A knowledge graph-based back-tracing data full-life-cycle automatic management system

Abstract

The application discloses a kind of based on knowledge graph's back data full life cycle automatic management system, it is related to data governance and background investigation technical field.System includes: data access module, back knowledge graph construction module, intelligent governance module, full life cycle scheduling module, interface service module and security integration module.Through automatic learning algorithm, back data is realized from multi-source collection, intelligent cleaning, semantic annotation, conflict resolution, dynamic update to the full life cycle automation management of safe archiving;Through flow rule dynamic optimization algorithm, data processing strategy is self-adaptively adjusted, to solve the problems such as traditional back data fragmentation, correlation missing, update lag, iteration inefficiency, low credibility, compliance traceability difficult, etc.The present application can be widely applied to human resources back, enterprise due diligence and other scenes, and significantly improves the efficiency of back data governance, data quality and business compliance level.

Description

Technical Field

[0001] This invention relates to the fields of data governance, knowledge graphs, natural language processing, and background investigation technologies, specifically to an automated management system for the entire lifecycle of background investigation data based on knowledge graphs. Background Technology

[0002] Background checks are widely used in various high-compliance scenarios, such as human resource onboarding reviews, corporate due diligence, and public official qualification reviews. The core data in these checks is characterized by diverse sources, heterogeneous structures, complex relationships, time sensitivity, and stringent compliance requirements. Current technologies in background check data management have not yet formed an integrated intelligent governance system covering the entire data lifecycle, and generally suffer from the following prominent deficiencies, seriously affecting the efficiency, quality, and compliance of background check operations:

[0003] 1) Data fragmentation is a prominent issue. Background check data from multiple sources (such as resume information, academic certificates, work experience, reward and punishment records, risk events, etc.) are stored in isolation in different systems or databases, lacking a unified link. This makes it impossible to form a global view of the relationship between personnel, organizations, certificates, resumes, and risks, resulting in low data reuse rate, difficulty in correlation analysis, and easy problems of information omission or duplicate verification.

[0004] 2) The data processing workflow is highly dependent on manual intervention. From formatting and cleaning abnormal data after multi-source data collection, to entity information labeling and resolving conflicts between multi-source data, and then to regular data updates and standardized archiving, all of these require manual operation. This not only results in low processing efficiency and is time-consuming and labor-intensive, but also makes it easy for human error to lead to poor data consistency and high error rate, making it difficult to meet the high-efficiency processing needs of large-scale background check business.

[0005] 3) The lack of a unified knowledge model for the background check domain leads to inconsistent definitions of core entities such as personnel, organizations, and documents, resulting in frequent ambiguities such as identical names referring to different entities and different names referring to the same entity. At the same time, conflicts between multiple data sources cannot be effectively resolved, resulting in insufficient credibility of the background check data and affecting the accuracy of the background check conclusions.

[0006] 4) The lifecycle management strategy is rigid and inflexible. The flow rules such as data update cycle, cleaning threshold, and labeling granularity cannot be adaptively adjusted according to data quality, business processing efficiency, compliance requirements and system resource consumption. This results in some time-sensitive data being updated late and some low-frequency data excessively occupying system resources, which not only affects the business experience but also increases the system operating cost.

[0007] 5) Weak compliance traceability capabilities, lack of operation traceability, access control and compliance auditing mechanisms for the entire lifecycle of background check data, and some sensitive background check data (such as personal identity information and privacy information) have not been properly anonymized, which may lead to data leakage and misuse.

[0008] 6) Poor knowledge reusability: The entities and relationships in the background check data have not been systematically sorted and stored, making it impossible to form a reusable background check knowledge system. Each time a background check is carried out, data needs to be collected and processed again, which greatly increases the business redundancy cost.

[0009] In summary, existing technologies have not formed an autonomous management system centered on knowledge graphs and covering the entire data lifecycle, and cannot effectively solve the above-mentioned defects. Therefore, there is an urgent need in this field for a system that can realize intelligent management of background check data from collection to archiving, while taking into account efficiency, quality and compliance. Summary of the Invention

[0010] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an automatic management system for the entire lifecycle of background check data based on knowledge graphs. By constructing a unified data association system through knowledge graphs and realizing intelligent governance of the entire data lifecycle through automatic learning algorithms, this invention solves problems such as data fragmentation, inefficient processing, missing associations, and difficulty in tracing.

[0011] To achieve the above objectives, the present invention adopts the following technical solution:

[0012] An automated management system for the entire lifecycle of background check data based on knowledge graphs includes: a data access module, a background check knowledge graph construction module, an intelligent governance module, a lifecycle scheduling module, and an interface service module. The data access module is used for the collection, parsing, and format unification of multi-source heterogeneous background check data. The background check knowledge graph construction module is used to extract entities, relationships, and attributes from standardized data to construct and store a background check domain knowledge graph. The intelligent governance module is configured with automatic learning algorithms for data cleaning, entity alignment, semantic annotation, and conflict resolution. The lifecycle scheduling module is used to perform closed-loop scheduling of background check data collection, cleaning, annotation, updating, archiving, and destruction, and dynamically optimize data flow rules. The interface service module provides interfaces for data query, graph retrieval, process monitoring, and access control.

[0013] Furthermore, the data access module includes a structured data adapter, an unstructured data parsing unit, and a data standardization unit. The standardization unit uniformly converts heterogeneous data into a standard structure that can be used for knowledge extraction.

[0014] Furthermore, the unstructured data parsing unit uses a hybrid extraction method combining pre-trained models and rules to automatically identify personnel entities, organizational entities, document information, time elements, address elements, and risk description information.

[0015] Furthermore, the background check knowledge graph construction module includes an ontology definition unit, a triple generation unit, and a graph database storage unit. The ontology definition unit pre-defines six core ontology categories: personnel, certificates, organizations, educational background, work experience, and risk events.

[0016] Furthermore, the background check knowledge graph construction module supports incremental updates, and when the data source changes, it only performs extraction, matching, fusion and verification on the difference subgraph.

[0017] Furthermore, the intelligent governance module performs entity alignment and conflict resolution, employs a multi-dimensional weighted fusion algorithm to automatically disambiguate homonymous and heteronymous entities, and corrects the confidence level of multi-source conflict data.

[0018] Furthermore, the intelligent governance module performs a comprehensive score based on the credibility of the data source, update time, and consistency of association. Data that is below the cleaning threshold is automatically marked and enters the review process.

[0019] Furthermore, the full lifecycle scheduling module includes a data update engine and an archiving strategy unit. The update engine performs real-time updates, scheduled updates, or incremental updates based on data timeliness scores and business event triggers.

[0020] Furthermore, the full lifecycle scheduling module incorporates a dynamic optimization algorithm for flow rules, which adaptively adjusts the cleaning threshold, labeling granularity, update cycle, and archiving conditions based on historical processing success rate, data quality, time consumption cost, and compliance level.

[0021] Furthermore, it also includes a security and compliance module, which is used for data anonymization, operation logging, hierarchical access control, and compliance audit log recording to achieve full lifecycle traceability and auditability of background check data.

[0022] This invention provides an automated management system for the entire lifecycle of background check data based on knowledge graphs, which, compared with existing technologies:

[0023] 1) Effectively solve the problem of data fragmentation. By constructing a knowledge graph in the background investigation domain, it connects and integrates isolated data from multiple sources such as personnel, certificates, organizations, education experience, work experience, and risk events to form a globally unified knowledge relationship view, breaking down data silos, significantly improving data reuse rate, avoiding information omissions and duplicate verifications, and reducing business redundancy costs.

[0024] 2) To solve the problems of inefficiency and error-proneness of manual processing, the automatic learning algorithm realizes the full-process automation of background check data from collection, cleaning, labeling, alignment, conflict resolution to updating and archiving, which greatly reduces human intervention, not only significantly improves data processing efficiency, but also effectively reduces human operation errors and ensures the consistency and accuracy of background check data.

[0025] 3) To address the issues of entity ambiguity and insufficient data credibility, a unified ontology model for the background check domain is pre-set. Combined with an original multi-dimensional weighted fusion algorithm, automatic entity disambiguation is achieved. Combined with data confidence assessment and conflict resolution algorithms, multi-source data conflicts are handled, significantly improving the credibility and accuracy of background check data and providing reliable data support for background check conclusions.

[0026] 4) To address the issue of rigid lifecycle management, an original dynamic optimization algorithm for data flow rules is used. Based on data quality scores, process execution efficiency, system resource consumption, and compliance and security indices, the algorithm adaptively adjusts data cleaning thresholds, labeling granularity, update cycles, and archiving conditions to achieve self-evolution of data flow rules. This balances business efficiency and system costs, ensuring timely updates of time-sensitive data and standardized archiving of low-frequency data.

[0027] 5) Address the issue of weak compliance traceability capabilities by implementing full lifecycle anonymization, operation tracking, hierarchical access control, and compliance auditing of background check data through a security and compliance module. This strictly protects personal privacy and sensitive information, ensuring full traceability and auditability of data usage and reducing compliance risks.

[0028] 6) Enhance knowledge reuse capabilities: By systematically organizing and structuring the entities and relationships in background check data through knowledge graphs, a reusable background check knowledge system is constructed. Subsequent background check operations can directly utilize existing knowledge, reducing redundant data collection and processing steps, further lowering business costs and improving efficiency. This invention is widely applicable to various background check scenarios, including human resources and corporate due diligence, and has strong practicality and promotional value. Attached Figure Description

[0029] Figure 1 A block diagram of the overall system architecture provided by the present invention;

[0030] Figure 2 A flowchart of the full lifecycle execution process provided by this invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0032] Example 1: System Overall Architecture

[0033] like Figure 1 The system includes: a data access module, a background check knowledge graph construction module, an intelligent governance module, a full lifecycle scheduling module, an interface service module, and a security and compliance module.

[0034] 1) Data access module: Supports structured, unstructured, and semi-structured data access, and outputs unified standard data.

[0035] 2) Background Check Knowledge Graph Construction Module: Ontology defines personnel, certificates, organizations, educational background, work experience, and risk events; relationships include: employed at, attended, affiliated with, held, involved in, and associated with; knowledge is stored in triples (subject, predicate, object).

[0036] 3) Intelligent governance module: Execution: Data cleaning → Entity alignment → Conflict resolution → Semantic annotation.

[0037] 4) Full lifecycle scheduling module: schedules the entire process and dynamically optimizes the rules.

[0038] 5) Interface service module: Provides standardized interfaces to external users.

[0039] 6) Security and compliance module: data anonymization, operation tracking, access control, and compliance auditing.

[0040] Example 2 provides the core algorithm.

[0041] Algorithm 1: Multi-dimensional weighted fusion alignment algorithm for background check entities

[0042] Fmerge = ω1·Sname + ω2·Sattr + ω3·Stime + ω4·Srel

[0043] Meaning of each symbol:

[0044] Fmerge: Entity Integration Score

[0045] ω1: Name similarity weight

[0046] Sname: Name string similarity

[0047] ω2: Attribute matching weight

[0048] Sattr: Key attribute matching degree

[0049] ω3: Time Consistency Weight

[0050] Stime: Time Information Consistency

[0051] ω4: Relationship structure weights

[0052] Srel: Adjacency relationship structural similarity

[0053] ω1+ω2+ω3+ω4=1

[0054] When Fmerge ≥ Tf, they are determined to be the same entity and automatically merged.

[0055] Algorithm 2: Comprehensive Confidence Evaluation Algorithm for Background Check Data

[0056] Cdata = λ1·Rsrc + λ2·Tage + λ3·Mcon + λ4·Kcon

[0057] Meaning of each symbol:

[0058] Cdata: Overall Data Confidence

[0059] λ1: Source credibility weight

[0060] Rsrc: Official credibility rating of the data source

[0061] λ2: Data timeliness weight

[0062] Tage: Time decay score

[0063] λ3: Multi-source consistency weight

[0064] Mcon: Multi-source data consistency rate

[0065] λ4: Graph constraint weights

[0066] Kcon: Logical Conflict Rate in Knowledge Graphs

[0067] λ1+λ2+λ3+λ4=1

[0068] If the value is below the threshold Tc, the system will automatically enter the exception handling phase.

[0069] Algorithm 3: Optimization Algorithm for Dynamically Optimizing Data Flow Rules

[0070] Orule = μ1·Qdata + μ2·Erun - μ3·Ccost + μ4·Gsafe

[0071] Meaning of each symbol:

[0072] Orule: Rule-based comprehensive optimization score

[0073] μ1: Data quality weight

[0074] Qdata: Data Quality Score

[0075] μ2: Execution efficiency weight

[0076] Erun: Process execution efficiency

[0077] μ3: Operating cost weight

[0078] Ccost: System resource consumption

[0079] μ4: Security compliance weight

[0080] Gsafe: Compliance and Security Index

[0081] μ1+μ2+μ3+μ4=1

[0082] If the score is below the threshold, rule optimization will be automatically triggered.

[0083] The calculation / scoring rules for Tage (time decay score) and Kcon (knowledge graph logical conflict rate) are as follows:

[0084] By combining the time-sensitive nature of background check data, the logical association constraints of knowledge graphs, and the design logic of the core algorithms in the documents, we designed adaptive calculation / scoring rules for Tage and Kcon. The scores are all mapped to the 0~1 range (1 is the best and 0 is the worst), which is consistent with the scoring rules of other parameters in the background check data confidence comprehensive evaluation algorithm, ensuring the effectiveness of the algorithm's weighted summation.

[0085] I. Tage (Time Decay Score) Calculation Rules

[0086] Core design logic

[0087] The timeliness of background check data is strongly correlated with the data type. Dynamic data such as work history and risk events are much more time-sensitive than basic identity information. Therefore, a categorized time decay model is adopted, which combines the time difference between the data collection / update time and the current time with the data type-specific decay coefficient to calculate the score. The more recent the data update time, the higher the Tage score. The decay coefficient of basic identity data is much lower than that of dynamic resumes and risk data, and the decay rate is slower.

[0088] 1. Core Parameter Definitions

[0089] Tnow is the current time when the system performs confidence assessment, accurate to the day; Tupdate is the update time of the last collection or verification of background check data, accurate to the day; ΔT is the time difference, obtained by subtracting Tupdate from Tnow, in days; αtype is the data type decay coefficient, ranging from 0.0005 to 0.01, with a smaller coefficient indicating slower data decay; Sbase is the base score, fixed at 1.

[0090] 2. Data type and corresponding attenuation coefficient

[0091] Background check data is divided into three categories, each matched with a different decay coefficient: Basic identity data includes name, ID number, registered address, document number, etc., with a decay coefficient αtype of 0.0005. This type of data is valid for a long time with almost no decay. Static resume data includes academic qualifications, graduating institution, professional qualification certificates, etc., with a decay coefficient αtype of 0.002. This type of data is of medium validity and only becomes invalid when the information changes. Dynamic / risk data includes work history, reward and punishment records, risk events, credit information, etc., with a decay coefficient αtype of 0.01. This type of data is highly time-sensitive, and its reference value will decrease rapidly over time.

[0092] 3. Calculation Formula and Result Correction

[0093] The formula for calculating Tage is Tage = Sbase - αtype × ΔT, which is Tage = 1 - αtype × ΔT. The calculation result is also corrected: if the calculated Tage value is less than 0, then Tage = 0 is directly taken; if ΔT is 0, meaning the data is in the latest update state, then Tage = 1 is directly taken.

[0094] 4. Example Calculation

[0095] Example 1: A background check data is a work history, belonging to the dynamic / risk category, with αtype set to 0.01. The time difference ΔT between its last update time and the current time is 60 days. The calculated Tage = 1 - 0.01 × 60 = 0.4.

[0096] Example 2: The background check data is an ID card number, which belongs to the basic identity category. The αtype is 0.0005, and the time difference ΔT is 365 days. The calculated Tage = 1 - 0.0005×365 = 0.8175.

[0097] II. Kcon (Knowledge Graph Logical Conflict Rate) Scoring Rules

[0098] Core design logic

[0099] Kcon is an inverse score of the logical conflict rate of the knowledge graph. The lower the conflict rate, the higher the Kcon score, and vice versa. This score is calculated based on the number of logical conflict nodes between the newly added data and the entities, relations, and attributes already stored in the knowledge graph. It is constrained by the six core ontology categories and preset relations of the knowledge graph to determine the logical contradiction between the new data and the existing data in the graph. When there is no conflict, the Kcon score is 1.

[0100] 1. Core Parameter Definitions

[0101] Nconf represents the number of logically conflicting nodes, indicating the number of entities, relationships, and attribute nodes that logically contradict each other in the new data and the knowledge graph. Deduplication is performed during the statistics. Ntotal represents the total number of associated nodes, indicating the total number of all related entities, relationships, and attribute nodes matched by the new data in the knowledge graph. Deduplication is performed during the statistics. Krate represents the logical conflict rate, calculated by dividing Nconf by Ntotal.

[0102] 2. Criteria for Determining Logical Conflict Nodes

[0103] Based on actual background check scenarios, core logical conflicts are categorized into four types. A new data node is considered a conflict node if it meets any of the following conditions: First, attribute contradiction, which refers to inconsistencies in the core attributes of the same person entity, such as inconsistent ID numbers or contradictory birth dates; second, relationship contradiction, which refers to logical conflicts in the relationships between entities, such as the company being deregistered during the employee's marked employment period, or the corresponding university not having a relevant major during the employee's marked study period; third, time contradiction, which refers to conflicts in the time dimensions of resumes and events, such as two completely overlapping work periods for the same person, or a risk event occurring before the employee's employment period; and fourth, organizational association contradiction, which refers to a lack of actual evidence of the relationship between the entity and the organization, such as an employee being marked as holding a certificate issued by a certain institution, but the institution in the data map does not have the authority to issue such certificates.

[0104] 3. Scoring Formula and Handling of Special Cases

[0105] The Kcon scoring formula is Kcon = 1 - Krate, which can also be converted to Kcon = 1 - (Nconf / Ntotal). Special cases are handled separately: if the new data is a newly added unrelated node in the graph (i.e., Ntotal is 0), indicating no logical conflict, Kcon is directly set to 1; if all related nodes have conflicts (i.e., Nconf and Ntotal are equal), Kcon is directly set to 0.

[0106] 4. Example Calculation

[0107] Example 1: A newly acquired work history data has 8 associated nodes matched in the graph. Among them, 2 nodes have logical conflicts: the work time overlaps with the person's education time, and the company's existence time is contradictory. After calculation, Krate = 2 / 8 = 0.25, Kcon = 1 - 0.25 = 0.75.

[0108] Example 2: The basic identity data of a newly added node in the graph has no matching associated nodes, so Kcon = 1 is directly set.

[0109] III. Overall Algorithm Adaptability Description

[0110] In terms of scoring range, the scores of Tage and Kcon are both controlled in the range of 0 to 1, which is fully compatible with the scoring rules of the official credibility score Rsrc of the data source and the consistency rate Mcon of multi-source data, ensuring the effectiveness of the weighted summation of the background check data confidence comprehensive evaluation algorithm.

[0111] Regarding weight settings, considering the characteristics of background check business, it is recommended that the weight λ2 of Tage be between 0.2 and 0.3, and the weight λ4 of Kcon be between 0.2 and 0.25. These, together with the weight λ1 (0.3~0.4) of Rsrc and the weight λ3 (0.2~0.3) of Mcon, satisfy the weight normalization constraint, i.e., λ1+λ2+λ3+λ4=1.

[0112] In terms of process integration, the comprehensive confidence level Cdata calculated by the formula is matched with the preset threshold Tc in the document. If the value of Cdata is less than Tc, the data will automatically enter the anomaly handling process, which is seamlessly connected with the conflict resolution and manual review of the intelligent governance module to ensure the integrity of data governance.

[0113] Example 3: Full Lifecycle Execution Process

[0114] 1. Data Acquisition: Multi-source access → Parsing → Standardization

[0115] 2. Data cleaning: deduplication, missing data filling, and anomaly detection.

[0116] 3. Knowledge annotation: Automatic annotation of entities, relations, and attributes.

[0117] 4. Entity Alignment: Algorithm Disambiguation → Conflict Resolution → Knowledge Storage

[0118] 5. Dynamic Updates: The graph is updated incrementally based on timeliness / events.

[0119] 6. Archive Management: Automatic archiving of expired / low-frequency data.

[0120] 7. Security Audit: Full traceability, access control, and compliance traceability.

[0121] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A knowledge graph-based automated management system for the entire lifecycle of background check data, characterized in that, include: The system comprises a data access module, a background check knowledge graph construction module, an intelligent governance module, a full lifecycle scheduling module, and an interface service module. The data access module is used for the collection, parsing, and format unification of multi-source heterogeneous background check data. The background check knowledge graph construction module extracts entities, relationships, and attributes from standardized data to construct and store a background check domain knowledge graph. The intelligent governance module is configured with automatic learning algorithms for data cleaning, entity alignment, semantic annotation, and conflict resolution. The full lifecycle scheduling module performs closed-loop scheduling of background check data collection, cleaning, annotation, updating, archiving, and destruction, and dynamically optimizes data flow rules. The interface service module provides external interfaces for data querying, graph retrieval, process monitoring, and access control.

2. The system according to claim 1, characterized in that, The data access module includes a structured data adapter, an unstructured data parsing unit, and a data standardization unit. The standardization unit converts heterogeneous data into a standard structure that can be used for knowledge extraction.

3. The system according to claim 2, characterized in that, The unstructured data parsing unit uses a hybrid extraction method combining pre-trained models and rules to automatically identify personnel entities, organizational entities, document information, time elements, address elements, and risk description information.

4. The system according to claim 1, characterized in that, The background check knowledge graph construction module includes an ontology definition unit, a triple generation unit, and a graph database storage unit. The ontology definition unit pre-defines six core ontology categories: personnel, certificates, organizations, educational background, work experience, and risk events.

5. The system according to claim 4, characterized in that, The background check knowledge graph construction module supports incremental updates. When the data source changes, it only performs extraction, matching, fusion, and verification on the difference subgraph.

6. The system according to claim 1, characterized in that, The intelligent governance module performs entity alignment and conflict resolution, and uses a multi-dimensional weighted fusion algorithm to automatically disambiguate homonymous and heteronymous entities, and corrects the confidence level of multi-source conflict data.

7. The system according to claim 6, characterized in that, The intelligent governance module performs a comprehensive score based on the credibility of the data source, update time, and consistency of association. Data that is below the cleaning threshold is automatically marked and enters the review process.

8. The system according to claim 1, characterized in that, The full lifecycle scheduling module includes a data update engine and an archiving strategy unit. The update engine performs real-time updates, scheduled updates, or incremental updates based on data timeliness scores and business event triggers.

9. The system according to claim 8, characterized in that, The full lifecycle scheduling module has a built-in dynamic optimization algorithm for flow rules. Based on historical processing success rate, data quality, time consumption cost and compliance level, it adaptively adjusts the cleaning threshold, labeling granularity, update cycle and archiving conditions.

10. The system according to claim 1, characterized in that, It also includes a security and compliance module, which is used for data anonymization, operation logging, hierarchical access control, and compliance audit log recording to achieve full lifecycle traceability and auditability of background investigation data.

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