithm, all reachable entity nodes are searched, and the entity nodes in the knowledge-graph node queue are updated by determining updating parameters in real time, so that the knowledge-graph node queue with higher reliability can be obtained, and the function point expansion method can be further optimized.
In addition, an automatic extraction model is built based on the Bert-BiLSTM-CRF algorithm, and the function points can be extracted more quickly and more effectively by using the automatic extraction model to automatically extract the function points of the existing function point analysis text, so that the function point expansion method can be further optimized.
The following are system embodiments of the present invention that may be used to perform method embodiments of the present invention. For details not disclosed in the system embodiments of the present invention, please refer to the method embodiments of the present invention.
FIG. 5 is a schematic diagram of the structure of an example of a knowledge-based functional point amplification system according to the present invention.
Referring to fig. 5, a second aspect of the present disclosure provides a functional point amplification system 500 based on a knowledge-graph, and the functional point amplification method based on the knowledge-graph according to the first aspect of the present disclosure is used. Specifically, the functional point augmentation system 500 includes an entity extraction module 510, a relationship extraction module 520, a receive processing module 530, a construction module 540, and an augmentation module 550.
Specifically, the entity extraction module 510 is configured to extract, according to the category of the function point and the scene parameter, a function point entity from the existing function point analysis text, where the function point entity includes multiple types of entities including verbs and/or nouns. The relationship extraction module 520 is configured to perform knowledge relationship extraction on the existing function point analysis text, and form a function point triplet to construct a function point knowledge graph, where the function point triplet includes a entity node corresponding to a function point entity, and a unidirectional relationship or a bidirectional relationship between adjacent entity nodes. The receiving processing module 530 recognizes the contained function points when receiving the text to be processed. The construction module 540 determines a starting node for performing traversal according to the identified functional point, so as to perform traversal of a relationship path between directed entity nodes in the functional point knowledge graph, and searches all reachable entity nodes based on a BFS search algorithm in the process of traversing the functional point knowledge graph, so as to establish a knowledge graph node queue, which specifically includes: and updating the entity nodes in the knowledge graph node queue. The amplification module 550 performs functional point amplification according to the established knowledge-graph node queue.
In an alternative embodiment, determining a starting node for performing traversal according to the identified function point so as to perform traversal of a relationship path between directed entity nodes in the function point knowledge graph, specifically including querying an original entity or a synonymous entity from a name dictionary according to the identified function point to determine an original entity corresponding to the identified function point, and further using the determined original entity as the starting node, starting to perform traversal of the function point knowledge graph until all relevant relationship paths are traversed.
In an optional embodiment, the updating the entity node in the knowledge-graph node queue includes: and determining scene parameters in real time, and updating the credibility threshold corresponding to each entity node in the functional point knowledge graph to be used for updating the entity nodes in the knowledge graph node queue.
In an optional embodiment, the establishing a knowledge-graph node queue includes: starting from the initial node, the adjacent entity node pointed by the initial node and the adjacent entity node pointed by the adjacent entity node until all the access of the reachable entity nodes are completed, judging whether each accessed entity node can be added to a knowledge graph node queue one by one.
The method specifically uses the following expression to calculate the accumulated weight value of each accessed entity node, and compares the calculated accumulated weight value with a corresponding credible threshold value to determine whether to add each accessed entity node to a knowledge-graph node queue or not, so as to establish the knowledge-graph node queue:
Figure SMS_12
wherein ,
Figure SMS_13
indicating which entity node N n Is added to the accumulated weight value of the (a); n represents the number of nodes passing by;
Figure SMS_14
representing slave entity node N 1 Start to reach the entity node N n The weight product of the n-1 paths experienced; w (W) 1 Representing slave entity node N 1 Starting the experienced weight of the 1 st edge; w (W) 2 Representing slave entity node N 1 Starting the experienced weight of the 2 nd edge; w (W) 3 Representing slave entity node N 1 Starting the experienced weight of the 3 rd edge; w (W) n-1 Representing slave entity node N 1 The weight of the n-1 th edge experienced, i.e. the weight of the last edge, starts.
In an optional embodiment, in the process of traversing the functional point knowledge graph, calculating the accumulated weight value of each accessed node, and determining the maximum relation path with the maximum accumulated weight value so as to determine all entity nodes on the maximum relation path.
And adding the entity nodes which are not in the knowledge-graph node queue on the maximum relation path into the knowledge-graph node queue so as to update related entity nodes in the knowledge-graph node queue in real time.
In an alternative embodiment, a new function point set is obtained according to the updated knowledge graph node queue, and the function point set is output.
In an alternative embodiment, the automatic function point extraction is performed on the existing function point analysis text using a pre-established automatic extraction model before the function point entity extraction is performed on the existing function point analysis text.
Based on the Bert-BiLSTM-CRF algorithm, an automatic extraction model is constructed, and the model construction specifically comprises the steps of optimizing model parameters in a multi-time model verification process and optimizing model parameters in a model test process.
In an alternative embodiment, knowledge relation extraction is performed on the existing function point analysis text to form a function point triplet to construct a function point knowledge graph, including: the method comprises the steps of extracting the knowledge relation of the internal relation between function points representing different kinds and different operations in the existing function point analysis text, and obtaining the following various relations for representing unidirectional or bidirectional edges between entity nodes: dependency, inheritance, aggregation, action, generalization, synonym, trigger, parallelism, interaction, coexistence.
Compared with the prior art, the method and the device have the advantages that the functional point entity extraction and the knowledge relation extraction are carried out on the existing functional point analysis text, the functional point triples are formed to construct the functional point knowledge graph, and the functional point knowledge graph which is more accurate and comprises the functional point triples can be obtained; when a text to be processed is received, the contained functional points are identified, the starting node for performing traversal is determined according to the identified functional points, so that the step of traversing the relation path between the directed entity nodes in the functional point knowledge graph is performed, a knowledge graph node queue is established in the process of traversing the functional point knowledge graph, further, the functional point amplification is performed according to the established knowledge graph node queue, the automatic functional point amplification process can be realized more quickly and more effectively, and the problem of functional point missing can be effectively avoided.
In addition, based on a BFS searching algorithm, all reachable entity nodes are searched, and the entity nodes in the knowledge-graph node queue are updated by determining updating parameters in real time, so that the knowledge-graph node queue with higher reliability can be obtained, and the function point expansion method can be further optimized.
In addition, an automatic extraction model is built based on the Bert-BiLSTM-CRF algorithm, and the function points can be extracted more quickly and more effectively by using the automatic extraction model to automatically extract the function points of the existing function point analysis text, so that the function point expansion method can be further optimized.
Fig. 6 is a schematic structural view of an embodiment of an electronic device according to the present invention.
As shown in fig. 6, the electronic device is in the form of a general purpose computing device. The processor may be one or a plurality of processors and work cooperatively. The invention does not exclude that the distributed processing is performed, i.e. the processor may be distributed among different physical devices. The electronic device of the present invention is not limited to a single entity, but may be a sum of a plurality of entity devices.
The memory stores a computer executable program, typically machine readable code. The computer executable program may be executed by the processor to enable an electronic device to perform the method, or at least some of the steps of the method, of the present invention.
The memory includes volatile memory, such as Random Access Memory (RAM) and/or cache memory, and may be non-volatile memory, such as Read Only Memory (ROM).
Optionally, in this embodiment, the electronic device further includes an I/O interface, which is used for exchanging data between the electronic device and an external device. The I/O interface may be a bus representing one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
It should be understood that the electronic device shown in fig. 6 is only one example of the present invention, and the electronic device of the present invention may further include elements or components not shown in the above examples. For example, some electronic devices further include a display unit such as a display screen, and some electronic devices further include a man-machine interaction element such as a button, a keyboard, and the like. The electronic device may be considered as covered by the invention as long as the electronic device is capable of executing a computer readable program in a memory for carrying out the method or at least part of the steps of the method.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, as shown in fig. 7, the technical solution according to the embodiment of the present invention may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several commands to cause a computing device (may be a personal computer, a server, or a network device, etc.) to perform the above-described method according to the embodiment of the present invention.
The software product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. The readable storage medium can also be any readable medium that can communicate, propagate, or transport the program for use by or in connection with the command execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
The computer-readable medium carries one or more programs (e.g., computer-executable programs) which, when executed by one of the devices, cause the computer-readable medium to implement the data interaction methods of the present disclosure.
Those skilled in the art will appreciate that the modules may be distributed throughout several devices as described in the embodiments, and that corresponding variations may be implemented in one or more devices that are unique to the embodiments. The modules of the above embodiments may be combined into one module, or may be further split into a plurality of sub-modules.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present invention may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and which includes several commands to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The exemplary embodiments of the present invention have been particularly shown and described above. It is to be understood that this invention is not limited to the precise arrangements, instrumentalities and instrumentalities described herein; on the contrary, the invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims (10)

1. The functional point amplification method based on the knowledge graph is characterized by comprising the following steps of:
extracting functional point entities from the existing functional point analysis text according to the functional point category and scene parameters, wherein the functional point entities comprise multi-class entities containing verbs and/or nouns;
extracting knowledge relation from the existing function point analysis text to form a function point triplet to construct a function point knowledge graph, wherein the function point triplet comprises entity nodes corresponding to the function point entities and unidirectional relations or bidirectional relations between adjacent entity nodes;
identifying the contained function points when receiving the text to be processed;
determining a starting node for performing traversal according to the identified functional points to perform the step of traversing the relation paths among the directed entity nodes in the functional point knowledge graph, searching all reachable entity nodes based on a BFS searching algorithm in the process of traversing the functional point knowledge graph to establish a knowledge graph node queue, wherein the method specifically comprises the following steps of: updating entity nodes in the knowledge graph node queue;
And performing functional point amplification according to the established knowledge graph node queue.
2. The knowledge-based functional point augmentation method of claim 1, wherein determining a starting node for performing traversal based on the identified functional points to perform traversal of a relationship path between directed entity nodes in the functional point knowledge graph, comprises:
according to the identified function points, the original entity or the synonymous entity is queried from the name dictionary to determine the original entity corresponding to the identified function points, and the step of traversing the function point knowledge graph is started to be executed by further taking the determined original entity as a starting node until all relevant relation paths are traversed.
3. The method for amplifying functional points based on a knowledge-graph according to claim 1, wherein updating the entity nodes in the knowledge-graph node queue comprises:
and determining scene parameters in real time, and updating the credibility threshold corresponding to each entity node in the functional point knowledge graph to be used for updating the entity nodes in the knowledge graph node queue.
4. A method for amplifying functional points based on a knowledge-graph according to claim 2 or 3, wherein the establishing a node queue of the knowledge-graph comprises:
Starting from the initial node, the adjacent entity node pointed by the initial node and the adjacent entity node pointed by the adjacent entity node until all the access of the reachable entity nodes are completed, judging whether each accessed entity node can be added to a knowledge graph node queue one by one.
5. The method for amplifying functional points based on a knowledge-graph according to claim 4, wherein,
calculating the accumulated weight value of each accessed entity node by using the following expression, and comparing the calculated accumulated weight value with a corresponding credible threshold value to determine whether to add each accessed entity node to a knowledge-graph node queue or not so as to establish the knowledge-graph node queue;
Figure QLYQS_1
wherein ,/>
Figure QLYQS_2
Indicating which entity node N n Is added to the accumulated weight value of the (a); n represents the number of nodes passing by; />
Figure QLYQS_3
Representing slave entity node N 1 Start to reach the entity node N n The weight product of the n-1 paths experienced; w (W) 1 Representing slave entity node N 1 Starting the experienced weight of the 1 st edge; w (W) 2 Representing slave entity node N 1 Starting the experienced weight of the 2 nd edge; w (W) 3 Representing slave entity node N 1 Starting the experienced weight of the 3 rd edge; w (W) n-1 Representing slave entity node N 1 The weight of the n-1 th edge experienced, i.e. the weight of the last edge, starts.
6. The method for amplifying functional points based on a knowledge-graph according to claim 4, wherein,
in the process of traversing the functional point knowledge graph, calculating the accumulated weight value of each accessed entity node, and determining the maximum relation path with the maximum accumulated weight value so as to determine all entity nodes on the maximum relation path;
adding entity nodes which are not in the knowledge graph node queue and are on the maximum relation path into the knowledge graph node queue so as to update related entity nodes in the knowledge graph node queue in real time.
7. The method for amplifying functional points based on a knowledge-graph according to claim 6, wherein,
and obtaining a new function point set according to the updated knowledge graph node queue, and outputting the function point set.
8. The method for amplifying functional points based on knowledge-graph according to claim 1 or 2,
before extracting the function point entity from the existing function point analysis text, using a pre-established automatic extraction model to extract the function point from the existing function point analysis text, wherein,
Based on the Bert-BiLSTM-CRF algorithm, an automatic extraction model is constructed, and the model construction specifically comprises the steps of optimizing model parameters in a multi-time model verification process and optimizing model parameters in a model test process.
9. The method for amplifying functional points based on a knowledge-graph according to claim 1, wherein,
the extracting knowledge relation to the existing function point analysis text to form a function point triplet to construct a function point knowledge graph comprises the following steps:
the method comprises the steps of extracting the knowledge relation of the internal relation between function points representing different kinds and different operations in the existing function point analysis text, and obtaining the following various relations for representing unidirectional or bidirectional edges between entity nodes:
dependency, inheritance, aggregation, action, generalization, synonym, trigger, parallelism, interaction, coexistence.
10. A knowledge-based functional point amplification system employing the knowledge-based functional point amplification method of any one of claims 1 to 9, comprising:
the entity extraction module is used for extracting functional point entities from the existing functional point analysis text according to the functional point category and the scene parameter, wherein the functional point entities comprise multi-class entities containing verbs and/or nouns;
The relation extraction module is used for extracting knowledge relation from the existing function point analysis text to form a function point triplet to construct a function point knowledge graph, wherein the function point triplet comprises entity nodes corresponding to the function point entities and one-way relations or two-way relations between adjacent entity nodes;
the receiving processing module is used for identifying the contained function points when receiving the text to be processed;
the construction module determines a starting node for performing traversal according to the identified functional points so as to perform traversal of a relation path among the directed entity nodes in the functional point knowledge graph, and searches all reachable entity nodes based on a BFS search algorithm in the process of traversing the functional point knowledge graph so as to establish a knowledge graph node queue, wherein the construction module specifically comprises the following steps: updating entity nodes in the knowledge graph node queue;
and the amplification module is used for carrying out functional point amplification according to the established knowledge graph node queue.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116841536A (en) * 2023-08-30 2023-10-03 成都摹客科技有限公司 Component reference relation reconstruction method, device, system and storage medium
CN117786126A (en) * 2023-12-28 2024-03-29 永信至诚科技集团股份有限公司 Knowledge graph-based naked-touch clue analysis method and device

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106886572A (en) * 2017-01-18 2017-06-23 中国人民解放军信息工程大学 Knowledge mapping relationship type estimation method and its device based on Markov Logic Networks
CN108776684A (en) * 2018-05-25 2018-11-09 华东师范大学 Optimization method, device, medium, equipment and the system of side right weight in knowledge mapping
CN109918511A (en) * 2019-01-29 2019-06-21 华融融通(北京)科技有限公司 A kind of knowledge mapping based on BFS and LPA is counter to cheat feature extracting method
US20200410008A1 (en) * 2019-06-27 2020-12-31 International Business Machines Corporation Auto generating reasoning query on a knowledge graph
CN114064910A (en) * 2021-09-29 2022-02-18 清华大学 Knowledge graph construction method and system
CN115098556A (en) * 2022-07-22 2022-09-23 重庆长安汽车股份有限公司 User demand matching method and device, electronic equipment and storage medium

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106886572A (en) * 2017-01-18 2017-06-23 中国人民解放军信息工程大学 Knowledge mapping relationship type estimation method and its device based on Markov Logic Networks
CN108776684A (en) * 2018-05-25 2018-11-09 华东师范大学 Optimization method, device, medium, equipment and the system of side right weight in knowledge mapping
CN109918511A (en) * 2019-01-29 2019-06-21 华融融通(北京)科技有限公司 A kind of knowledge mapping based on BFS and LPA is counter to cheat feature extracting method
US20200410008A1 (en) * 2019-06-27 2020-12-31 International Business Machines Corporation Auto generating reasoning query on a knowledge graph
CN114064910A (en) * 2021-09-29 2022-02-18 清华大学 Knowledge graph construction method and system
CN115098556A (en) * 2022-07-22 2022-09-23 重庆长安汽车股份有限公司 User demand matching method and device, electronic equipment and storage medium

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116841536A (en) * 2023-08-30 2023-10-03 成都摹客科技有限公司 Component reference relation reconstruction method, device, system and storage medium
CN116841536B (en) * 2023-08-30 2023-12-05 成都摹客科技有限公司 Component reference relation reconstruction method, device, system and storage medium
CN117786126A (en) * 2023-12-28 2024-03-29 永信至诚科技集团股份有限公司 Knowledge graph-based naked-touch clue analysis method and device

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