Data asset value evaluation method and device, medium, equipment and product

Abstract

The invention relates to the technical field of data asset evaluation, and particularly provides a data asset value evaluation method and device, a medium, equipment and a product, and the method can comprise the steps: obtaining a first comprehensive value score of a to-be-evaluated data asset based on an initial knowledge graph; the initial knowledge graph is constructed based on business data; the initial knowledge graph represents an entity association relationship between different types of business data; the business data comprises customer data, order data, contract data, account data, product data, store data or index data; obtaining a second comprehensive value score of the to-be-evaluated data assets on the basis of an extended knowledge graph obtained after reasoning of the initial knowledge graph; and sorting the to-be-evaluated data assets through a difference value between the first comprehensive value score and the second comprehensive value score to obtain a value sorting result of the to-be-evaluated data assets. According to the embodiment of the invention, the accuracy of data asset evaluation can be improved.

Description

Technical Field

[0001] This application relates to the field of data asset valuation technology, and more specifically, to a method, apparatus, medium, equipment, and product for data asset valuation. Background Technology

[0002] Data asset assessment typically relies on asset catalogs and metadata tags, combined with metrics such as access frequency, call count, storage volume, and update frequency to score data assets. However, existing solutions often use technical resources such as "tables / fields / files / interfaces" for statistical analysis, making it difficult to uniformly express the identity and entity relationships of cross-system business objects at the semantic level. This results in the difficulty of accurately assessing the overall business value generated by cross-table and multi-domain data combinations.

[0003] On the other hand, data asset valuation is a professional service in which asset valuation institutions and professionals assess and estimate the value of data assets for a specific purpose in accordance with regulations and valuation standards. Its core is to combine the characteristics of data assets, such as non-physical nature, dependence, and value volatility.

[0004] Currently, data asset assessment primarily relies on asset catalogs and metadata tagging methods. This approach involves manually or semi-automatically constructing a data asset catalog and tagging each table and field with metadata tags such as "subject domain" and "system origin." Then, technical indicators such as access frequency, call count, storage volume, and update frequency are used to score the importance or value of the data assets. However, this data asset assessment method largely depends on subjective human scoring, making it difficult to accurately and objectively assess the qualitative or quantitative nature of data assets.

[0005] Therefore, how to provide a more accurate method for assessing the value of data assets has become an urgent technical problem to be solved. Summary of the Invention

[0006] The purpose of some embodiments of this application is to provide a method, apparatus, medium, equipment and product for data asset valuation. The technical solutions of the embodiments of this application can realize qualitative or quantitative valuation of data assets, improve the accuracy of data asset valuation, and accurately reflect the overall business value of data assets.

[0007] In a first aspect, some embodiments of this application provide a method for assessing the value of data assets, comprising: obtaining a first comprehensive value score of the data asset to be assessed based on an initial knowledge graph; wherein the initial knowledge graph is constructed based on business data; the initial knowledge graph represents entity relationships between different types of business data; the business data includes customer data, order data, contract data, account data, product data, store data, or indicator data; obtaining a second comprehensive value score of the data asset to be assessed based on an extended knowledge graph inferred from the initial knowledge graph; and ranking the data asset to be assessed by the difference between the first comprehensive value score and the second comprehensive value score to obtain a value ranking result of the data asset to be assessed.

[0008] Some embodiments of this application obtain a first comprehensive value score for the data asset to be evaluated through an initial knowledge graph, then obtain a second comprehensive value score by expanding the knowledge graph, and finally rank the data assets to be evaluated based on the difference between the two scores to obtain the value ranking result of the data assets to be evaluated. Embodiments of this application can achieve qualitative or quantitative evaluation of data assets, improve the accuracy of data asset value evaluation, and accurately reflect the overall business value of data assets.

[0009] In some embodiments, obtaining a first comprehensive value score for the data asset to be evaluated includes: determining a first evaluation indicator for the data asset to be evaluated; wherein the types of the first evaluation indicator include business contribution, data quality, reusability, and risk compliance value; and performing a weighted calculation on the first evaluation indicator and its corresponding weight to obtain the first comprehensive value score.

[0010] Some embodiments of this application determine a first evaluation index for the data asset to be evaluated and then perform a weighted calculation based on the weights to obtain a first comprehensive value score, providing data support for subsequent data asset value evaluation.

[0011] In some embodiments, obtaining the second comprehensive value score of the data asset to be evaluated includes: determining a second evaluation indicator for the data asset to be evaluated; wherein the types of the second evaluation indicator include business contribution, data quality, reusability, and risk compliance value; and performing a weighted calculation on the second evaluation indicator and its corresponding weight to obtain the second comprehensive value score.

[0012] Some embodiments of this application determine a second evaluation index for the data asset to be evaluated and then perform a weighted calculation based on the weights to obtain a second comprehensive value score, providing data support for subsequent data asset value evaluation.

[0013] In some embodiments, the extended knowledge graph is obtained through the following steps: loading preset reasoning rules; reasoning on the initial knowledge graph based on the preset reasoning rules to generate reasoning tags and associations for the target entities; and adding the reasoning tags and associations to the initial knowledge graph to obtain the extended knowledge graph.

[0014] Some embodiments of this application use preset reasoning rules to reason about the initial knowledge graph to generate reasoning labels and relationships of target entities, and add them to the initial graph to obtain an extended knowledge graph. This can help to uncover implicit relationships between entities and improve the accuracy of data asset value assessment.

[0015] In some embodiments, the initial knowledge graph is obtained through the following steps: obtaining raw business data from multiple business systems; preprocessing the raw business data to obtain the business data; mapping the business data to a preset business ontology model to obtain the initial knowledge graph; wherein the business ontology model includes entity classes and relationship types between entities.

[0016] Some embodiments of this application map the business data obtained by preprocessing the acquired raw business data into a business ontology model to obtain an initial knowledge graph, thereby achieving the effective construction of the initial knowledge graph.

[0017] In some embodiments, after obtaining the value ranking result of the data asset to be evaluated, the method further includes: generating a visualization result that matches the value ranking result; wherein the visualization result is represented using multiple types of graphics; displaying the visualization result, or sending the visualization result to an external data platform.

[0018] Some embodiments of this application can clearly display the data asset value assessment results by showing the visualization results or sending them to an external data platform, making them easy to view.

[0019] Secondly, some embodiments of this application provide an apparatus for assessing the value of data assets, comprising: a first scoring module, used to obtain a first comprehensive value score of the data asset to be assessed based on an initial knowledge graph; wherein the initial knowledge graph is constructed based on business data; the initial knowledge graph represents entity relationships between different types of business data; the business data includes customer data, order data, contract data, account data, product data, store data, or indicator data; a second scoring module, used to obtain a second comprehensive value score of the data asset to be assessed based on an extended knowledge graph inferred from the initial knowledge graph; and a value assessment module, used to rank the data asset to be assessed by the difference between the first comprehensive value score and the second comprehensive value score, to obtain a value ranking result of the data asset to be assessed.

[0020] Thirdly, some embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can implement the method described in any embodiment of the first aspect.

[0021] Fourthly, some embodiments of this application provide an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, can implement the method as described in any embodiment of the first aspect.

[0022] Fifthly, some embodiments of this application provide a computer program product, the computer program product including a computer program, wherein the computer program, when executed by a processor, can implement the method described in any embodiment of the first aspect. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of some embodiments of this application, the accompanying drawings used in some embodiments of this application will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A system diagram for data asset valuation provided for some embodiments of this application; Figure 2 Flowcharts of methods for data asset valuation provided for some embodiments of this application; Figure 3 A schematic diagram illustrating the composition structure of data asset valuation provided for some embodiments of this application; Figure 4Block diagram of an apparatus for assessing the value of data assets provided for some embodiments of this application; Figure 5 A schematic diagram of an electronic device provided for some embodiments of this application. Detailed Implementation

[0025] The technical solutions of some embodiments of this application will now be described with reference to the accompanying drawings.

[0026] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] In evaluating data assets, relevant technologies typically employ methods such as asset catalogs and metadata tagging, single-table / single-domain dimensional indicator analysis, or subjective scoring or expert evaluation. Single-table / single-domain dimensional indicator analysis focuses on a specific subject domain (e.g., customer domain, order domain), constructing quality and business indicators (e.g., missing rate, consistency, error rate, order amount distribution) for that domain, using these domain-specific indicators as the primary measure of the data asset's value. Subjective scoring or expert evaluation methods involve business experts and data governance teams subjectively scoring the importance, commercial value, and risk level of data assets based on experience; the scores are then weighted and aggregated to derive a comprehensive value score for each asset type. It can be seen that existing data asset valuation methods generally rely on technical or management indicators such as data access frequency, data volume, and subjective scoring to qualitatively or quantitatively assess data assets.

[0028] As can be seen from the aforementioned technologies, existing methods typically treat data assets merely as "tables, fields" or "files, interfaces," lacking the ability to uniformly model business entities such as "customers, orders, contracts, accounts, and products" and their semantic relationships. In various business systems, objects of the same type often exist with different field names and encoding methods. Existing solutions only perform statistics and measurements from the perspective of "technical resources," failing to identify the identity and relationships of these objects at the semantic level. Due to the lack of a unified business ontology model, existing data asset assessment methods struggle to uniformly model cross-system and cross-table data at the semantic level. This results in customer data, order data, contract data, etc., being treated as multiple isolated data sources, failing to form a unified "business entity view," thus making it difficult to accurately assess the overall business value generated by these data combinations. Existing methods typically use statistical indicators based on "single-table dimensions" or "single-subject domain dimensions," lacking the ability to automatically mine relationship links between multiple tables and domains. For example, the multi-hop relationship between customer, account, contract, and transaction generally requires customized ETL or specific report development for use in a particular scenario, rather than being incorporated into the general framework of "data asset valuation." Because existing methods lack reasoning mechanisms based on entity relationships, they cannot automatically identify multi-hop relationships such as "customer-order-product" and "customer-account-contract" and their combination patterns. This makes it difficult to discover high-value patterns and risk clues hidden in complex relationship chains, resulting in many potential data values ​​not being identified and included in asset valuation.

[0029] Furthermore, existing assessment methods lack formal modeling of business rules and indicator calculation formulas, making it impossible to quantify the value of data assets based on their actual business reasoning and decision-making capabilities. For example, whether an order table has the capability to support customer lifetime value (LTV) analysis or can be used for high-risk customer identification lacks a systematic judgment and quantification mechanism, resulting in coarse-grained and inaccurate data asset value assessments. Moreover, existing methods typically only score the "static dataset itself," lacking a quantitative framework for the "incremental data value after introducing knowledge and reasoning"; they cannot measure the contribution of "new knowledge based on knowledge reasoning" to business value, i.e., they cannot compare the "original data asset value before reasoning" and the "extended data asset value after reasoning," thus failing to quantify the effect of using knowledge reasoning to enhance data value.

[0030] In other words, there are two problems in the existing technology: (1) The existing data asset assessment lacks the ability to model cross-system semantic uniformity, making it difficult to uniformly depict business entities and their multi-hop relationship links, resulting in inaccurate assessment results; (2) The existing solutions lack a mechanism to quantify the value contribution of the new labels and implicit relationships introduced by knowledge reasoning, making it impossible to measure the value increment brought by reasoning and difficult to guide data governance.

[0031] In view of this, some embodiments of this application provide a method for data asset valuation. This method first uses an initial knowledge graph constructed from business data as a basis to obtain a first comprehensive value score for the data asset to be valued; then, based on an extended knowledge graph reasoned from the initial knowledge graph, a second comprehensive value score is obtained for the data asset to be valued; finally, the difference between the two scores is used to rank the data assets to be valued, resulting in a value ranking. Embodiments of this application can establish semantic connections between business data through knowledge graphs, forming a unified business entity view, accurately assessing the overall business value generated by the combination of these data; simultaneously, using both the initial knowledge graph and the reasoned extended knowledge graph to comprehensively value the data asset to be valued can leverage knowledge reasoning to improve the accuracy of data asset valuation, achieving both quantitative and qualitative assessment of data assets.

[0032] As can be seen, in order to solve the above problems, the embodiments of this application mainly propose a method for data asset valuation from the following technical perspectives, which mainly includes: 1) Obtain raw business data from multiple business systems and preprocess the raw business data. The preprocessing includes at least cleaning, field standardization, and identifier unification to obtain business data for semantic modeling. 2) Map the business data to a preset business ontology model to generate an initial knowledge graph containing triples of entities, attributes, and relationships. The initial knowledge graph is used to represent the entity association relationships between different types of business data. 3) Based on the initial knowledge graph, identify the data assets to be evaluated and calculate the first comprehensive value score V. before The first comprehensive value score is obtained by weighting at least the assessment indicators of business contribution, data quality, reusability, and risk compliance value. 4) Load the preset reasoning rules, perform logical reasoning on the initial knowledge graph, generate reasoning tags and / or implicit relationships of the target entity, and write the reasoning tags and / or implicit relationships back to the knowledge graph to form an extended knowledge graph; 5) Based on the extended knowledge graph, calculate the second comprehensive value score V. after The second comprehensive value score is obtained by weighting at least the assessment indicators of business contribution, data quality, reusability, and risk compliance value. 6) Calculate the value increment ΔV=V after V before The data assets to be evaluated are ranked according to ΔV, and the value ranking results are output; where ΔV is used to quantify the contribution of knowledge reasoning to the improvement of data asset value.

[0033] The following is in conjunction with the appendix Figure 1The overall structure of a data asset valuation system provided by some embodiments of this application is illustrated.

[0034] like Figure 1 As shown, some embodiments of this application provide a system diagram for data asset valuation. This data asset valuation system may include a business system 100 and an evaluation system 200. The evaluation system 200 can obtain raw business data related to the data assets from the business system 100. Then, after preprocessing the raw business data, the evaluation system 200 constructs an initial knowledge graph based on a pre-built business ontology model. Subsequently, it performs logical reasoning on the initial knowledge graph using reasoning rules to generate an extended knowledge graph. Finally, it scores the data assets to be evaluated using both the initial knowledge graph and the reasoned extended knowledge graph, and then determines the value ranking of the data assets by the difference between the two scores. There may be multiple data assets to be evaluated.

[0035] In some embodiments of this application, multiple business systems 100 may be configured, each containing different types of business data. The evaluation system 200 may be deployed in a terminal device, which may be a mobile terminal, a non-portable computer terminal, or a server terminal; specific limitations are not made here.

[0036] In addition, this application uses data ontology modeling when constructing the knowledge graph. In the model, the attributes, categories and relationships between data can be defined. The ontology data is reasoned through an inference engine to mine the value of the data and provide a basis for the value of the data from more dimensions, rather than being limited to the value assessment at the level of data assets.

[0037] The following is in conjunction with the appendix Figure 2 The present application provides an exemplary embodiment of the data asset valuation process performed by the valuation system 200.

[0038] Please see the appendix Figure 2 , Figure 2 A flowchart illustrating a method for data asset valuation is provided for some embodiments of this application. This data asset valuation method may include: S210, based on the initial knowledge graph, obtain the first comprehensive value score of the data asset to be evaluated; wherein, the initial knowledge graph is constructed based on business data; the initial knowledge graph represents the entity relationship between different types of business data; the business data includes customer data, order data, contract data, account data, product data, store data or indicator data.

[0039] For example, in a specific embodiment of this application, the evaluation system 200 includes a value evaluation module. Using an initial knowledge graph as input, the system scores various data assets within the data assets to be evaluated according to a preset value evaluation process, resulting in a first comprehensive value score V. before The data assets to be evaluated can be categorized into customer data assets, contract data assets, order data assets, and relationship data assets.

[0040] In some embodiments of this application, S210 may include: determining a first evaluation indicator for the data asset to be evaluated; wherein the type of the first evaluation indicator includes business contribution, data quality, reusability and risk compliance value; and performing a weighted calculation on the first evaluation indicator and its corresponding weight to obtain the first comprehensive value score.

[0041] For example, in a specific embodiment of this application, the evaluation indicators set for each type of data asset in the value assessment module include business contribution B, data quality Q, reusability R, and risk compliance value K. Business contribution B indicates how many business scenarios the data asset is used in, how many inference rules it supports, and how many inference tags depend on it; data quality Q is an indicator related to the completeness, consistency, accuracy, and timeliness of the data asset; reusability R is determined based on the number of systems called and the interface encapsulation; and risk compliance value K is obtained based on the proportion of sensitive data and the strength of compliance constraints. Then, according to preset weights, a comprehensive value score for each type of data asset is calculated, using the following formula:

[0042] in, These are the weights corresponding to each evaluation indicator.

[0043] In addition to being preset manually, the aforementioned weighting coefficients can also be used to construct a fitting model using historical evaluation data and actual business performance data. The fitting model can then be automatically solved using regression or optimization algorithms to minimize the deviation between the comprehensive value score and the actual business performance, thereby quantifying and determining the weight of each indicator.

[0044] It is understandable that the first comprehensive value score V can be obtained through the above formula. before .

[0045] S220, based on the extended knowledge graph after reasoning from the initial knowledge graph, obtain the second comprehensive value score of the data asset to be evaluated.

[0046] For example, in a specific embodiment of this application, an extended knowledge graph is used as the input to the value assessment module. Following a preset value assessment process, various data assets within the data assets to be assessed are scored to obtain a second comprehensive value score V. after .

[0047] In some embodiments of this application, S210 may include: determining a second evaluation indicator for the data asset to be evaluated; wherein the type of the second evaluation indicator includes business contribution, data quality, reusability and risk compliance value; and performing a weighted calculation on the second evaluation indicator and its corresponding weight to obtain the second comprehensive value score.

[0048] For example, in a specific embodiment of this application, the second comprehensive value score V can be obtained through the above calculation formula. after .

[0049] S230, the data assets to be evaluated are sorted by the difference between the first comprehensive value score and the second comprehensive value score to obtain the value ranking result of the data assets to be evaluated.

[0050] For example, in a specific embodiment of this application, the value assessment module calculates the first comprehensive value score V of various data assets under both the initial knowledge graph and the extended knowledge graph states. before Second comprehensive value score V after And calculate the difference ΔV=V based on the two. after -V before The difference ΔV is used to quantify the degree to which ontology reasoning enhances the value of various data assets. The value assessment module ranks the data assets to be assessed based on ΔV to obtain the value ranking results, in order to identify key data assets whose value has been significantly enhanced after the introduction of ontology reasoning.

[0051] The value ranking result can then be output to the result display and interface module of the evaluation system 200 to guide data governance priorities and resource allocation strategies. The difference ΔV can also be used as a feedback signal to optimize the inference rule set and / or the weight parameters of the value scoring model in the evaluation system 200.

[0052] The following example illustrates the knowledge graph construction process in the above implementation.

[0053] In some embodiments of this application, the initial knowledge graph is obtained through the following steps: obtaining raw business data from multiple business systems; preprocessing the raw business data to obtain the business data; mapping the business data to a preset business ontology model to obtain the initial knowledge graph; wherein the business ontology model includes entity classes and relationship types between entities.

[0054] For example, in a specific embodiment of this application, the evaluation system 200 collects raw business data, including customers, orders, contracts, accounts, products, stores, etc., from one or more business systems. The collected raw business data is cleaned, its fields are standardized, and its ID is unified (as a specific example of preprocessing). The same business object (e.g., the same customer) from different business systems is mapped to a unified internal identifier, thus obtaining business data under the same business object. Based on a preset business ontology model, the collected business data is mapped to ontology entity classes, attributes, and relationships (i.e., relationship types). Specifically, customer data is used to generate customer entities, order data to generate order entities, contract data to generate contract entities, etc.; the "order placement" relationship between customers and orders, the "inclusion" relationship between orders and products, and the "constraint" relationship between contracts and accounts are generated into corresponding triples. The generated entities, attributes, and relationships are stored in the knowledge graph storage module of the evaluation system 200 to form an initial knowledge graph.

[0055] In some embodiments of this application, the extended knowledge graph is obtained through the following steps: loading preset reasoning rules; reasoning on the initial knowledge graph based on the preset reasoning rules to generate reasoning tags and associations of target entities; adding the reasoning tags and associations to the initial knowledge graph to obtain the extended knowledge graph.

[0056] For example, in a specific embodiment of this application, preset reasoning rules are read from the indicator and rule management module of the evaluation system 200. The ontology reasoning module of the evaluation system 200 performs pattern matching on entities and their relationships in the initial knowledge graph based on the reasoning rules, and generates reasoning tags and associations based on the matching results. For example, for customer entities that meet the criteria of "consumption amount ≥ threshold and risk score ≤ threshold", a "high-value customer" tag is added; for contract entities with overdue days greater than the threshold, an "overdue contract" or "high-risk contract" tag is added; for customer entities that have a "customer-use-account-constrained-contract" relationship chain with high-risk contracts, a "risk-related customer" tag is added. The ontology reasoning module writes the new reasoning tags generated by the above reasoning and / or the new implicit relationships (i.e., associations) generated by reasoning back to the initial knowledge graph in the knowledge graph storage module in the form of triples, forming an extended knowledge graph containing the reasoning results.

[0057] In some embodiments of this application, after performing S230, the method for assessing the value of data assets may further include generating a visualization result that matches the value ranking result; wherein the visualization result is represented using multiple types of graphics; displaying the visualization result, or sending the visualization result to an external data platform.

[0058] For example, in a specific embodiment of this application, the result display and interface module of the evaluation system 200 can generate and display the comprehensive value score, value ranking result, and value increment before and after inference for each type of data asset, so as to present them to the user. The visualization results can be displayed using various types of charts and reports, which are not specifically limited in this embodiment of the application. In addition, the visualization results can also be sent to a data governance platform or business system (as a specific example of an external data platform) through an interface for the purpose of formulating data governance priorities, resource allocation strategies, or risk control strategies.

[0059] The following is in conjunction with the appendix Figure 3 The present application provides an exemplary framework for data asset valuation based on some embodiments.

[0060] Please see the appendix Figure 3 , Figure 3 This is a schematic diagram illustrating the structural framework for data asset valuation, provided for some embodiments of this application. Figure 3 As can be seen, the data asset valuation architecture includes a data acquisition module, an ontology construction module, a knowledge graph storage module, an indicator and rule management module, an ontology reasoning module, a value assessment module, and a result display and interface module.

[0061] The specific functions of each module are illustrated below.

[0062] The data acquisition module is used to obtain raw business data from multiple business systems. This raw business data includes customer data, order data, contract data, account data, product data, store data, and performance indicator data. Specifically, the data acquisition module may include an interface adaptation unit and an ETL unit. The interface adaptation unit communicates with external business systems via REST APIs, message queues, and database connections, importing raw business data in batch or real-time streaming formats. The ETL unit performs preprocessing operations on the raw data, such as format conversion, cleaning, and field mapping, transforming it into an internally unified data format to obtain business data for subsequent knowledge graph construction. Afterward, the data acquisition module sends the processed business data to the ontology construction module.

[0063] The ontology construction module is used to map the collected business data into entity classes, attributes and relationships in the ontology based on a preset business ontology model, and to build an initial knowledge graph.

[0064] The ontology construction module includes an ontology model storage unit, a mapping configuration unit, an entity generation unit, and a relation generation unit. The ontology model storage unit pre-stores the business ontology model, defining entity classes such as customer, order, contract, account, product, store, and indicator, as well as relation types such as "place order," "contains," "constraint," and "payment." The mapping configuration unit stores field mapping rules; for example, mapping the "cust_id" field in the CRM to a customer entity identifier in the ontology, and mapping "user_id" in the transaction system to the same customer class entity. The entity generation unit extracts entities from business data according to the mapping rules, generating an entity set. The relation generation unit generates triples (h, r, t) based on business primary key associations and configured relation rules, forming an initial knowledge graph. Afterward, the ontology construction module sends the initial knowledge graph to the knowledge graph storage module for interaction with the indicator and rule management module to obtain attribute definitions and rule references from the ontology.

[0065] The knowledge graph storage module stores the initial knowledge graph generated by the ontology construction module, including entities, attributes, and relations. This module can be implemented using a graph database (such as Neo4j or JanusGraph), an RDF triple repository (such as Jena or Virtuoso), or a relational database with a graph structure index. It provides a query interface supporting queries by entity ID, relation type, and attribute conditions. The knowledge graph storage module can also provide knowledge graph data to the ontology reasoning module for subsequent write-back operations of the extended knowledge graph after reasoning.

[0066] The ontology reasoning module performs logical reasoning on the initial knowledge graph based on preset reasoning rules, automatically generating derived tags and implicit relationships to obtain an extended knowledge graph. The ontology reasoning module includes a rule engine unit, a pattern matching unit, and a reasoning result generation unit. The rule engine unit loads reasoning rules stored in the indicator and rule management module, such as "high-value customer rules," "high-risk contract rules," and "risk-related customer rules." The pattern matching unit searches the initial knowledge graph for entities and relationship patterns that satisfy the antecedent conditions of the rules, such as: a customer entity's consumption amount attribute ≥ a threshold, and a risk score ≤ a threshold; a relationship chain of customer – usage – account – constrained – high-risk contract. The reasoning result generation unit adds tags or attributes to the corresponding entities based on the consequents of the rules, or adds new implicit relationship triples, such as: adding a "high-value customer" tag to customer entities that meet the conditions; adding a "high-risk contract" tag to contract entities that meet the conditions; adding a "risk-related customer" tag to customer entities that have an indirect relationship with high-risk contracts.

[0067] Understandably, the ontology reasoning module reads graph data from the knowledge graph storage module, writes the new entity attributes and new relationships after reasoning back to the knowledge graph storage module, and finally sends the reasoning result metadata (such as the types and quantities of new tags) to the value assessment module.

[0068] The Indicator and Rule Management module is used to uniformly manage the definitions and inference rules of business indicators related to data asset valuation. This module includes indicator definition units, rule definition units, threshold configuration units, and weight configuration units. The indicator definition unit defines indicators such as business contribution (B), data quality (Q), reusability (R), and risk compliance value (K), as well as business indicators such as customer lifetime value (LTV), average order value, and bad debt rate. The rule definition unit stores inference rules in a structured format, such as rules for identifying high-value customers and high-risk contracts, and provides a graphical interface or configuration file for business personnel to edit. The threshold configuration unit configures the thresholds used in calculating each indicator, such as consumption amount thresholds and risk scoring thresholds. The weight configuration unit configures the weight coefficients of each indicator in the data asset valuation formula. .

[0069] This indicator and rule management module can provide attribute and indicator definition references for the ontology construction module, provide inference rules for the ontology reasoning module, and provide indicator definitions and weight configurations for the value assessment module.

[0070] The value assessment module is used to evaluate and compare the value of various data assets based on the knowledge graph before and after inference and a preset comprehensive value scoring formula. The value assessment module includes a data asset identification unit, an indicator calculation unit, a scoring calculation unit, and an incremental analysis unit. The data asset identification unit maps database tables, subject domains, or entity sets to individual data asset units to be evaluated (such as "customer data assets," "order data assets," "customer-contract relationship data assets," etc.) according to the business ontology model. The indicator calculation unit calculates the business contribution B of each type of data asset (e.g., the number of inference rules supported by the asset, the types of business tags that can be generated, and its usage in actual business scenarios); calculates data quality indicators Q (completeness, consistency, timeliness, etc.); assesses reusability R (how many systems / scenarios reuse it, whether it is packaged as a service, etc.); and assesses the risk compliance value K (the proportion of sensitive data involved, the degree of compliance control, etc.). The scoring calculation unit contains the aforementioned comprehensive value scoring formula to calculate the comprehensive value score for each type of data asset. The incremental analysis unit performs value assessments before and after ontology reasoning, obtaining ΔV, which serves as the incremental value brought by business ontology reasoning. Subsequently, it can combine newly identified entity sets (e.g., new high-value customer sets) and their business benefits to calculate the incremental business value. Specifically, the value assessment module obtains the original and inferred data states (i.e., the initial and extended knowledge graphs) from the knowledge graph storage module and the ontology reasoning module; obtains indicator definitions and weights from the indicator and rule management module; and outputs the value assessment results (i.e., value ranking results) to the results display and interface module.

[0071] The Results Display and Interface module is used to present the valuation results of the data assets to be evaluated to users and provides interfaces for external systems to call. This module includes a graphical display unit and an interface service unit. The graphical display unit displays the value scores, rankings, and grading results of various data assets in the form of reports and dashboards, as well as the value increment before and after inference. The interface service unit provides API services to external data governance platforms, data middleware, or business systems, returning data asset value scores and tags for resource allocation, priority management, or decision support. Specifically, the Results Display and Interface module can obtain value assessment results from the value assessment module and interact with external data platforms.

[0072] As can be seen from the embodiments of this application described above, this application achieves integrated semantic expression of cross-system data by constructing an initial knowledge graph, enabling data asset assessment to be based on "business entities and relationships" rather than just "technical tables," thus improving the accuracy of the assessment. Furthermore, this application expands the dimensions of available information without increasing the cost of original data collection, discovering more potential value points and increasing the depth of data value mining. The indicator and rule management module manages business indicators and inference rules in a structured form, serving as input to the value assessment module, enabling the assessment to reflect the data asset's "ability to support advanced analysis / decision-making," and making the assessment results closer to actual business value. The value assessment module calculates V before and after inference. before With V after The difference ΔV is calculated to quantify the value enhancement brought by ontology reasoning. This allows for an objective measurement of the benefits of introducing ontology reasoning mechanisms and provides a quantitative basis for data governance investment and technology solution selection.

[0073] Please refer to Figure 4 , Figure 4 The diagram illustrates the composition of an apparatus for data asset valuation according to some embodiments of this application. It should be understood that this data asset valuation apparatus corresponds to the method embodiments described above and is capable of performing the various steps involved in the method embodiments. The specific functions of this data asset valuation apparatus can be found in the description above; detailed descriptions are omitted here to avoid repetition.

[0074] Figure 4 The data asset valuation apparatus includes at least one software functional module that can be stored in a memory or embedded in the data asset valuation apparatus in the form of software or firmware. The apparatus includes: a first scoring module 410, used to obtain a first comprehensive value score of the data asset to be valued based on an initial knowledge graph; wherein the initial knowledge graph is constructed based on business data; the initial knowledge graph represents entity relationships between different types of business data; the business data includes customer data, order data, contract data, account data, product data, store data, or indicator data; a second scoring module 420, used to obtain a second comprehensive value score of the data asset to be valued based on an extended knowledge graph derived from the initial knowledge graph; and a value valuation module 430, used to rank the data asset to be valued based on the difference between the first comprehensive value score and the second comprehensive value score, to obtain a value ranking result for the data asset to be valued.

[0075] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the aforementioned method, and will not be elaborated further here.

[0076] Some embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can perform the operation of any of the methods corresponding to the methods provided in the above embodiments.

[0077] Some embodiments of this application also provide a computer program product, which includes a computer program, wherein when the computer program is executed by a processor, it can implement the operation of any of the methods corresponding to the above embodiments provided in the above embodiments.

[0078] like Figure 5 As shown, some embodiments of this application provide an electronic device 500, which includes a memory 510, a processor 520, and a computer program stored in the memory 510 and executable on the processor 520. When the processor 520 reads the program from the memory 510 via a bus 530 and executes the program, it can implement the methods of any of the above embodiments.

[0079] Processor 520 can process digital signals and can include various computing architectures. For example, it can be a complex instruction set computer architecture, a reduced instruction set computer architecture, or an architecture that implements multiple instruction set combinations. In some examples, processor 520 can be a microprocessor.

[0080] The memory 510 can be used to store instructions executed by the processor 520 or data related to the execution of instructions. These instructions and / or data may include code for implementing some or all of the functions of one or more modules described in the embodiments of this application. The processor 520 of this disclosure embodiment can be used to execute the instructions in the memory 510 to implement the methods shown above. The memory 510 includes dynamic random access memory, static random access memory, flash memory, optical memory, or other memories well known to those skilled in the art.

[0081] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0082] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0083] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method of data asset value assessment, characterized by, include: Based on the initial knowledge graph, a first comprehensive value score is obtained for the data asset to be evaluated; wherein, the initial knowledge graph is constructed based on business data; The initial knowledge graph represents the entity relationships between different types of business data; the business data includes customer data, order data, contract data, account data, product data, store data, or indicator data. Based on the extended knowledge graph after reasoning from the initial knowledge graph, a second comprehensive value score is obtained for the data asset to be evaluated. The data assets to be evaluated are ranked by the difference between the first comprehensive value score and the second comprehensive value score, and the value ranking result of the data assets to be evaluated is obtained.

2. The method of claim 1, wherein, The process of obtaining the first comprehensive value score of the data asset to be evaluated includes: A first evaluation indicator is determined for the data asset to be evaluated; wherein, the type of the first evaluation indicator includes business contribution, data quality, reusability, and risk compliance value. The first comprehensive value score is obtained by weighting the first evaluation index and its corresponding weight.

3. The method of claim 1 or 2, wherein, The process of obtaining the second comprehensive value score of the data asset to be evaluated includes: A second evaluation metric is determined for the data asset to be evaluated; wherein, the type of the second evaluation metric includes business contribution, data quality, reusability, and risk compliance value; The second comprehensive value score is obtained by weighting the second evaluation index and its corresponding weight.

4. The method of claim 1 or 2, wherein, The extended knowledge graph was obtained through the following steps: Load preset inference rules; Based on the preset reasoning rules, reasoning is performed on the initial knowledge graph to generate reasoning tags and associations for the target entities; The reasoning tags and the relationships are added to the initial knowledge graph to obtain the extended knowledge graph.

5. The method of claim 1 or 2, wherein, The initial knowledge graph was obtained through the following steps: Obtain raw business data from multiple business systems; The original business data is preprocessed to obtain the business data; The business data is mapped to a preset business ontology model to obtain the initial knowledge graph; wherein, the business ontology model includes entity classes and relationship types between entities.

6. The method of claim 1 or 2, wherein, After obtaining the value ranking result of the data asset to be evaluated, the method further includes: Generate visualization results that match the value ranking results; wherein the visualization results are represented using multiple types of graphics; Display the visualization results, or send the visualization results to an external data platform.

7. An apparatus for data asset value assessment, the apparatus comprising: include: The first scoring module is used to obtain a first comprehensive value score of the data asset to be evaluated based on the initial knowledge graph; wherein, the initial knowledge graph is constructed based on business data; the initial knowledge graph represents the entity relationship between different types of business data; the business data includes customer data, order data, contract data, account data, product data, store data or indicator data; The second scoring module is used to obtain the second comprehensive value score of the data asset to be evaluated based on the extended knowledge graph after reasoning from the initial knowledge graph. The value assessment module is used to sort the data assets to be assessed by the difference between the first comprehensive value score and the second comprehensive value score, and obtain the value ranking result of the data assets to be assessed.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is executed by a processor to perform the method as described in any one of claims 1-6.

9. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored on the memory and running on the processor, wherein the computer program is executed by the processor to perform the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program, wherein the computer program is executed by a processor to perform the method as described in any one of claims 1-6.

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