CN115827893A - Skill culture knowledge map generation method, device, equipment and medium - Google Patents
Skill culture knowledge map generation method, device, equipment and medium Download PDFInfo
- Publication number
- CN115827893A CN115827893A CN202211582910.6A CN202211582910A CN115827893A CN 115827893 A CN115827893 A CN 115827893A CN 202211582910 A CN202211582910 A CN 202211582910A CN 115827893 A CN115827893 A CN 115827893A
- Authority
- CN
- China
- Prior art keywords
- skill
- information
- map
- points
- point
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 59
- 238000012549 training Methods 0.000 claims abstract description 90
- 238000004590 computer program Methods 0.000 claims description 16
- 239000000126 substance Substances 0.000 claims description 2
- 230000009286 beneficial effect Effects 0.000 abstract description 7
- 239000013598 vector Substances 0.000 description 9
- 238000004891 communication Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 8
- 238000012545 processing Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 6
- 238000012800 visualization Methods 0.000 description 6
- 238000011161 development Methods 0.000 description 5
- 230000018109 developmental process Effects 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 239000000463 material Substances 0.000 description 4
- 238000003058 natural language processing Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000013473 artificial intelligence Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 230000007115 recruitment Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 239000000969 carrier Substances 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000006698 induction Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000000513 principal component analysis Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000012954 risk control Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/042—Knowledge-based neural networks; Logical representations of neural networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0464—Convolutional networks [CNN, ConvNet]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/02—Knowledge representation; Symbolic representation
- G06N5/022—Knowledge engineering; Knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
- G06Q50/2057—Career enhancement or continuing education service
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Computational Linguistics (AREA)
- Business, Economics & Management (AREA)
- General Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Educational Administration (AREA)
- Educational Technology (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Human Resources & Organizations (AREA)
- Animal Behavior & Ethology (AREA)
- Databases & Information Systems (AREA)
- Economics (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- General Business, Economics & Management (AREA)
- Micro-Organisms Or Cultivation Processes Thereof (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention discloses a skill training knowledge graph generation method, a skill training knowledge graph generation device, skill training knowledge graph generation equipment and skill training knowledge graph generation media. The method comprises the following steps: acquiring a plurality of skill point information and difficulty advanced relations among skill points with incidence relations; generating a plurality of initial skill maps according to the incidence relation and the difficulty order relation among the skill points; and generating a target skill culture knowledge map according to the direct association skill point set information of each skill point in each initial skill map and the complete skill advance path information among the skill points. The technical scheme of the embodiment of the invention solves the problem that the skill mastering condition of the cultured object cannot be visually displayed by the current skill culture knowledge base, can generate the skill culture knowledge base containing difficulty advanced information, and is beneficial to understanding the skill mastering condition and the skill level of different difficulty degrees of the cultured object.
Description
Technical Field
The embodiment of the invention relates to the technical field of artificial intelligence, in particular to a skill training knowledge graph generation method, a skill training knowledge graph generation device, skill training knowledge graph generation equipment and skill training knowledge graph generation media.
Background
With the development of internet technology, the skill training knowledge graph is also gradually used in work. However, the existing skill cultivation knowledge graph only presents name information of each skill point, and the graph content is not comprehensive, so that the skill mastering condition of the staff cannot be directly obtained.
Disclosure of Invention
The invention provides a skill training knowledge map generation method, device, equipment and medium, which can generate a skill training knowledge map containing difficulty level information and are beneficial to understanding the skill mastering conditions and skill levels of different difficulty levels of a training object.
According to an aspect of the present invention, there is provided a skill training knowledge base generation method, the method comprising:
acquiring a plurality of skill point information and difficulty advanced relations among skill points with incidence relations;
generating a plurality of initial skill maps according to the incidence relation and the difficulty order relation among the skill points;
and generating a target skill culture knowledge map according to the directly associated skill point set information of each skill point in each initial skill map and the complete skill progress path information among the skill points.
According to another aspect of the present invention, there is provided a skill training knowledge base generation apparatus comprising:
the skill point acquisition module is used for acquiring a plurality of skill point information and difficulty order relations among skill points with incidence relations;
the skill map generation module is used for generating a plurality of initial skill maps according to the incidence relation and the difficulty advanced relation among the skill points;
and the target map generation module is used for generating a target skill culture knowledge map according to the direct association skill point set information of each skill point in each initial skill map and the complete skill advance path information among the skill points.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the skills training knowledge map generation method of any of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the skill training knowledgegraph generation method of any one of the embodiments of the present invention when executed.
According to the technical scheme of the embodiment of the invention, the difficulty advanced relationship between a plurality of skill point information and the skill points with the incidence relationship is obtained; generating a plurality of initial skill maps according to the association relationship and the difficulty order relationship among the skill points; and generating a target skill culture knowledge map according to the directly associated skill point set information of each skill point in each initial skill map and the complete skill progress path information among the skill points. The technical scheme of the embodiment of the invention solves the problem that the skill mastering condition of the cultured object cannot be visually displayed by the current skill culture knowledge base, can generate the skill culture knowledge base containing difficulty advanced information, and is beneficial to understanding the skill mastering condition and the skill level of different difficulty degrees of the cultured object.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a skill training knowledge base generation method provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of an initial skills map provided in accordance with an embodiment of the present invention;
FIG. 3 is a flow chart of another skill training knowledge base generation method provided by an embodiment of the invention;
FIG. 4 is a flow chart of yet another skill training knowledge base generation method provided by an embodiment of the invention;
FIG. 5 is a flow chart of a specific skill training knowledge base generation method provided by an embodiment of the invention;
FIG. 6 is a visualization effect diagram of a station skill map provided by an embodiment of the present invention;
FIG. 7 is a block diagram of a skill training knowledge base generation apparatus according to an embodiment of the present invention;
fig. 8 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It is to be understood that the terms "target" and "initial" and the like in the description and claims of the invention and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 is a flowchart of a skill training knowledge graph generation method according to an embodiment of the present invention, and this embodiment may be applied to a scene of skill training knowledge graph generation. The method can be executed by a skill training knowledge map generating device, which can be realized in the form of hardware and/or software and can also be configured in electronic equipment.
As shown in fig. 1, the skill training knowledge base generation method includes the following steps:
and S110, acquiring a plurality of skill point information and difficulty advanced relations among the skill points with the incidence relations.
The skill point information comprises text data of the skill point information acquired from channels such as an enterprise cultivation scheme, skill demand information of each recruitment post, an existing skill learning or training platform and the like.
It is understood that a skill point with an association relationship means that the skill content exists between two or more skills and is related and complied with, for example, the relationship between a skill and a preceding skill, and a preceding skill B needs to be mastered before a skill A is mastered; difficulty advanced relations among the skill points can be established on the basis of the relation between the skills and the preposed skills, and the skill 1 with relatively low difficulty can be learned before the skill 2 with relatively high difficulty is learned.
And S120, generating a plurality of initial skill maps according to the incidence relation and the difficulty order relation among the skill points.
The map is a map set compiled by classes. Correspondingly, the initial skill map is used for classifying and compiling the skill points according to the incidence relation and the difficulty order relation.
Specifically, for each skill point, all the skill points having an association relationship with the skill point are connected together, and the skill points having a difficulty step relationship and the skill points having a learning sequence are marked, for example, two skill points having an association relationship with a difficulty from low to high may be connected in series to generate a skill step path of the skill point, which is used as a corresponding initial skill map; and traversing all the skill points, generating an initial skill map corresponding to the skill points, and finally obtaining a plurality of initial skill maps.
And S130, generating a target skill culture knowledge map according to the direct association skill point set information of each skill point in each initial skill map and the complete skill progress path information among the skill points.
The directly associated skill point set information is used to indicate a skill point set directly connected to a skill point, and may be, for example, a set of name numbers corresponding to the name settings of the directly connected front skill point and the advanced skill point corresponding to the skill point.
And for each skill point, taking all the skill points which can be directly connected with the skill point and the connection relation between the skill points as a complete skill path of the skill point.
The complete skill progress path information is used for representing the complete skill path and all the skill points related to the complete skill path, and a path identifier or a corresponding path number can be set for the connection relationship between the skill points. Specifically, fig. 2 is a schematic diagram of an initial skill map provided by an embodiment of the present invention, and as shown in fig. 2, each skill point in an initial skill map has a corresponding complete skill and a complete skill path corresponding to the complete skill. Taking the skill point S as an example, all the skill points that can be directly connected with S are collectively regarded as the complete skill of the skill point S, wherein all the skill points include all the preposed skill points and the advanced skill points that are directly connected with the skill point S. For skill point S, the skill point S and the skill points { S1, S2, S3, S4, S6} form a complete skill of S; s to skill point S4 there are three connections: S-S4, S-S3-S4 and S-S2-S3-S4, wherein the three connection relations comprise all the step paths of the S-S4 as the complete skill paths from the S to the skill point S4, and the complete skill paths from the S to other skill points can be obtained in the same way.
A Knowledge map (Knowledge Graph) is a Graph showing the relationship between the process of Knowledge development and the structure, and Knowledge facts are extracted from raw data (including structured data, semi-structured data, and unstructured data) and an external Knowledge base by techniques such as Knowledge Extraction (KE), knowledge Fusion (KF), knowledge Processing (KP), and Knowledge Update (KU). The knowledge map can be combined with a visualization technology to describe knowledge resources and carriers thereof, mine, analyze, construct, draw and display knowledge and mutual connection thereof, and can also be applied to scenes such as intelligent search, text analysis, machine reading understanding, abnormity monitoring, risk control and the like.
The target skill culture knowledge graph reflects the complete relationship of all skill points and consists of nodes (points) and edges (edges), wherein each node represents one skill point, and each edge represents the connection relationship between the skill points.
Specifically, according to each skill point in each primary skill map, a preposed skill point directly connected with the skill point, an advanced skill point and a complete skill path between all the skill points, all the skill points are correspondingly connected, a skill map comprising all the skill points and complete connection relations between all the skill points is generated, and the skill map is used as a target skill culture knowledge map.
According to the technical scheme of the embodiment of the invention, the difficulty advanced relationship between a plurality of skill point information and the skill points with the incidence relationship is obtained; generating a plurality of initial skill maps according to the incidence relation and the difficulty order relation among the skill points; and generating a target skill culture knowledge map according to the directly associated skill point set information of each skill point in each initial skill map and the complete skill progress path information among the skill points. The technical scheme of the embodiment of the invention solves the problem that the skill mastering condition of the cultured object cannot be visually displayed by the current skill culture knowledge base, can generate the skill culture knowledge base containing difficulty advanced information, and is beneficial to understanding the skill mastering condition and the skill level of different difficulty degrees of the cultured object.
Fig. 3 is a flowchart of another skill training knowledge base generation method provided in an embodiment of the present invention, which belongs to the same inventive concept as the skill training knowledge base generation method in the above embodiment, and further describes, on the basis of the above embodiment, a process of generating a target skill training knowledge base according to directly associated skill point set information of each skill point in each initial skill base and complete skill progression path information between each skill point. The method can be executed by a skill training knowledge map generation device, and the device can be realized by software and/or hardware and is integrated in electronic equipment with application development function.
As shown in fig. 3, the skill training knowledge base generation method includes the following steps:
s210, acquiring a plurality of skill point information and difficulty advanced relations among the skill points with the incidence relations.
And S220, generating a plurality of initial skill maps according to the incidence relation and the difficulty order relation among the skill points.
And S230, extracting direct association skill point set information of each skill point in each initial skill map and complete skill progress path information among the skill points.
Specifically, for each skill point, all the skill points directly associated with the skill point in the initial skill map are combined into a skill point set related to the skill point, and the skill point set is used as the directly associated skill point set information of the skill point; and extracting a complete step path composed of paths among the skill points in the initial skill map as complete skill step path information among the corresponding skill points.
And S240, vectorizing the directly related skill point set information and the complete skill progress path information, and generating vectorized representation information of each initial skill map based on vectorized representation results.
Specifically, the Text can be converted into a vector capable of expressing the corresponding Text semantics by using one-hot coding or Text embedding (Text entries) and other modes, so that vectorized representation of directly associated skill point set information and complete skill progression path information is realized, computer processing is facilitated, and the efficiency of generating a skill culture knowledge map can be improved.
Further, vectorizing and representing the directly related skill point set information and the complete skill progress path information, and generating vectorized representation information of each initial skill map based on a vectorized representation result, including:
firstly, directly associated skill point set information and complete skill progress path information are vectorized and represented in a text embedding mode, and vectorized representation results are obtained.
The text embedding is used for converting the character strings into corresponding real-value vectors, the text embedding establishes a vector for each word, similar vectors are selected for words appearing in the text, and the conversion of the text and the vectors is realized. In traditional Natural Language Processing (NLP), a word is treated as a discrete symbol, and then vector representation of directly associating skill point set information and complete skill progression path information can be achieved through one-hot Processing. However, one-hot vectors have no natural ambiguity concept, and cannot be used for natural languages that deal with ambiguities or ambiguities (ambiguities) that exist at various levels, such as text and dialog.
For NLP, word embedding can be done using Word2vec model, gloVe model or deepearning 4 j. Word embedding each word or phrase is mapped to a vector on the real number domain by embedding a high dimensional space with the number of all words in a continuous vector space with a low dimension. The word embedding method comprises an artificial neural network, dimension reduction of a word co-occurrence matrix, a probability model, explicit representation of the context in which the word is positioned and the like.
Specifically, the directly associated skill point set information and the complete skill progress path information are input into a Word2vec model, so that vectorization representation of the directly associated skill point set information and the complete skill progress path information is realized, and a vectorization representation result is obtained. In addition, the principal component analysis and the t-distribution neighborhood embedding algorithm can be used for reducing the dimension of a word space, and the visualization and word sense induction of word embedding are realized.
And then, performing network training by taking the vectorization representation result as input information of the convolution network of the first preset graph to obtain vectorization representation information of each initial skill map.
Among them, the Graph Convolutional Network (GCN) is a Convolutional neural network that can directly act on a Graph and use its structural information, and is used to extract features of the Graph, and can perform operations such as node classification (node classification), graph classification (Graph classification), and edge prediction (link prediction) by considering all the features of each node in the Graph.
The first preset graph convolution network is used for extracting the characteristics of the initial skill map and realizing vectorization representation of the initial skill map.
Specifically, the vectorization representation result of the directly associated skill point set information and the complete skill progression path information in each initial skill map is used as input information of a first preset graph convolution network for training, so that vectorization representation of the initial skill maps including each node in the graph and all characteristics corresponding to the node is obtained and used as vectorization representation information of each initial skill map.
And S250, generating a target skill culture knowledge graph based on the vectorization representation information of each initial skill graph.
Specifically, for vectorization representation information of each initial skill map, vectorization representation information corresponding to directly associated skill point set information of each skill point in each initial skill map and complete skill progression path information between each skill point needs to be extracted through a graph convolution network, so that a target skill culture knowledge map is generated.
Further, generating a target skill culture knowledge graph according to the vectorization representation information of each initial skill graph, wherein the generating comprises: and performing network training by taking the vectorization representation information of each initial skill map as input information of a convolution network of a second preset map to obtain a target skill culture knowledge map.
The second preset graph convolutional network is used for extracting all features of vectorization representation information of the initial skill graph, achieving skill point classification and obtaining a target skill culture knowledge graph.
Specifically, vectorization representing information of each initial skill map is input into a second preset map convolution network, so that further vectorization representing of the initial skill maps is achieved, and the efficiency of generating the skill training knowledge maps is further improved.
According to the technical scheme of the embodiment of the invention, the difficulty advanced relationship between a plurality of skill point information and the skill points with the incidence relationship is obtained; generating a plurality of initial skill maps according to the association relationship and the difficulty order relationship among the skill points; extracting direct associated skill point set information of each skill point in each initial skill map and complete skill progress path information among the skill points; vectorizing and representing the directly associated skill point set information and the complete skill step path information, and generating vectorized representation information of each initial skill map based on vectorized representation results; and generating a target skill culture knowledge graph based on the vectorization representation information of each initial skill graph. The technical scheme of the embodiment of the invention solves the problem that the skill mastering condition of the culture object cannot be visually displayed by the current skill cultivation knowledge map, can generate the skill cultivation knowledge map containing difficulty advanced information, and is beneficial to further knowing the skill mastering condition and the skill level of different difficulty degrees of the culture object.
Fig. 4 is a flowchart of another skill training knowledge graph generation method according to an embodiment of the present invention, which is applicable to the generation of a skill training knowledge graph of an enterprise, belongs to the same inventive concept as the skill training knowledge graph generation method in the foregoing embodiment, and further describes a process of updating a target skill training knowledge graph on the basis of the foregoing embodiment. The method can be executed by a skill training knowledge map generation device, and the device can be realized by software and/or hardware and is integrated in electronic equipment with application development function.
As shown in fig. 4, the skill training knowledge base generation method includes the following steps:
and S310, acquiring a plurality of skill point information and difficulty advanced relations among the skill points with the association relations.
And S320, generating a plurality of initial skill maps according to the incidence relation and the difficulty order relation among the skill points.
And S330, generating a target skill culture knowledge map according to the direct association skill point set information of each skill point in each initial skill map and the complete skill progress path information among the skill points.
And S340, acquiring information of the newly added skill points, and updating the target skill culture knowledge map according to the incidence relation and the difficulty step relation of the information of the newly added skill points and the related skill points in the target skill culture knowledge map.
In particular, as technology has evolved and knowledge has increased, so has the skills. Firstly, acquiring newly added skill point information when the newly added skill point is required; secondly, establishing an incidence relation and a difficulty step relation between the newly added skill points and the skill points in the target skill culture knowledge map, calling all the skill points with incidence relation with the newly added skill points as related skill points, and generating a plurality of initial skill maps according to the incidence relation and the difficulty step relation among the skill points; then, extracting direct association skill point set information of each skill point in each initial skill map and complete skill progress path information among the skill points through a graph volume network to obtain vectorization representation information of each initial skill map; and finally, generating an updated target skill culture knowledge graph based on the vectorization representation information of each initial skill graph.
Exemplarily, as shown in fig. 2, assume that a new skill point a is added between S and S2; the step relation r2 from S to S2 becomes S- > A- > S2, then the complete skills of S and S2 and all the complete skill step paths of all the skill points of the complete skill step paths of S and S2 should be used together as the part to be updated at this time. Similarly, if the step relation r4 from S to S4 is deleted, the complete skills of S and S4 and all the complete skill step paths of all the skill points of the complete skill step paths of S and S4 are used together as the update part at this time. And vectorizing and expressing the complete skill and the complete step path corresponding to the updated part, and using the vectorized and expressed complete skill and complete step path as input information of the GCN to obtain an updated target skill culture knowledge map through a GCN algorithm.
And S350, analyzing the incidence relation state of each skill point in the target skill culture knowledge graph, and determining the outdated skill point according to the incidence relation state.
Specifically, as technology develops, some skills that are no longer applied are correspondingly eliminated. Firstly, analyzing the association relation state of each skill point in the target skill culture knowledge map, and when the complete skill of a certain skill point is included in the complete skills of other skill points and the connection relation between the skill point and other skill points is reduced or disconnected, determining that the skill point is an outdated skill point.
And S360, removing outdated skill points and skill points with difficulty step relation with the outdated skill points from the target skill culture knowledge graph.
It will be appreciated that skill points that have difficulty ranking relationships with outdated skill points also need to be eliminated. And eliminating outdated skill points and skill points with difficulty progressive relation with the outdated skill points to update the target skill culture knowledge map.
In a specific embodiment, fig. 5 is a flow chart of a skill culture knowledge map generation method. The skill culture knowledge map generation method comprises the following steps:
and S410, acquiring a plurality of skill point information and preprocessing the skill point information to obtain text data of the skill point information.
Specifically, the skill point information is obtained from channels such as an enterprise cultivation scheme, skill demand information of each recruitment post, an existing skill learning or training platform and the like, and preprocessing such as data cleaning and name standardization is performed to obtain text data of the skill point information.
And S420, generating an initial skill map based on the difficulty order relationship among the skill points of the incidence relationship and storing the initial skill map.
Specifically, extracting difficulty advanced relations among skill points with incidence relations in text data through semantic recognition, entity segmentation and other modes; converting all skill points with the association relationship into triples (skill 1, advance, skill 2) for representing the difficulty advance from the skill 1 to the skill 2, and storing the triples (skill 1, advance, skill 2) corresponding to the initial skill map in an RDF format; and connecting the skill points corresponding to each triple according to the difficulty step relationship to obtain one or more initial skill maps as corresponding initial skill maps, and storing the initial skill maps by using neo4j based on the form of a map database.
S430, performing initial vectorization representation on the directly associated skill point set information and the complete skill progress path information in the initial skill map to obtain an initial vectorization representation result; and inputting the initial vectorization representation result into a first preset graph convolution network for vectorization representation to obtain vectorization representation information of each initial skill graph.
Performing vectorization representation on the directly associated skill point set information and the complete skill progress path information in the initial skill map in a text embedding mode to obtain a vectorization representation result serving as an initial vectorization representation result; inputting the initial vectorization representation result into a first preset graph convolution network for vectorization representation again to obtain vectorization representation information of each initial skill map.
And S440, generating a target skill culture knowledge graph according to the vectorization representation information of each initial skill graph.
And taking vectorization representation information of each initial skill map as input information of a second preset map convolutional network to obtain a target skill training knowledge map, and storing the target skill training knowledge map by using neo4j based on the form of a map database.
And S450, updating the target skill culture knowledge graph based on the skill points or the incidence relation thereof.
And updating the target skill culture knowledge map according to the association relationship and the difficulty step relationship corresponding to the skill point information corresponding to the newly added skill point/association relationship and the target skill culture knowledge map, and/or according to the skill point corresponding to the outdated skill point/association relationship and the skill point corresponding to the association relationship and the difficulty step relationship in the target skill culture knowledge map.
And S460, generating the post skill maps of the preset work posts on the basis of the target skill culture knowledge maps according to the preset skill requirements of the preset work posts.
It will be appreciated that the work station requires corresponding skills. Firstly, establishing a corresponding relation between a work post and skills, acquiring various types of learning materials related to each skill point aiming at each skill point, integrating all the materials into a skill knowledge base, and mastering the corresponding skills of the work post by a training object through the related materials of the learning skill knowledge base; and then, according to the corresponding relation between the working post and the skill, based on the target skill culture knowledge map, generating a corresponding post skill map, and when the target skill culture knowledge map is updated, correspondingly updating the post skill map and the skill knowledge base. Further, when there is a certain skill S, the following two cases are satisfied: first, a full skill of a skill S is included in a full skill of at least one other skill; secondly, when the connection coefficient of the skill S to other skills is reduced or the connection is disconnected, the skill S is considered as an outdated skill. And if the outdated skill points do not exist in any post skill graph after updating, the skill S is considered to be the skill to be deleted, and correspondingly, the skill nodes and all the advanced relations thereof are deleted from the enterprise skill graph.
Specifically, according to preset skill requirements of preset work positions in each enterprise, a position skill map of each preset work position is generated on the basis of a target skill culture knowledge map, and the position skill map is stored according to a position skill map data structure shown in table 1.
TABLE 1 post skill atlas data Structure
Firstly, the working position and the skill learning state of the training object are obtained and stored.
Specifically, the skill learning state of the training object is obtained through a written test, an interview and/or a comprehensive evaluation, the skill learning state of the training object is generated into a personal skill map of the training object, and the personal skill map of the training object is stored according to a data structure shown in table 2.
TABLE 2 data Structure of personal skills map
The training subjects' personal skill patterns are then matched to their position skill patterns.
Specifically, the personal skill pattern of the employee is matched with the post skill pattern thereof, so that the skill of the work post which is not mastered by the employee can be obtained, the learning material of the corresponding skill can be pushed, and the corresponding learning path is generated and stored according to the data structure shown in table 3.
Table 3 data structure of learning path
Finally, the personal skill map is updated.
Specifically, when the training subject completes the learning of the corresponding skill or updates the post skill pattern thereof, the personal skill pattern of the training subject is updated, and is stored according to the data structure shown in table 2, and the personal skill pattern of the training subject is updated.
And S470, visually displaying the post skill map according to the post information and the skill learning state of the training object.
The post information is used to represent the work post of the training subject.
The skill learning state is used to indicate the degree of mastery of the skill by the training subject, for example, the skill learning state includes mastered, not mastered and in learning, wherein the training subject is considered to have mastered the skill when the training subject learns all skills corresponding to the complete skill progression path of the skill point.
Specifically, firstly, acquiring a working post and a skill learning state of a training object; then, according to the corresponding relation between the working post and the skill, obtaining the learning state of the skill corresponding to the working post of the training object; finally, different visualization effects are set for the difficulty step relationship between the work post of the training object and the skill learning state and the skill point, for example, different colors can correspond to different skill learning states, the states can be displayed on the corresponding post skill maps, corresponding updating is carried out according to updating of the post skill maps, and the corresponding post skill maps are stored according to the data structures of the corresponding post skill maps; the relation between the skill points and the preposed skills can be displayed in an arrow or other forms and displayed on the corresponding target skill knowledge map, so that the visualization of the difficulty advanced relation between the skill points is realized.
Specifically, as shown in fig. 6, the post skill map is visually displayed according to the post information and the skill learning state of the training subject, so that the skill learning state of the training subject and the skills mastered, not mastered and learned by the training subject can be visually displayed.
Specifically, a post skill map is pushed to a training interface in a graphical mode, the post skill map is designed into a tree structure, and all skill points are set to be in a dark state to be displayed; when the training subject masters the corresponding skill, the personal skill map is updated, the corresponding post skill point is set to be in an 'illumination' state,
according to the technical scheme of the embodiment of the invention, the difficulty advanced relationship between a plurality of skill point information and the skill points with the incidence relationship is obtained; generating a plurality of initial skill maps according to the incidence relation and the difficulty order relation among the skill points; generating a target skill culture knowledge map according to the direct association skill point set information of each skill point in each initial skill map and the complete skill progress path information among the skill points; acquiring newly added skill point information, and updating the target skill culture knowledge map according to the incidence relation and difficulty step relation between the newly added skill point information and the related skill points in the target skill culture knowledge map; analyzing the incidence relation state of each skill point in the target skill culture knowledge graph, and determining outdated skill points according to the incidence relation state; and (4) eliminating outdated skill points and skill points with difficulty-order relation with the outdated skill points from the knowledge map cultured in the target skill. According to the technical scheme of the embodiment of the invention, the target skill culture knowledge map is updated by using the newly added and/or removed skill point information, so that the problem that the skill grasping condition of the culture object cannot be visually shown by the current skill culture knowledge map is solved, the skill culture knowledge map containing difficulty advanced information can be generated, and the skill grasping condition and the skill level of different difficulty degrees of the culture object can be favorably known.
Fig. 7 is a block diagram of a skill training knowledge base generation apparatus according to an embodiment of the present invention, which is applicable to a scenario of skill training knowledge base generation, and in particular, is more applicable to a situation of skill training knowledge base generation of an enterprise. The device can be realized in the form of hardware and/or software and is integrated in the electronic equipment with the application development function.
As shown in fig. 7, the skill training knowledge map generation device includes: a skill point acquisition module 510, a skill map generation module 520, and a target map generation module 530.
The skill point obtaining module 510 is configured to obtain a difficulty order relationship between a plurality of skill point information and skill points having an association relationship; a skill map generation module 520, configured to generate a plurality of initial skill maps according to the association relationship and the difficulty rank relationship between the skill points; and a target map generation module 530, configured to generate a target skill training knowledge map according to the directly associated skill point set information of each skill point in each initial skill map and the complete skill advance path information between each skill point.
According to the technical scheme of the embodiment of the invention, through mutual cooperation among all modules, the difficulty advanced relationship among a plurality of skill point information and the skill points with the incidence relationship is obtained; generating a plurality of initial skill maps according to the incidence relation and the difficulty order relation among the skill points; and generating a target skill culture knowledge map according to the directly associated skill point set information of each skill point in each initial skill map and the complete skill progress path information among the skill points. The technical scheme of the embodiment of the invention solves the problem that the skill mastering condition of the cultured object cannot be visually displayed by the current skill culture knowledge base, can generate the skill culture knowledge base containing difficulty advanced information, and is beneficial to understanding the skill mastering condition and the skill level of different difficulty degrees of the cultured object.
Optionally, the target map generating module 530 is configured to:
extracting direct associated skill point set information of each skill point in each initial skill map and complete skill progress path information among the skill points;
vectorizing and representing the directly associated skill point set information and the complete skill step path information, and generating vectorized representation information of each initial skill map based on vectorized representation results;
and generating a target skill culture knowledge graph based on the vectorization representation information of each initial skill graph.
Optionally, the target map generating module 530 is further configured to:
vectorizing the directly associated skill point set information and the complete skill progress path information in a text embedding mode to obtain a vectorized representation result;
and performing network training by taking the vectorization representation result as input information of the convolution network of the first preset graph to obtain vectorization representation information of each initial skill map.
Optionally, the target map generating module 530 is further configured to: and performing network training by taking the vectorization representation information of each initial skill map as input information of a convolution network of a second preset map to obtain a target skill culture knowledge map.
Optionally, the skill training knowledge map generation apparatus further includes a post skill map generation module, configured to: and generating the post skill map of each preset post on the basis of the target skill culture knowledge map according to the preset skill requirement of each preset post.
Optionally, the skill training knowledge map generation apparatus further includes a map visualization module, configured to: and visually displaying the post skill map according to the post information and the skill learning state of the training object.
Optionally, the skill training knowledge map generating device further includes a map updating module, configured to: and acquiring information of the newly added skill points, and updating the target skill culture knowledge map according to the incidence relation and difficulty step relation between the information of the newly added skill points and the related skill points in the target skill culture knowledge map.
Optionally, the map updating module is further configured to:
analyzing the incidence relation state of each skill point in the target skill culture knowledge graph, and determining outdated skill points according to the incidence relation state;
and eliminating the outdated skill points and the skill points with difficulty step relation with the outdated skill points from the target skill culture knowledge graph.
The skill training knowledge graph generation device provided by the embodiment of the invention can execute the skill training knowledge graph generation method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Fig. 8 is a block diagram of an electronic device according to an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, or other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), or other similar computing devices. The components shown herein, their connections, and their functions are exemplary only, and are not intended to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 8, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard or a mouse; an output unit 17 such as various types of displays or speakers; a storage unit 18 such as a magnetic disk or an optical disk; and a communication unit 19 such as a network card, modem, or wireless communication transceiver. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), any suitable processor, controller or microcontroller, and so forth. The processor 11 performs the various methods and processes described above, such as the skill training knowledgemap generation method.
In some embodiments, the skill training knowledge map generation method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the skills training knowledge-map generation method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the skill cultivation knowledgemap generation method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine or partly on a machine, partly on a machine and partly on a remote machine or entirely on a remote machine or server as a stand-alone software package.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a Display device (e.g., a CRT (Cathode Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user may provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (11)
1. A skill training knowledge map generation method is characterized by comprising the following steps:
acquiring a plurality of skill point information and difficulty advanced relations among skill points with incidence relations;
generating a plurality of initial skill maps according to the incidence relation and the difficulty order relation among the skill points;
and generating a target skill culture knowledge map according to the direct association skill point set information of each skill point in each initial skill map and the complete skill progress path information between the skill points.
2. The method of claim 1 wherein generating a target skill culture knowledge-graph from the directly associated skill point set information for each skill point and the complete skill progression path information between each skill point in each of the initial skill-graphs comprises:
extracting direct associated skill point set information of each skill point in each initial skill map and complete skill progress path information between each skill point;
vectorizing the direct associated skill point set information and the complete skill progress path information, and generating vectorized representation information of each initial skill map based on vectorized representation results;
and generating the target skill culture knowledge graph based on vectorized representation information of each initial skill graph.
3. The method according to claim 2 wherein vectorizing the directly related skill point set information and the complete skill progression path information and generating vectorized representation information for each of the initial skill atlases based on vectorized representation results comprises:
vectorizing the direct associated skill point set information and the complete skill progress path information in a text embedding mode to obtain a vectorized representation result;
and performing network training by taking the vectorization representation result as input information of a first preset graph convolutional network to obtain vectorization representation information of each initial skill map.
4. The method of claim 2, wherein generating the target skill culture profile from the vectorized representation information of each of the initial skill profiles comprises:
and performing network training by taking the vectorization representation information of each initial skill map as input information of a second preset map convolutional network to obtain the target skill culture knowledge map.
5. The method according to any one of claims 1-4, further comprising:
and generating the post skill map of each preset post on the basis of the target skill culture knowledge map according to the preset skill requirement of each preset post.
6. The method of claim 5, further comprising:
and visually displaying the post skill map according to the post information and the skill learning state of the training object.
7. The method of claim 1, further comprising:
and acquiring newly added skill point information, and updating the target skill culture knowledge map according to the incidence relation and difficulty step relation between the newly added skill point information and the related skill points in the target skill culture knowledge map.
8. The method of claim 1, further comprising:
analyzing the incidence relation state of each skill point in the target skill culture knowledge graph, and determining outdated skill points according to the incidence relation state;
and eliminating the outdated skill points and the skill points with difficulty step relation with the outdated skill points from the target skill culture knowledge graph.
9. A skill training knowledge map generation apparatus, comprising:
the skill point acquisition module is used for acquiring a plurality of skill point information and difficulty advanced relations among the skill points with incidence relations;
a skill map generation module for generating a plurality of initial skill maps according to the incidence relation and the difficulty order relation between the skill points;
and the target map generation module is used for generating a target skill culture knowledge map according to the direct association skill point set information of each skill point in each initial skill map and the complete skill advance path information among the skill points.
10. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the skill training knowledgemap generation method of any of claims 1-8.
11. A computer-readable storage medium storing computer instructions for causing a processor to perform the skill training knowledgegraph generation method of any of claims 1-8 when executed.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211582910.6A CN115827893A (en) | 2022-12-09 | 2022-12-09 | Skill culture knowledge map generation method, device, equipment and medium |
| CN202310578160.3A CN116662563A (en) | 2022-12-09 | 2023-05-22 | Skill culture knowledge graph generation method, device, equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211582910.6A CN115827893A (en) | 2022-12-09 | 2022-12-09 | Skill culture knowledge map generation method, device, equipment and medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115827893A true CN115827893A (en) | 2023-03-21 |
Family
ID=85546163
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211582910.6A Pending CN115827893A (en) | 2022-12-09 | 2022-12-09 | Skill culture knowledge map generation method, device, equipment and medium |
| CN202310578160.3A Pending CN116662563A (en) | 2022-12-09 | 2023-05-22 | Skill culture knowledge graph generation method, device, equipment and medium |
Family Applications After (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310578160.3A Pending CN116662563A (en) | 2022-12-09 | 2023-05-22 | Skill culture knowledge graph generation method, device, equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (2) | CN115827893A (en) |
-
2022
- 2022-12-09 CN CN202211582910.6A patent/CN115827893A/en active Pending
-
2023
- 2023-05-22 CN CN202310578160.3A patent/CN116662563A/en active Pending
Also Published As
| Publication number | Publication date |
|---|---|
| CN116662563A (en) | 2023-08-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112560496A (en) | Training method and device of semantic analysis model, electronic equipment and storage medium | |
| CN112579727B (en) | Document content extraction method and device, electronic equipment and storage medium | |
| CN111522967A (en) | Knowledge graph construction method, device, equipment and storage medium | |
| CN115688920B (en) | Knowledge extraction method, training device, training equipment and training medium for model | |
| CN112541070B (en) | Mining method and device for slot updating corpus, electronic equipment and storage medium | |
| CN113722493A (en) | Data processing method, device, storage medium and program product for text classification | |
| CN112148881A (en) | Method and apparatus for outputting information | |
| CN111859953A (en) | Training data mining method and device, electronic equipment and storage medium | |
| CN114281968A (en) | Model training and corpus generation method, device, equipment and storage medium | |
| CN113361240B (en) | Method, apparatus, device and readable storage medium for generating target article | |
| CN112560480B (en) | Task community discovery method, device, equipment and storage medium | |
| CN112699237B (en) | Label determination method, device and storage medium | |
| CN113837307A (en) | Data similarity calculation method and device, readable medium and electronic equipment | |
| CN115248890A (en) | User interest portrait generation method and device, electronic equipment and storage medium | |
| CN115186738B (en) | Model training method, device and storage medium | |
| CN114239583B (en) | Method, device, equipment and medium for training entity chain finger model and entity chain finger | |
| CN116467461A (en) | Data processing method, device, equipment and medium applied to power distribution network | |
| CN113392220B (en) | Knowledge graph generation method and device, computer equipment and storage medium | |
| CN114611609A (en) | Graph network model node classification method, device, equipment and storage medium | |
| CN115827893A (en) | Skill culture knowledge map generation method, device, equipment and medium | |
| CN114528378A (en) | Text classification method and device, electronic equipment and storage medium | |
| CN113590774A (en) | Event query method, device and storage medium | |
| CN113536751B (en) | Processing method and device of form data, electronic equipment and storage medium | |
| CN116737520B (en) | Data braiding method, device and equipment for log data and storage medium | |
| CN117574168A (en) | Information report generation method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20230321 |
|
| WD01 | Invention patent application deemed withdrawn after publication |