CN112463985B - Government map model construction method, government map model construction device, government map model construction equipment and computer readable medium - Google Patents
Government map model construction method, government map model construction device, government map model construction equipment and computer readable medium Download PDFInfo
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Abstract
The application relates to the technical field of knowledge graph, in particular to a government affair graph model construction method, a government affair graph model construction device, government affair graph model construction equipment and a computer readable medium. The method comprises the following steps: acquiring target government affair data acquired from the field of internet government affairs and used for representing legal person expansion information; extracting map knowledge matched with target government affair data from a preset legal person map knowledge base, wherein the legal person map knowledge base is obtained according to construction data of a general government affair knowledge map; and constructing a knowledge graph of legal person expansion information for the target government affair data by utilizing graph knowledge. The application deposits the knowledge accumulation generated in the construction process and provides necessary industry knowledge guidance for the construction of the extended information map model of the next government legal person, thereby applying the knowledge in the knowledge base to iteration and enabling non-professional technicians to participate in the construction work of the map model.
Description
Technical Field
The application relates to the technical field of knowledge graph, in particular to a government affair graph model construction method, a government affair graph model construction device, government affair graph model construction equipment and a computer readable medium.
Background
Regional industry analysis capability is provided for government industry, local industry development is guided, and meanwhile, comprehensive enterprise information query service is provided based on multidimensional enterprise big data, so that enterprise development situation and risk situation are monitored. By constructing enterprise legal knowledge maps, complex network relations among enterprises, high management, legal persons, brands, products, regions and industrial chains are deeply excavated. The construction of the corporate legal knowledge graph library in the government industry mainly gathers related information generated by legal persons in social and economic activities.
At present, in the related art, in the process of treating legal knowledge graph data, repeated iteration update is needed. According to the logic of knowledge acquisition, each iteration basically needs to go through the following 3 processes:
1. information extraction: extracting entities, attributes and interrelationships among the entities from various data sources, and forming an ontology knowledge expression on the basis of the entity and the attribute;
2. Knowledge fusion: after new knowledge is obtained, it needs to be integrated to resolve contradictions and ambiguities, such as that some entities may have multiple expressions, a particular designation may correspond to multiple different entities, etc.;
3. Knowledge processing: for the new knowledge after fusion, the qualified part can be added into the knowledge base after quality evaluation (part needs to be manually screened) so as to ensure the quality of the knowledge base.
Each iteration requires an industry expert to participate in screening deeply, for example, when the combined extraction is carried out by means of structured, unstructured and semi-structured data from the inside of an organization, a large amount of manpower is needed for auditing and verification, and therefore the efficiency is low. And the quality of knowledge extraction is poor, the low data quality has a great hindrance to the later application of the government map, and the knowledge in the knowledge base cannot be applied to iteration.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The application provides a government map model construction method, a government map model construction device, government map model construction equipment and a computer readable medium, which are used for solving the technical problem that knowledge in a knowledge base cannot be applied to iteration.
According to an aspect of the embodiment of the application, the application provides a government map model construction method, which comprises the following steps:
acquiring target government affair data acquired from the field of internet government affairs and used for representing legal person expansion information;
extracting map knowledge matched with target government affair data from a preset legal person map knowledge base, wherein the legal person map knowledge base is obtained according to construction data of a general government affair knowledge map;
and constructing a knowledge graph of legal person expansion information for the target government affair data by utilizing graph knowledge.
Optionally, obtaining the target government affair data includes at least one of the following modes:
Sequentially grabbing target government affair data in each page in the first grabbing link from the initial page of the first grabbing link; under the condition that all pages of the first grabbing link are grabbed and the ending condition is not met, continuing to grab target government affair data in each page of the second grabbing link from the initial page of the second grabbing link in sequence until the ending condition is met, and ending grabbing data;
Grabbing target government affair data in the current page; and under the condition that the ending condition is not met, determining a target link from a plurality of links in the current page, grabbing target government affair data in the target page pointed by the target link, and ending grabbing data when the ending condition is met.
Optionally, extracting the graph knowledge matched with the target government affair data from the preset legal graph knowledge base includes:
extracting a model identifier of a knowledge graph to be constructed of target government data;
at least one of a data classification label, a data coding standard and an entity association relation matched with the model identification is extracted from a preset legal person map knowledge base.
Optionally, constructing the knowledge graph of legal person expansion information for the target government affair data by utilizing the graph knowledge includes:
Classifying the target government affair data by utilizing a data classification label, wherein the data classification label comprises at least one of business information, stakeholder information, main personnel information, branch office information, annual report information, tax rating, illegal information, judicial assistance information and belief-losing person information;
encoding the classified target government affair data according to a data encoding standard;
correlating the encoded target government affair data according to the entity correlation relationship;
And constructing a knowledge graph by using the associated target government affair data.
Optionally, constructing the knowledge graph by using the associated target government affair data includes:
determining a target legal person from the target government affair data;
Extracting an ontology data set of the target legal person, wherein the data in the ontology data set is used for representing at least one of an object, an enterprise, a social organization, a road, a building and internet text associated with the target legal person;
And constructing the association edges between the main entity and the sub-entity and between the sub-entity and the sub-entity according to the association relationship between the object indicated by the body data set, the enterprise, the social organization, the building, the road and the Internet text by taking the target legal person as the main entity and the business field of the target legal person as the sub-entity.
Optionally, before extracting the graph knowledge matched with the target government affair data from the preset legal graph knowledge base, the method further comprises determining a data classification label according to at least one of the following modes, and storing the data classification label in the legal graph knowledge base:
Acquiring a first reference data set; converting data in the first reference data set into feature vectors; determining cosine similarity between the feature vectors, and classifying the feature vectors with the cosine similarity smaller than the target threshold value into the same classified data set; determining data classification labels of different classification data sets, and storing the data classification labels and the classification data sets in a legal atlas knowledge base;
Acquiring a second reference data set, wherein the second reference data set is stored by a table structure; performing semantic recognition on a second reference data set of the table structure; classifying according to the identification result; determining data classification labels of each class; and storing the data classification labels and the recognition results in a legal person map knowledge base.
Optionally, after constructing a knowledge graph of legal person expansion information for the target government affair data by utilizing graph knowledge, the method further comprises:
Acquiring verification data;
verifying the knowledge graph of the legal person expansion information by using verification data;
And when the verification result indicates that the accuracy of the knowledge graph reaches the target threshold, the verification is passed.
According to another aspect of the embodiment of the present application, there is provided a government map model construction apparatus, including:
the government affair data acquisition module is used for acquiring target government affair data acquired from the field of internet government affairs and used for representing legal person expansion information;
the map knowledge extraction module is used for extracting map knowledge matched with the target government affair data from a preset legal person map knowledge base which is obtained according to the construction data of the universal government affair knowledge map;
And the knowledge graph construction module is used for constructing a knowledge graph of legal person expansion information on the target government data by utilizing graph knowledge.
Optionally, the government affair data acquisition module includes:
the depth traversing unit is used for sequentially grabbing target government affair data in each page in the first grabbing link from the initial page of the first grabbing link; under the condition that all pages of the first grabbing link are grabbed and the ending condition is not met, continuing to grab target government affair data in each page of the second grabbing link from the initial page of the second grabbing link in sequence until the ending condition is met, and ending grabbing data;
The breadth traversing unit is used for capturing target government affair data in the current page; and under the condition that the ending condition is not met, determining a target link from a plurality of links in the current page, grabbing target government affair data in the target page pointed by the target link, and ending grabbing data when the ending condition is met.
Optionally, the atlas knowledge extraction module includes:
the model identification extraction unit is used for extracting a model identification of a knowledge graph to be constructed by the target government affair data;
the knowledge extraction unit is used for extracting at least one of data classification labels, data coding standards and entity association relations matched with the model identification from a preset legal person map knowledge base.
Optionally, the knowledge graph construction module includes:
The data classification unit is used for classifying the target government affair data by utilizing a data classification label, wherein the data classification label comprises at least one of business information, stakeholder information, main personnel information, branch office information, annual report information, tax rating, illegal information, judicial assistance information and belief-losing person information;
the data coding unit is used for coding the classified target government affair data according to a data coding standard;
The data association unit is used for associating the encoded target government affair data according to the entity association relation;
And the map construction unit is used for constructing a knowledge map by utilizing the associated target government affair data.
Optionally, the map construction unit includes:
The legal person determining subunit is used for determining a target legal person from the target government affair data;
the related data extraction subunit is used for extracting an ontology data set of the target legal person, wherein the data in the ontology data set is used for representing at least one of an object, an enterprise, a social organization, a road, a building and internet text related to the target legal person;
the map construction subunit is used for constructing the association edges between the main entity and the sub-entity and between the sub-entity and the sub-entity according to the association relationship between the object indicated by the body data set, the enterprise, the social organization, the building, the road and the Internet text by taking the target legal person as the main entity and the business field where the target legal person is located as the sub-entity.
Optionally, the apparatus further includes a data classification tag determination module, including:
a first determining unit for acquiring a first reference data set; converting data in the first reference data set into feature vectors; determining cosine similarity between the feature vectors, and classifying the feature vectors with the cosine similarity smaller than the target threshold value into the same classified data set; determining data classification labels of different classification data sets, and storing the data classification labels and the classification data sets in a legal atlas knowledge base;
A second determining unit, configured to obtain a second reference data set, where the second reference data set is stored with a table structure; performing semantic recognition on a second reference data set of the table structure; classifying according to the identification result; determining data classification labels of each class; and storing the data classification labels and the recognition results in a legal person map knowledge base.
Optionally, the apparatus further comprises a verification module comprising:
A verification data acquisition unit configured to acquire verification data;
The verification unit is used for verifying the knowledge graph of the legal extension information by using the verification data;
and the verification result determining unit is used for passing verification when the verification result indicates that the accuracy of the knowledge graph reaches the target threshold.
According to another aspect of the embodiments of the present application, there is provided an electronic device including a memory, a processor, a communication interface, and a communication bus, where the memory stores a computer program executable on the processor, the memory, the processor, and the communication interface communicate through the communication bus, and the processor executes the steps of the method when the processor executes the computer program.
According to another aspect of embodiments of the present application, there is also provided a computer readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the above-described method.
Compared with the related art, the technical scheme provided by the embodiment of the application has the following advantages:
The technical scheme of the application is to acquire target government affair data which is acquired from the field of Internet government affairs and is used for representing legal person expansion information; extracting map knowledge matched with target government affair data from a preset legal person map knowledge base, wherein the legal person map knowledge base is obtained according to construction data of a general government affair knowledge map; and constructing a knowledge graph of legal person expansion information for the target government affair data by utilizing graph knowledge. The application deposits the knowledge accumulation generated in the construction process and provides necessary industry knowledge guidance for the construction of the extended information map model of the next government legal person, thereby applying the knowledge in the knowledge base to iteration and enabling non-professional technicians to participate in the construction work of the map model.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described, and it will be apparent to those skilled in the art that other drawings can be obtained according to these drawings without inventive effort.
FIG. 1 is a schematic diagram of an alternative hardware environment for a government map model construction method according to an embodiment of the present application;
FIG. 2 is a flowchart of an alternative government map model construction method according to an embodiment of the present application;
FIG. 3 is a block diagram of an alternative government map model building device according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an alternative electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In the following description, suffixes such as "module", "component", or "unit" for representing elements are used only for facilitating the description of the present application, and are not of specific significance per se. Thus, "module" and "component" may be used in combination.
In the related art, in the process of treating legal knowledge graph data, repeated iterative updating is needed. According to the logic of knowledge acquisition, each iteration basically needs to go through the following 3 processes:
1. information extraction: extracting entities, attributes and interrelationships among the entities from various data sources, and forming an ontology knowledge expression on the basis of the entity and the attribute;
2. Knowledge fusion: after new knowledge is obtained, it needs to be integrated to resolve contradictions and ambiguities, such as that some entities may have multiple expressions, a particular designation may correspond to multiple different entities, etc.;
3. Knowledge processing: for the new knowledge after fusion, the qualified part can be added into the knowledge base after quality evaluation (part needs to be manually screened) so as to ensure the quality of the knowledge base.
Each iteration requires an industry expert to participate in screening deeply, for example, when the combined extraction is carried out by means of structured, unstructured and semi-structured data from the inside of an organization, a large amount of manpower is needed for auditing and verification, and therefore the efficiency is low. And the quality of knowledge extraction is poor, the low data quality has a great hindrance to the later application of the government map, and the knowledge in the knowledge base cannot be applied to iteration.
In order to solve the problems mentioned in the background art, according to an aspect of the embodiments of the present application, an embodiment of a government map model construction method is provided.
Alternatively, in the embodiment of the present application, the above-described government map model construction method may be applied to a hardware environment formed by the terminal 101 and the server 103 as shown in fig. 1. As shown in fig. 1, the server 103 is connected to the terminal 101 through a network, which may be used to provide services to the terminal or a client installed on the terminal, and a database 105 may be provided on the server or independent of the server, for providing data storage services to the server 103, where the network includes, but is not limited to: a wide area network, metropolitan area network, or local area network, and terminal 101 includes, but is not limited to, a PC, a cell phone, a tablet computer, etc.
The government map model construction method in the embodiment of the present application may be executed by the server 103, or may be executed by the server 103 and the terminal 101 together, as shown in fig. 2, and the method may include the following steps:
Step S202, acquiring target government affair data acquired from the field of Internet government affairs and used for representing legal person expansion information.
In the embodiment of the application, the internet in the government affair field comprises government departments, statistical offices and large sites related to enterprise management, and the target government affair data is enterprise business extension information surrounding legal persons (expressed by unified social credit codes), wherein the enterprise business extension information comprises business information, stakeholder information, main personnel, branch offices, annual report information, tax rating, serious illegal laws, judicial assistance, a letter loss person, a court announcement, a judge document, a court announcement, an executed person, spot check, environmental protection punishment, administrative punishment, engineering abnormality, management abnormality, real estate mortgage, judicial auction, stock right outgoing quality, owe taxes information, patents, trademark information, external investment, bidding, website record, copyright, financing history, administrative license, qualification certificate, software copyright, import-export credit, recruitment and the like. The data provides the bottom data support for shared services such as information inquiry, identity check and the like. The extended information of the legal person reflects the state attribute of the legal person in different life cycles, and the change frequency of the extended information of the legal person is larger due to the uncertainty of the operation and the activity of the legal person.
In the embodiment of the application, a legal person (unified social credit code) can be used as a core, and the one-code association of legal person information can be realized by associating enterprise registration information, enterprise change registration information, enterprise annual report information, tax registration information and other legal person expansion information with the unified social credit code.
Optionally, obtaining the target government affair data includes at least one of the following modes:
Sequentially grabbing target government affair data in each page in the first grabbing link from the initial page of the first grabbing link; under the condition that all pages of the first grabbing link are grabbed and the ending condition is not met, continuing to grab target government affair data in each page of the second grabbing link from the initial page of the second grabbing link in sequence until the ending condition is met, and ending grabbing data;
Grabbing target government affair data in the current page; and under the condition that the ending condition is not met, determining a target link from a plurality of links in the current page, grabbing target government affair data in the target page pointed by the target link, and ending grabbing data when the ending condition is met.
In the embodiment of the application, the depth traversal can be performed according to the depth-first traversal strategy to obtain the data, and the breadth traversal can be performed according to the breadth-first traversal strategy to obtain the data.
The depth-first traversal strategy is to select one link from a plurality of grabbing links, namely a first link, then the web crawler starts from a start page of the first link, tracks one link after another along the first link, transfers the first link into a second link after finishing processing the first link, starts from the start page in the second link, and then crawls data one link after another along the second link, and if an end condition is met in the process of grabbing data, the crawling of the data is stopped, the end condition can be determined according to the amount of the data to be acquired, and can also be determined according to the number of the links.
The breadth-first traversal strategy refers to that links found in the newly downloaded web page are directly inserted into the end of the address queue to be crawled. That is, the web crawler will first capture all the web pages linked in the current web page, then select one of the linked web pages, and continue capturing all the web pages linked in the web page. Likewise, crawling of data is stopped when an end condition is satisfied, which may be determined according to the amount of data to be acquired, and may also be determined according to the lateral extent of the link.
Optionally, in the embodiment of the present application, a data warehouse technology may be further adopted to align real-time data and non-real-time data, so as to implement full-scale and incremental data acquisition, and the data warehouse technology is already mature and will not be described herein.
Step S204, extracting graph knowledge matched with the target government affair data from a preset legal graph knowledge base, wherein the legal graph knowledge base is obtained according to the construction data of the universal government affair knowledge graph.
In the embodiment of the application, the knowledge graph can be constructed or iterated by utilizing the graph knowledge in the legal graph knowledge base. The map knowledge is obtained according to accumulated experience of various aspects such as data, methods, relations, standards and the like related in the construction process of the universal government knowledge map.
Optionally, extracting the graph knowledge matched with the target government affair data from the preset legal graph knowledge base includes:
extracting a model identifier of a knowledge graph to be constructed of target government data;
at least one of a data classification label, a data coding standard and an entity association relation matched with the model identification is extracted from a preset legal person map knowledge base.
In the embodiment of the application, the knowledge graph to be constructed can be predetermined before the knowledge graph is constructed, so that data acquisition is performed, the acquired data is marked with the model identifier, and the data classification label, the data coding standard and the entity association relation matched with the model identifier can be extracted from the legal graph knowledge base.
Optionally, before extracting the graph knowledge matched with the target government affair data from the preset legal graph knowledge base, the method further comprises determining a data classification label according to at least one of the following modes, and storing the data classification label in the legal graph knowledge base:
Acquiring a first reference data set; converting data in the first reference data set into feature vectors; determining cosine similarity between the feature vectors, and classifying the feature vectors with the cosine similarity smaller than the target threshold value into the same classified data set; determining data classification labels of different classification data sets, and storing the data classification labels and the classification data sets in a legal atlas knowledge base;
Acquiring a second reference data set, wherein the second reference data set is stored by a table structure; performing semantic recognition on a second reference data set of the table structure; classifying according to the identification result; determining data classification labels of each class; and storing the data classification labels and the recognition results in a legal person map knowledge base.
In the embodiment of the application, the first reference data set is an unstructured data set, the content of unstructured data can be identified through a natural language processing technology, then the data is classified, and finally corresponding class labels are marked. Specifically, the data in the first reference data set is converted into feature vectors in Embedding mode or Word2Vector mode, and the data are classified by calculating cosine similarity between the feature vectors, so that the feature vectors with the cosine similarity within a target threshold are classified into the same classified data set, and finally, a data classification label is attached to each classified data set. The second reference data set is a structured stored data set, and through analyzing the table structure, semantic analysis is carried out on the table nouns, the column names and the example data, and corresponding class labels are marked after analysis. After determining the data classification labels, the data classification labels and the corresponding data sets are stored in a legal profile knowledge base.
In the embodiment of the application, the finally obtained data classification label comprises industrial and commercial information, stakeholder information, main personnel information, branch office information, annual report information, tax rating, illegal information, judicial assistance information, and information of a belief-losing person.
The data coding standard can be realized through data cleaning, data integration and data reduction. The data cleaning is used for filling in the vacancy values, identifying isolated points, eliminating noise and correcting data inconsistency. Data integration may be used to integrate data from different data sources into a consistent data store, e.g., data integration may be performed by converting into metadata, correlation analysis, data collision detection, and semantic heterogeneity analysis, among other examples. Data reduction is a technique for data processing such as data cube aggregation, dimension reduction, data compression, numerical reduction, and discretization, which can be used to derive a reduced representation of data with minimal loss of information content.
The acquired data can be unified in format and data identification by unifying the data coding standard.
The entity association relationship can be divided into three aspects of entity, attribute and relationship. The entity level can determine the main entity types in the legal extended information map model: a legal person; the attribute layer comprises the attributes of legal persons including business information, annual report information, tax information and the like; relationship level, mainly including investment/stakeholders, legal representatives, high-rise pipes, branches and logical relationships between them.
And S206, constructing a knowledge graph of legal person expansion information for the target government affair data by utilizing graph knowledge.
In the embodiment of the application, the association edges between the entities can be constructed through each relation of the relation level, so that a knowledge graph is formed, and each information of the attribute level is stored in each entity node of the knowledge graph, so that the construction of the knowledge graph of the legal extension information is completed.
Optionally, constructing the knowledge graph of legal person expansion information for the target government affair data by utilizing the graph knowledge includes:
Classifying the target government affair data by utilizing a data classification label, wherein the data classification label comprises at least one of business information, stakeholder information, main personnel information, branch office information, annual report information, tax rating, illegal information, judicial assistance information and belief-losing person information;
encoding the classified target government affair data according to a data encoding standard;
correlating the encoded target government affair data according to the entity correlation relationship;
And constructing a knowledge graph by using the associated target government affair data.
In the embodiment of the application, the target government affair data can be classified according to the data classification labels stored in the legal person map knowledge base, then the target government affair data is encoded according to the data encoding standard to eliminate the problems of non-uniform format and incorrect data, finally the complex network relations among enterprises, high management, legal persons, brands, products, regions and industrial chains indicated in the target government affair data are determined according to the entity association relations, finally the legal persons create the entities, and the association sides are created according to the association relations, so that the knowledge map is constructed.
Optionally, constructing the knowledge graph by using the associated target government affair data includes:
determining a target legal person from the target government affair data;
Extracting an ontology data set of the target legal person, wherein the data in the ontology data set is used for representing at least one of an object, an enterprise, a social organization, a road, a building and internet text associated with the target legal person;
And constructing the association edges between the main entity and the sub-entity and between the sub-entity and the sub-entity according to the association relationship between the object indicated by the body data set, the enterprise, the social organization, the building, the road and the Internet text by taking the target legal person as the main entity and the business field of the target legal person as the sub-entity.
In the embodiment of the application, because the number of the bodies (including entities such as people, enterprises, social organizations, roads, buildings and the like, and also including events, related texts and multimedia occurring in the cities) of all root types of the cities is numerous, the number of the entity types can reach tens of thousands, the method is implemented according to knowledge graph steps, the concept of rapid iteration is implemented, a hierarchical domain division mode is adopted, a target legal person is firstly determined as a main entity, a sub-entity is determined according to the service use condition of the target legal person, and then the correlation edges between the main entity and the sub-entity and between the sub-entity and the sub-entity are added, so that the knowledge graph of the complete legal person expansion information is formed.
Optionally, after constructing a knowledge graph of legal person expansion information for the target government affair data by utilizing graph knowledge, the method further comprises:
Acquiring verification data;
verifying the knowledge graph of the legal person expansion information by using verification data;
And when the verification result indicates that the accuracy of the knowledge graph reaches the target threshold, the verification is passed.
In the embodiment of the application, the model designer needs to continuously check the data content to verify the service. The data content is inconsistent with the actual service due to the fact that the data content is too long, lack of maintenance and the like, and is limited by various conditions such as personnel experience, service system personnel experience and the like, more missed places exist, and in the model design stage, the problems are required to be found through continuous verification, and related results are required to be updated.
By adopting the technical scheme of the application, the knowledge accumulation generated in the construction process can be deposited, necessary industry knowledge guidance is provided for the next government law extended information map model construction, so that the knowledge in the knowledge base is applied to iteration, and non-professional technicians can participate in the map model construction work.
According to still another aspect of the embodiment of the present application, as shown in fig. 3, there is provided a government map model construction apparatus, including:
the government affair data acquisition module 301 is configured to acquire target government affair data, where the target government affair data is acquired from the internet government affair field and is used to represent legal person extension information;
the map knowledge extraction module 303 is configured to extract map knowledge matched with the target government affair data from a preset legal person map knowledge base, where the legal person map knowledge base is obtained according to construction data of a universal government affair knowledge map;
The knowledge graph construction module 305 is configured to construct a knowledge graph of legal person expansion information for the target government data by using graph knowledge.
It should be noted that, the government affair data obtaining module 301 in this embodiment may be used to execute step S202 in the embodiment of the present application, the atlas knowledge extracting module 303 in this embodiment may be used to execute step S204 in the embodiment of the present application, and the knowledge atlas constructing module 305 in this embodiment may be used to execute step S206 in the embodiment of the present application.
It should be noted that the above modules are the same as examples and application scenarios implemented by the corresponding steps, but are not limited to what is disclosed in the above embodiments. It should be noted that the above modules may be implemented in software or hardware as a part of the apparatus in the hardware environment shown in fig. 1.
Optionally, the government affair data acquisition module includes:
the depth traversing unit is used for sequentially grabbing target government affair data in each page in the first grabbing link from the initial page of the first grabbing link; under the condition that all pages of the first grabbing link are grabbed and the ending condition is not met, continuing to grab target government affair data in each page of the second grabbing link from the initial page of the second grabbing link in sequence until the ending condition is met, and ending grabbing data;
The breadth traversing unit is used for capturing target government affair data in the current page; and under the condition that the ending condition is not met, determining a target link from a plurality of links in the current page, grabbing target government affair data in the target page pointed by the target link, and ending grabbing data when the ending condition is met.
Optionally, the atlas knowledge extraction module includes:
the model identification extraction unit is used for extracting a model identification of a knowledge graph to be constructed by the target government affair data;
the knowledge extraction unit is used for extracting at least one of data classification labels, data coding standards and entity association relations matched with the model identification from a preset legal person map knowledge base.
Optionally, the knowledge graph construction module includes:
The data classification unit is used for classifying the target government affair data by utilizing a data classification label, wherein the data classification label comprises at least one of business information, stakeholder information, main personnel information, branch office information, annual report information, tax rating, illegal information, judicial assistance information and belief-losing person information;
the data coding unit is used for coding the classified target government affair data according to a data coding standard;
The data association unit is used for associating the encoded target government affair data according to the entity association relation;
And the map construction unit is used for constructing a knowledge map by utilizing the associated target government affair data.
Optionally, the map construction unit includes:
The legal person determining subunit is used for determining a target legal person from the target government affair data;
the related data extraction subunit is used for extracting an ontology data set of the target legal person, wherein the data in the ontology data set is used for representing at least one of an object, an enterprise, a social organization, a road, a building and internet text related to the target legal person;
the map construction subunit is used for constructing the association edges between the main entity and the sub-entity and between the sub-entity and the sub-entity according to the association relationship between the object indicated by the body data set, the enterprise, the social organization, the building, the road and the Internet text by taking the target legal person as the main entity and the business field where the target legal person is located as the sub-entity.
Optionally, the apparatus further includes a data classification tag determination module, including:
a first determining unit for acquiring a first reference data set; converting data in the first reference data set into feature vectors; determining cosine similarity between the feature vectors, and classifying the feature vectors with the cosine similarity smaller than the target threshold value into the same classified data set; determining data classification labels of different classification data sets, and storing the data classification labels and the classification data sets in a legal atlas knowledge base;
A second determining unit, configured to obtain a second reference data set, where the second reference data set is stored with a table structure; performing semantic recognition on a second reference data set of the table structure; classifying according to the identification result; determining data classification labels of each class; and storing the data classification labels and the recognition results in a legal person map knowledge base.
Optionally, the apparatus further comprises a verification module comprising:
A verification data acquisition unit configured to acquire verification data;
The verification unit is used for verifying the knowledge graph of the legal extension information by using the verification data;
and the verification result determining unit is used for passing verification when the verification result indicates that the accuracy of the knowledge graph reaches the target threshold.
According to another aspect of the embodiments of the present application, as shown in fig. 4, the present application provides an electronic device, including a memory 401, a processor 403, a communication interface 405 and a communication bus 407, where the memory 401 stores a computer program that can be executed on the processor 403, and the memory 401 and the processor 403 communicate with each other through the communication interface 405 and the communication bus 407, and the processor 403 executes the steps of the method.
The memory and the processor in the electronic device communicate with the communication interface through a communication bus. The communication bus may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, or the like. The communication bus may be classified as an address bus, a data bus, a control bus, or the like.
The memory may include random access memory (Random Access Memory, RAM) or may include non-volatile memory (non-volatile memory), such as at least one disk memory. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor.
The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, abbreviated as CPU), a network processor (Network Processor, abbreviated as NP), etc.; but may also be a digital signal processor (DIGITAL SIGNAL Processing, DSP), application Specific Integrated Circuit (ASIC), field-Programmable gate array (FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components.
There is also provided in accordance with yet another aspect of an embodiment of the present application a computer readable medium having non-volatile program code executable by a processor.
Optionally, in an embodiment of the present application, the computer readable medium is arranged to store program code for the processor to:
acquiring target government affair data acquired from the field of internet government affairs and used for representing legal person expansion information;
extracting map knowledge matched with target government affair data from a preset legal person map knowledge base, wherein the legal person map knowledge base is obtained according to construction data of a general government affair knowledge map;
and constructing a knowledge graph of legal person expansion information for the target government affair data by utilizing graph knowledge.
Alternatively, specific examples in this embodiment may refer to examples described in the foregoing embodiments, and this embodiment is not described herein.
When the embodiment of the application is specifically implemented, the above embodiments can be referred to, and the application has corresponding technical effects.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For a hardware implementation, the Processing units may be implemented within one or more Application SPECIFIC INTEGRATED Circuits (ASICs), digital signal processors (DIGITAL SIGNAL Processing, DSPs), digital signal Processing devices (DSP DEVICE, DSPD), programmable logic devices (Programmable Logic Device, PLDs), field-Programmable gate arrays (Field-Programmable GATE ARRAY, FPGA), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units for performing the functions described herein, or a combination thereof.
For a software implementation, the techniques described herein may be implemented by means of units that perform the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the embodiments of the present application may be embodied in essence or a part contributing to the prior art or a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc. It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing is only a specific embodiment of the application to enable those skilled in the art to understand or practice the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (6)
1. The government map model construction method is characterized by comprising the following steps of:
acquiring target government affair data, wherein the target government affair data is acquired from the field of internet government affairs and is used for representing legal extended information, and the legal extended information is associated information of unified social credit codes;
Extracting map knowledge matched with the target government affair data from a preset legal person map knowledge base, wherein the legal person map knowledge base is obtained according to construction data of a general government affair knowledge map;
constructing a knowledge graph of the legal person expansion information for the target government affair data by utilizing the graph knowledge;
The extracting the map knowledge matched with the target government affair data from the preset legal person map knowledge base comprises the following steps: extracting a model identifier of the knowledge graph to be constructed of the target government data; extracting at least one of a data classification label, a data coding standard and an entity association relation matched with the model identification from a preset legal person map knowledge base;
The constructing the knowledge graph of the legal person expansion information for the target government affair data by utilizing the graph knowledge comprises the following steps: classifying the target government data by using the data classification tag, wherein the data classification tag comprises at least one of business information, stakeholder information, main personnel information, branch office information, annual report information, tax rating, illegal information, judicial assistance information and belief-losing person information; coding the classified target government affair data according to the data coding standard; correlating the encoded target government affair data according to the entity correlation relationship; constructing the knowledge graph by utilizing the associated target government affair data;
The constructing the knowledge graph by using the associated target government affair data comprises the following steps: determining a target legal person from the target government affair data; extracting an ontology dataset of the target legal person, wherein data in the ontology dataset is used for representing at least one of an object, an enterprise, a social organization, a road, a building and internet text associated with the target legal person; the target legal person is taken as a main entity, the business field in which the target legal person is located is taken as a sub-entity, and the association edges between the main entity and the sub-entity and between the sub-entity and the sub-entity are constructed according to the association relationship among the object, the enterprise, the social organization, the building, the road and the Internet text indicated by the ontology data set;
Before extracting the graph knowledge matched with the target government affair data from a preset legal graph knowledge base, the method further comprises determining the data classification label according to at least one of the following modes, and storing the data classification label in the legal graph knowledge base: acquiring a first reference data set; converting data in the first reference data set into feature vectors; determining cosine similarity between the feature vectors, and classifying the feature vectors with the cosine similarity smaller than a target threshold value into the same classified data set; determining the data classification labels of different classification data sets, and storing the data classification labels and the classification data sets in the legal atlas knowledge base; acquiring a second reference data set, wherein the second reference data set is stored by a table structure; performing semantic recognition on the second reference data set of the table structure; classifying according to the identification result; determining the data classification labels of each class; and storing the data classification labels and the identification results in the legal person pattern knowledge base.
2. The method of claim 1, wherein obtaining target government data comprises at least one of:
Sequentially grabbing the target government affair data in each page in the first grabbing link from the initial page of the first grabbing link; under the condition that all pages of the first grabbing link are grabbed and the ending condition is not met, continuing to grab the target government affair data in each page of the second grabbing link from the initial page of the second grabbing link in sequence until the ending condition is met, and ending grabbing data;
Grabbing the target government affair data in the current page; and under the condition that the ending condition is not met, determining a target link from a plurality of links in the current page, and grabbing the target government affair data in the target page pointed by the target link until the ending condition is met, and ending grabbing data.
3. The method according to any one of claims 1 to 2, wherein after constructing a knowledge graph of the legal extension information for the target government affair data using the graph knowledge, the method further comprises:
Acquiring verification data;
verifying the knowledge graph of the legal person expansion information by using the verification data;
And when the verification result indicates that the accuracy of the knowledge graph reaches the target threshold, the verification is passed.
4. The utility model provides a government map model construction device which characterized in that includes:
the government affair data acquisition module is used for acquiring target government affair data, wherein the target government affair data are acquired from the field of internet government affairs and are used for representing legal extension information, and the legal extension information is associated information of unified social credit codes;
The map knowledge extraction module is used for extracting map knowledge matched with the target government affair data from a preset legal person map knowledge base, wherein the legal person map knowledge base is obtained according to construction data of a universal government affair knowledge map;
the knowledge graph construction module is used for constructing a knowledge graph of the legal person expansion information for the target government affair data by utilizing the graph knowledge;
The map knowledge extraction module is specifically configured to: extracting a model identifier of the knowledge graph to be constructed of the target government data; extracting at least one of a data classification label, a data coding standard and an entity association relation matched with the model identification from a preset legal person map knowledge base;
The knowledge graph construction module is specifically configured to: classifying the target government data by using the data classification tag, wherein the data classification tag comprises at least one of business information, stakeholder information, main personnel information, branch office information, annual report information, tax rating, illegal information, judicial assistance information and belief-losing person information; coding the classified target government affair data according to the data coding standard; correlating the encoded target government affair data according to the entity correlation relationship; constructing the knowledge graph by utilizing the associated target government affair data;
The knowledge graph construction module is further configured to construct the knowledge graph by using the associated target government data according to the following manner: determining a target legal person from the target government affair data; extracting an ontology dataset of the target legal person, wherein data in the ontology dataset is used for representing at least one of an object, an enterprise, a social organization, a road, a building and internet text associated with the target legal person; the target legal person is taken as a main entity, the business field in which the target legal person is located is taken as a sub-entity, and the association edges between the main entity and the sub-entity and between the sub-entity and the sub-entity are constructed according to the association relationship among the object, the enterprise, the social organization, the building, the road and the Internet text indicated by the ontology data set;
The data classification label determining module is used for: acquiring a first reference data set; converting data in the first reference data set into feature vectors; determining cosine similarity between the feature vectors, and classifying the feature vectors with the cosine similarity smaller than a target threshold value into the same classified data set; determining the data classification labels of different classification data sets, and storing the data classification labels and the classification data sets in the legal atlas knowledge base; acquiring a second reference data set, wherein the second reference data set is stored by a table structure; performing semantic recognition on the second reference data set of the table structure; classifying according to the identification result; determining the data classification labels of each class; and storing the data classification labels and the identification results in the legal person pattern knowledge base.
5. An electronic device comprising a memory, a processor, a communication interface and a communication bus, said memory storing a computer program executable on said processor, said memory, said processor communicating with said communication interface via said communication bus, characterized in that said processor, when executing said computer program, implements the steps of the method of any of the preceding claims 1 to 3.
6. A computer readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the method of any one of claims 1 to 3.
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| CN114119317A (en) * | 2021-11-22 | 2022-03-01 | 浪潮软件股份有限公司 | Knowledge graph construction method based on government affair service scene |
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