CN113065657A - Knowledge graph construction method and device based on public data of bank - Google Patents
Knowledge graph construction method and device based on public data of bank Download PDFInfo
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- CN113065657A CN113065657A CN202110382082.0A CN202110382082A CN113065657A CN 113065657 A CN113065657 A CN 113065657A CN 202110382082 A CN202110382082 A CN 202110382082A CN 113065657 A CN113065657 A CN 113065657A
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- G06N5/02—Knowledge representation; Symbolic representation
- G06N5/022—Knowledge engineering; Knowledge acquisition
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- G06N5/00—Computing arrangements using knowledge-based models
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- G06N5/022—Knowledge engineering; Knowledge acquisition
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Abstract
The invention discloses a method and a device for constructing a knowledge graph based on public data of a bank, wherein the method comprises the following steps: s1, data acquisition, namely establishing a main key for each acquired data source; s2, data cleaning, namely processing dirty data, missing values and abnormal values in original data; s3, performing correlation analysis, namely analyzing the correlation rate of the primary key in the primary table and the primary key in other data sources; s4, processing a data mart; s5, designing a network body layer, namely designing the network body layer relative to a physical layer so that only two bodies, namely people and enterprises, exist in a public network; and S6, extracting the network node relation. According to the method and the device, the important key information is extracted and a large number of invalid entities are removed in the construction process of the knowledge graph, so that the data processing efficiency is improved.
Description
Technical Field
The invention relates to the technical field of information, in particular to a knowledge graph construction method and device based on public data of banks.
Background
The banking industry generates massive transaction data every day, and with the continuous development of business of commercial banks and financial institutions, a large amount of business data is accumulated and is growing at a faster speed, and it is important to extract valuable entities and relationships from the massive data.
In recent years, with the rapid development of big data technology and the rapid increase of the computing capability of computers. Machine learning and deep learning technologies are increasingly widely used in banking industries, and great achievements are achieved in many application scenarios. In 2012, google proposed the concept of a knowledge graph for enhancing search engine functions, which is essentially a concept network whose nodes represent entities in the objective physical world and edges represent various semantic relationships existing between the entities. Through these relationships, an enterprise relationship network, i.e., an enterprise knowledge graph, can be constructed. The enterprise knowledge graph is constructed, so that potential association of enterprises can be mined from a large amount of disordered data, and an enterprise portrait can be generated.
It is understood that although the highly efficient integrated machine learning algorithm has wide application in banks, since banking services are divided into two major categories, namely, public services and private services, for public services, there are many known nodes of known knowledge graphs, and a large number of irrelevant entities and relationships are filled. Such huge data also presents a huge challenge to the data analysis departments of banks and their regulatory agencies. When the complexity of the data analysis task is relatively high, a large amount of information redundancy exists due to the fact that nodes and relations are numerous, and calculation of massive data causes overhead which is difficult to bear and low processing efficiency. How to construct a knowledge graph of massive financial data with various nodes and relations and analyze the knowledge graph so as to achieve the purpose of finding and avoiding financial risks is a technical problem to be solved urgently at present. Therefore, it is necessary to develop a method and a device for constructing a knowledge graph of public data by a bank, which can abstract node types and relationships in the graph highly, extract important key information, and remove a large number of invalid entities, thereby simplifying graph relationship structure and improving data processing efficiency.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to solve the technical problem of low data processing efficiency caused by various nodes and relations in the construction of the public data knowledge graph of a bank in the prior art, and provides a method and a device for constructing the public data knowledge graph based on the bank.
The invention provides a bank public data-based knowledge graph construction method, which is characterized by comprising the following steps of:
s1, data acquisition, namely establishing a main key for each acquired data source;
s2, data cleaning, namely processing dirty data, missing values and abnormal values in original data;
s3, performing correlation analysis, namely analyzing the correlation rate of the primary key in the primary table and the primary key in other data sources;
s4, processing the data mart, and merging the data sources needing to participate in the data mart construction in the step S3 according to the primary keys in the primary table;
s5, designing a network body layer, namely designing the network body layer relative to a physical layer so that only two bodies, namely people and enterprises, exist in a public network;
and S6, extracting network node relations, namely extracting all entities and relations among the entities from the data marts constructed in the step S4.
Further, in step S1, the data acquisition module is used to acquire data, and the acquired data sources include enterprise registration information, corporate governance, intangible assets, tax data, industrial and commercial annual inspection data, court litigation, share pledge, industrial and commercial punishment, enterprise loan overdue information, enterprise credit information, enterprise mobile assets and/or enterprise fixed assets; and establishing a primary key by using the enterprise social uniform code.
Further, in step S2, the missing value is processed by deleting the variable sequence with the missing rate greater than the first threshold, and for the missing value less than the first threshold, the missing sample is used as a predicted value, and the predicted value is calculated by using a random forest algorithm to fill the value;
further, in step S2, the processing for the abnormal value is to fill the abnormal value as a state with a special flag or to remove the abnormal value.
Further, in step S3, the main table is the enterprise registration information, and the main key is the enterprise social uniform code.
Further, in step S3, the association rates of the primary keys in the primary table and the primary keys in the other data sources are analyzed, the data sources with the association rates lower than the second threshold are set not to participate in the construction of the data mart, and the data sources with the association rates higher than the second threshold are set to participate in the construction of the data mart.
Further, the second threshold of the association rate is set to 80%, and the data sources with the association rate higher than 80% are set to participate in the construction of the data marts.
Further, in step S5, only two ontologies, namely, people and enterprise, are set in the public data network; only one relationship between persons is set; there are three relationships between people and businesses; there are six relationships between enterprises, removing other ontologies and redundant relationships.
Furthermore, there is a relationship between people, which is a relationship of relatives; there are three relations between people and enterprises, namely, an arbitrary relation, an investment relation and a guarantee relation; there are six relations between enterprises, namely investment relation, guarantee relation, litigation relation, suspected relation, trading relation and supply relation.
On the other hand, the invention provides a knowledge graph construction device based on bank public data, which comprises a data acquisition module, a data cleaning module, an association analysis module, a data market processing module, a network body layer design module and a network node relation extraction module, wherein the device is used for realizing the knowledge graph construction method based on bank public data.
The key technology of the invention is that the invention constructs the knowledge map of the massive financial data with various nodes and relations, abstracts the nodes in the map into two kinds of nodes of adults and enterprises, one relation of people and people, three relations of people and enterprises and six relations of enterprises and enterprises, thereby removing a large number of redundant nodes and relations, realizing map structure simplification, highly abstracting the node types and relations in the map, extracting important key information and removing a large number of invalid entities, thereby simplifying the map relation structure and improving the data processing efficiency.
Drawings
Fig. 1 shows a flow diagram of a method for building a knowledge graph based on public data of banks according to the invention.
FIG. 2 is a diagram illustrating a knowledge graph architecture of a bank-to-public data-based knowledge graph construction method and apparatus according to the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. 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.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
The knowledge graph is a basis for constructing a bank financial graph and is a financial relation network obtained by connecting massive financial data of banks together. In a relational database for storing financial data, the types of information stored can be mainly summarized into four categories of basic information of a client, all deposit account information of the client, basic information of a bank and all business information. The invention provides a method and a device for constructing an associated network for bank public data.
In the associated network model, the nodes are a set, each node has a label and a plurality of attributes, the label represents the type of the node, and the attributes include attribute names and attribute values and represent various information contained in the node.
In the relational database of the bank, the financial data are stored in the form of tables. Therefore, when the financial map is constructed, a mapping relation from a bank relation database mode to a financial map data mode needs to be established, and data conversion is carried out according to the mapping relation to obtain a corresponding financial map entity.
According to the attached figures 1 and 2, the invention provides a method and a device for constructing an associated network based on bank public data, which can be used for a financial company or a bank to construct an own public knowledge map. Referring to fig. 1, the construction process includes the following steps:
s1, data acquisition, namely establishing a main key for each acquired data source;
s2, data cleaning, namely processing dirty data, missing values and abnormal values in original data;
s3, performing correlation analysis, namely analyzing the correlation rate of the primary key in the primary table and the primary key in other data sources;
s4, processing the data mart, and merging the data sources needing to participate in the data mart construction in the step S3 according to the primary keys in the primary table;
s5, designing a network body layer, namely designing the network body layer relative to a physical layer so that only two bodies, namely people and enterprises, exist in a public network;
and S6, extracting network node relations, namely extracting all entities and relations among the entities from the data marts constructed in the step S4.
Correspondingly, the related network construction device based on the public data of the bank comprises a data acquisition module, a data cleaning module, a related analysis module, a data market processing module, a network body layer design module and a network node relation extraction module, and the functions are respectively realized.
The detailed procedure is as follows:
step S1, using a data acquisition module to acquire data, wherein the acquired data sources mainly include the following: enterprise registration information, corporate governance, intangible assets (patents, copyrights), tax data, industrial and commercial annual inspection data, court litigation, share pledge, industrial and commercial punishment, enterprise loan overdue information, enterprise credit information, enterprise mobile assets, enterprise fixed assets, and the like. Each acquired data source needs to establish a primary key, and generally, an enterprise social uniform code is used as a unique identifier for the purpose of merging the data sources;
step S2, entering a data cleaning process after the step I is completed; processing dirty data, missing values and abnormal values in original data, wherein the processing method of the missing values is to delete variable columns with the missing rate exceeding a given threshold, and missing samples with the missing rate less than the threshold can be used as predicted values to predict the values to be filled by using a random forest algorithm, or the missing values can be directly filled. The abnormal value is processed by filling the abnormal value as a state by using a special identifier, and the abnormal value can be directly removed;
step S3, after the data cleaning is finished, the correlation analysis is carried out; and taking the enterprise registration information as a main table, taking the enterprise social uniform code as a main key, analyzing the association rate of the main key in the main table and the main key in other data sources, if the association rate is lower than a threshold value, the data source does not participate in the construction of the data mart, and if the association rate is higher than the threshold value, the data source participates in the construction of the data mart. If the association rate of the social uniform codes of the major key enterprises in the major table and the social uniform codes of the enterprises in the tax data is more than 80%, the tax data can participate in the construction of the subsequent data mart. If the association rate of the main key enterprise social uniform code in the main table and the social uniform code of the enterprise in the enterprise loan overdue data is lower than 80%, the enterprise loan overdue data does not participate in the subsequent data mart construction process;
step S4, starting to process the data mart after step S2 and step S3 are completed; the main operation process is a process of merging the data sources which need to participate in the data mart construction in the step S3 according to the primary key enterprise social uniform code in the primary table;
step S5, after the data mart is built, a body layer of the public key network is designed; the design of the body layer is shown in fig. 2. There are only two ontologies in the public network, human and enterprise respectively. There is a relationship between people, which is a relationship of relativity; there are three relations between people and enterprises, which are an arbitrary relationship, an investment relationship and a guarantee relationship; there are six relationships between enterprises, namely investment relationships, warranty relationships, litigation relationships, suspected relationships, trading relationships, and supply relationships. The specific relationship is shown in fig. 2. For example, the following steps are carried out: zhang three is a specific entity of the ontology of people, and Lile four is also a specific entity of the ontology of people, company A is a specific entity of the ontology of enterprises, and company B is also a specific entity of the ontology of enterprises. Zhang III and Li IV are related, Zhang III plays the role of company A, Li IV invests company A, and company A invests company B.
Step S6, extracting all entities and the concrete relationships between the entities from the data mart constructed in step S4 according to the designed ontology.
The method has the advantages that the knowledge graph construction is carried out on the public-sea financial data with various nodes and relationships and large information redundancy, the node types and the relationships in the graph are highly abstracted, important key information is extracted, and meanwhile, a large number of invalid entities are removed, so that the graph relationship structure is simplified, and the data processing efficiency is improved. The key technology of the invention is to abstract the nodes in the map into two types of nodes of adults and enterprises, one type of relationship between people and people, three types of relationships between people and enterprises and six types of relationships between enterprises and enterprises, thereby eliminating a large number of redundant nodes and relationships, realizing high-efficiency processing of data, improving development efficiency and reducing maintenance cost.
The embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the computer program implements the processes of the above embodiments, and can achieve the same technical effect. The computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
Claims (10)
1. A knowledge graph construction method based on public data of banks is characterized by comprising the following steps:
s1, data acquisition, namely establishing a main key for each acquired data source;
s2, data cleaning, namely processing dirty data, missing values and abnormal values in original data;
s3, performing correlation analysis, namely analyzing the correlation rate of the primary key in the primary table and the primary key in other data sources;
s4, processing the data mart, and merging the data sources needing to participate in the data mart construction in the step S3 according to the primary keys in the primary table;
s5, designing a network body layer, namely designing the network body layer relative to a physical layer so that only two bodies, namely people and enterprises, exist in a public network;
and S6, extracting network node relations, namely extracting all entities and relations among the entities from the data marts constructed in the step S4.
2. The bank-to-public-data-based knowledge graph construction method according to claim 1, wherein in step S1, data acquisition is performed by using a data acquisition module, and acquired data sources comprise enterprise registration information, legal governance, intangible assets, tax data, industrial and commercial annual inspection data, court litigation, share property mortgage, industrial and commercial punishment, enterprise loan overdue information, enterprise credit information, enterprise mobile assets and/or enterprise fixed assets; and establishing a primary key by using the enterprise social uniform code.
3. The bank public data-based knowledge graph construction method according to claim 1, wherein in step S2, the missing value is processed by deleting variable columns with missing rate greater than a first threshold, and the missing samples with missing rate less than the first threshold are used as predicted values to be filled in by calculating and predicting the values by using a random forest algorithm.
4. The bank-based public data knowledge graph construction method according to claim 3, wherein the abnormal value is processed by filling the abnormal value as a state with a special identifier or removing the abnormal value in step S2.
5. The bank public data-based knowledge graph construction method according to claim 1, wherein in step S3, the main form is enterprise registration information, and the main key is an enterprise social uniform code.
6. The bank public data-based knowledge graph construction method according to claim 5, wherein the association rates of the primary keys in the primary table and the primary keys in other data sources are analyzed in step S3, the data sources with the association rates lower than the second threshold are set not to participate in the construction of the data marts, and the data sources with the association rates higher than the second threshold are set to participate in the construction of the data marts.
7. The bank public data-based knowledge graph construction method according to claim 6, wherein the second threshold value of the association rate is set to 80%, and data sources with the association rate higher than 80% are set to participate in the construction of the data marts.
8. The bank public data-based knowledge graph construction method according to claim 1, wherein in step S5, only two ontologies, respectively human and enterprise, are set in the public data network; only one relationship between persons is set; there are three relationships between people and businesses; there are six relationships between enterprises, removing other ontologies and redundant relationships.
9. The bank public data-based knowledge graph construction method according to claim 8, wherein there is a relationship between people, which is a relationship of relativity; there are three relations between people and enterprises, namely, an arbitrary relation, an investment relation and a guarantee relation; there are six relations between enterprises, namely investment relation, guarantee relation, litigation relation, suspected relation, trading relation and supply relation.
10. A knowledge graph construction device based on bank public data is characterized by comprising a data acquisition module, a data cleaning module, an association analysis module, a data market processing module, a network body layer design module and a network node relation extraction module, and the device is used for realizing the knowledge graph construction method based on bank public data according to any one of claims 1-9.
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