Enterprise knowledge graph driven legal compliance audit response method

Abstract

The application discloses an enterprise knowledge graph driven legal compliance auditing response method, and relates to the technical field of artificial intelligence, comprising the following steps: performing semantic analysis on the regulation text, constructing a context layered expression model, dividing each regulation into a title semantic block, a main body semantic block, a limitation semantic block and a constraint semantic block, and generating a layered semantic graph. The application realizes the structured analysis of the regulation article by constructing the layered semantic graph, and accurately aligns it with the business entity in the enterprise knowledge graph to construct a regulatable regulation-enterprise semantic mapping relationship. Combined with boundary reasoning, confidence scoring, context backtracking and semantic correction, the application realizes the intelligent discrimination and correction of the applicability of the regulation obligations. Through the feedback optimization learning mechanism, the model and the rules are continuously updated, the dynamic adaptability of the system is improved, the accuracy of the compliance suggestions is improved, the risk of misjudgment is significantly reduced, and the intelligence and practical application value of the system are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of artificial intelligence, specifically to a legal compliance audit response method driven by enterprise knowledge graph. BACKGROUND

[0002] "Legal compliance audit response driven by enterprise knowledge graph" refers to constructing a knowledge graph covering core information such as enterprise internal management structure, business process, contract elements, and audit records, and associating it with national laws and regulations, industry standards and policy documents to realize an intelligent identification and response mechanism for compliance issues. When users raise legal compliance issues related to enterprise operation, the system can automatically analyze the problem intention based on the entity relationship and context logic in the knowledge graph, match the corresponding regulations or internal enterprise standards, and output targeted response content or compliance suggestions, replacing the traditional inefficient process of relying on manual interpretation of regulations, and improving the intelligence, automation and real-time response capability of compliance audit. This method integrates knowledge graph construction, natural language processing and legal rule reasoning technology, and is suitable for enterprise daily compliance management, contract review, internal question and answer system and external compliance consulting services, etc.

[0003] The prior art has the following disadvantages:

[0004] In the existing legal compliance audit based on knowledge graph, the semantic analysis of regulations usually relies on static text feature matching or shallow semantic similarity analysis, lacking the ability to dynamically model the context of regulations, resulting in the inability to accurately identify the scope of application, constraints and applicable subjects when associating the semantics of regulatory obligations, thus making it difficult to achieve consistent correction of the context of regulations. Specifically, in the presence of ambiguous or applicable restrictions in legal provisions, if the limiting expressions in the context cannot be identified, it is easy to cause "context drift" problem, i.e. the erroneous generalization of regulations obligations that are only applicable to specific subjects (such as listed companies, franchised enterprises, etc.) to ordinary enterprises or non-applicable objects, thus causing the applicability deviation of the output compliance suggestions. This problem not only leads to the misjudgment and misuse of irrelevant regulations by enterprises, resulting in waste of compliance resources and increase of operational burden, but also may cause substantial compliance risks due to the omission of truly applicable regulatory provisions.

[0005] The above information disclosed in the background section is only used to strengthen the understanding of the background of the present disclosure, therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] The purpose of the present application is to provide a legal compliance audit response method driven by enterprise knowledge graph, to realize the structured analysis of the provisions of laws and regulations by constructing a hierarchical semantic graph, and to accurately align them with business entities in the enterprise knowledge graph, to construct a law-enterprise semantic mapping relationship that can be inferred. Combined with boundary reasoning, confidence scoring, context backtracking and semantic correction, intelligent discrimination and correction of the applicability of legal obligations are realized. Through the feedback optimization learning mechanism, the model and rules are continuously updated, the dynamic adaptability of the system and the accuracy of the compliance suggestions are improved, the risk of misjudgment is significantly reduced, and the intelligence and practical application value of the system are enhanced, to solve the problems in the above background technology.

[0007] In order to achieve the above purpose, the present application provides the following technical scheme: a legal compliance audit response method driven by enterprise knowledge graph, comprising the following steps:

[0008] S100, semantic analysis of the provisions of laws and regulations is performed, a context hierarchical expression model is constructed, each provision of laws and regulations is divided into a title semantic block, a main body semantic block, a limitation semantic block and a constraint semantic block, and a hierarchical semantic graph is generated;

[0009] S200, based on the hierarchical semantic graph, the semantic units in the title semantic block, the main body semantic block, the limitation semantic block and the constraint semantic block are labeled using the structured information in the enterprise knowledge graph, a one-to-one mapping relationship between the semantic units of laws and regulations and the business entities is established, and a law-entity semantic mapping matrix is constructed;

[0010] S300, based on the law-entity semantic mapping matrix, combined with the explicit conditions and implicit conditions in the limitation semantic block and the constraint semantic block, the applicability condition logic verification is performed, the boundary reasoning is performed, and a law applicability confidence scoring model is constructed;

[0011] S400, based on the law applicability confidence scoring model, the context drift discrimination is performed on the behavior obligation items with confidence scores in the critical interval, the context consistency backtracking mechanism is called, the context traceability analysis is performed, and the behavior obligation items with context shift risk are marked;

[0012] S500, for the behavior obligation items with context shift risk, the semantic correction rule set is called, the law obligation output correction processing is performed, the semantic filtering and adaptation correction are performed, and the compliance suggestions that meet the context consistency and have the applicable accuracy are generated;

[0013] S600, after the law obligation output correction is completed, based on the context feature parameters, the rule calling logs and the judgment results recorded in the context drift discrimination and the semantic correction process, the feedback optimization learning processing is performed, the context drift discrimination model and the semantic correction rule set are updated, and the dynamic evolution and automatic adaptation of the law audit context are realized.

[0014] Preferably, step S100 includes:

[0015] Obtain the set of legal texts, and perform sentence segmentation and grammatical element annotation;

[0016] A multi-level text attention mechanism is used to generate context-enhanced word vector representations;

[0017] Word vectors are input into a conditional random field method for semantic block category prediction;

[0018] A structured hierarchical semantic graph is generated based on the prediction results, and semantic integrity and structural legality are verified.

[0019] Preferably, step S200 includes:

[0020] Extract core semantic units from the title semantic block, body semantic block, limiting semantic block, and constraint semantic block, and establish a semantic unit candidate pool;

[0021] Extract structured entity information from the enterprise knowledge graph and complete entity standardization and semantic expansion processing;

[0022] A bidirectional semantic matching strategy is adopted, which combines context-enhanced semantic coding method and unified scoring function to calculate the matching score between semantic units and enterprise entities;

[0023] The semantically aligned mapping relationships are constructed into a legal entity semantic mapping matrix, which records the semantic matching results and path information.

[0024] Preferably, step S300 includes:

[0025] Extract the conditional expressions from the limiting semantic blocks and constraint semantic blocks of the legal behavioral obligations, and construct a legal condition dependency graph;

[0026] The corresponding business entities in the enterprise knowledge graph are processed into feature vectors to generate enterprise business background vectors.

[0027] Input the regulatory condition structure and enterprise feature vector into the boundary reasoning engine, and determine whether the regulatory item meets the applicable conditions based on the rule logic chain and vector similarity calculation.

[0028] Based on the reasoning results, a confidence scoring model for the applicability of regulations is constructed, which outputs the applicability score and judgment path for the behavioral obligation items.

[0029] Preferably, step S400 includes:

[0030] By screening behavioral obligations whose scores fall within the critical range in the applicability confidence scoring model, a set of questionable legal obligations is constructed.

[0031] Invoke the context consistency backtracking mechanism to locate the constraint semantic chain of the behavioral obligation item and perform structural backtracking;

[0032] Based on the backtracking results, perform context consistency analysis to determine whether there are semantic conflicts or potential context drift risks.

[0033] The identified context-biased risk behaviors and obligations are explicitly marked and the offset information field is recorded.

[0034] Preferably, step S500 includes:

[0035] Initialize the semantic correction rule set and associate it with the behavioral obligations marked as having the risk of contextual deviation;

[0036] Based on semantic filtering rules, identify semantically conflicting items that are inconsistent with the enterprise's business context and perform output removal operations;

[0037] Based on semantic adaptation correction rules, semantically incomplete entries are processed by subject explicitation, condition completion, and constraint path clarification.

[0038] Integrate all processing results, reconstruct the set of regulatory obligations outputs, and update the compliance suggestion generation path and context optimization logs.

[0039] Preferably, step S600 includes:

[0040] Collect context path features, rule call logs, and judgment results recorded during the context drift recognition and semantic correction process, and construct a feedback learning data pool;

[0041] A semantic feature sample set is generated based on the data pool, and context offset labels are labeled. A supervised learning method is used to train the context drift discrimination model.

[0042] Based on the correction processing logs, the semantic correction rule set is maintained and expanded to generate correction suggestions and update the rule management list.

[0043] The updated context drift discrimination model and semantic correction rule set are loaded into the regulatory review process to complete the feedback loop.

[0044] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0045] This invention constructs a hierarchical semantic graph to achieve structured parsing of regulatory provisions, thereby precisely aligning business entities in the enterprise knowledge graph with regulatory semantic units, and building a reasonable and interpretable regulatory-enterprise semantic mapping relationship. Based on this, boundary reasoning and confidence scoring mechanisms are introduced to quantitatively judge the applicability of each regulatory obligation item, and potential application errors are identified and corrected through context backtracking and semantic correction mechanisms. Finally, dynamic evolution of the context model and correction rules is achieved through feedback optimization learning, enabling the system to continuously learn and adapt to changes in different regulatory language. Overall, this method not only improves the contextual consistency and applicability accuracy of regulatory recommendations, but also enhances the intelligence and practicality of compliance review, significantly reducing the risk of misuse and the burden of manual review, demonstrating good engineering implementation value and innovation. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0047] Figure 1 This is a flowchart illustrating the legal compliance review and response method driven by the enterprise knowledge graph of this invention. Detailed Implementation

[0048] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0049] This invention provides, for example Figure 1 The enterprise knowledge graph-driven legal compliance audit response method shown includes the following steps:

[0050] S100 performs semantic analysis on the legal text, constructs a context-layered expression model, divides each legal article into four types of semantic blocks: title semantic block, main semantic block, limiting semantic block, and constraint semantic block, and generates a structured layered semantic graph based on the semantic blocks;

[0051] To address the issue of existing legal compliance review technologies failing to accurately identify the applicable context of regulations, this paper proposes a method that performs semantic parsing of regulatory texts and constructs a context-layered representation model. This enables a structured understanding and semantic hierarchy differentiation of regulatory provisions, thereby providing precise contextual support for subsequent interpretation of regulatory intent and matching with corporate entities. The process specifically includes the following steps:

[0052] The process involves acquiring a set of target legal texts and preprocessing each text, including standardizing the encoding format, removing format control characters, segmenting sentences, and standardizing the language. Specifically, the legal texts are divided into multiple smallest semantic units based on logical sentences, and legal terms, citation numbers, and chapter structures are explicitly labeled. This step aims to establish a mapping channel between the natural language layer and the structural semantic layer, laying the corpus foundation for subsequent semantic layering. Furthermore, a legal language part-of-speech tagging tool is introduced during preprocessing to mark grammatical elements such as subjects, predicates, and conditional clauses in each sentence to assist in the delineation of semantic block boundaries.

[0053] Based on the grammatical structure and semantic function of legal provisions, semantic block identification processing is performed. This process uses each legal provision as the basic analysis unit, employing a trained semantic function classification model to identify and extract title semantic blocks, body semantic blocks, limiting semantic blocks, and constraint semantic blocks. Title semantic blocks mainly cover the chapter titles, item numbers, and policy background information; body semantic blocks focus on the applicable objects and obligors of the legal constraints; limiting semantic blocks express the preconditions, contextual constraints, or triggering mechanisms for the application of the legal provisions; and constraint semantic blocks carry the core content of the legal provisions, such as clearly defined obligations, behavioral requirements, and prohibited items. In this process, a multi-level text attention mechanism combined with a conditional random field method is used for block-level partitioning and semantic classification, ensuring that each type of semantic block possesses semantic integrity and functional independence. This partitioning not only performs syntactic analysis based on grammatical structure but also introduces a contextual reasoning mechanism to identify cross-sentence limiting relationships, thereby achieving contextual consistency labeling of semantic blocks.

[0054] In this step, to achieve accurate identification and classification of various semantic blocks in the regulatory text, a strategy combining a multi-level text attention mechanism and a Conditional Random Field (CRF) method is adopted to improve the accuracy and consistency of semantic block segmentation.

[0055] Among them, the multi-level text attention mechanism is a context modeling method in deep learning. It captures semantic association features at different granularities by calculating attention weights for the input text at different levels (such as word level, phrase level, sentence level, and paragraph level). In this invention, this mechanism first applies attention weights to each word or phrase during the encoding stage of the legal text, identifying its semantic contribution in the current context, and then generating semantically enhanced word vector representations. This mechanism effectively strengthens the attention to key semantic words (such as legal verbs or qualifiers like "shall," "limited to," and "responsible for") while suppressing interference from regions with unclear semantic boundaries, thus improving the model's ability to identify "semantic functional paragraphs" in the legal structure.

[0056] Conditional Random Fields (CRFs) are probabilistic graphical models used for sequence labeling tasks. They can predict the labeling of each element in a sequence based on globally optimal path reasoning. In this invention, CRFs are used to perform refined labeling of the "semantic block category" of each word or phrase in a legal text, specifically categorized into four types: title semantic blocks, body semantic blocks, limiting semantic blocks, and constraint semantic blocks. Unlike simple classification models, CRFs can simultaneously consider the labeling states of preceding and following words. By modeling the transition probabilities between labels, they optimize the label distribution path of the entire sentence, avoiding "jumps" or "conflicts" in semantic labeling.

[0057] In practical implementation, a pre-trained language model (such as BERT or LegalBERT) is first used to encode word vectors in the regulatory text. A multi-level text attention mechanism is then superimposed on the output to obtain vector representations with context-enhanced capabilities. Subsequently, these representations are input into a CRF layer, where the CRF predicts and categorizes the semantic block category corresponding to each word based on the overall sentence context and label dependencies, thus completing block-level partitioning and semantic classification. This method combines the deep semantic modeling capabilities provided by the former with the sequence optimization capabilities provided by the latter, achieving a triple guarantee of "functional consistency, boundary integrity, and logical coherence" in the process of regulatory semantic block partitioning, effectively improving the accuracy and stability of hierarchical regulatory modeling.

[0058] The semantic blocks identified above are structured to generate a hierarchical semantic expression structure oriented towards the regulatory context. This structure is expressed in the form of a multi-layered directed connection graph, where each type of semantic block is a node in the graph, and edges connect them to reflect their contextual dependencies, semantic orientations, and logical constraints. Specifically, the title semantic block is located at the top layer of the structure, and is associated downwards with the main semantic block. The main semantic block is then connected to the limiting semantic block and the constraint semantic block according to the subject it describes. The limiting semantic block further points to the constraint semantic block within its control, forming a complete semantic path from regulatory identification to applicable premises to obligation description. This graph structure not only preserves the semantic integrity of regulatory provisions, but also supports the modeling of ambiguous clauses in complex contexts and the calculation of cross-layer dependency paths, enhancing the ability to determine the applicability of regulatory content in different enterprise scenarios.

[0059] The semantic integrity and legality verification of the hierarchical semantic graph are performed. This step, based on a domain knowledge base and legally annotated corpus, checks the consistency of each semantic block in the generated hierarchical semantic graph, including whether semantic boundaries are clear, whether the connections between upper and lower layers are compliant, and whether ambiguous statements are correctly categorized. For detected inconsistent blocks, a correction process is invoked to readjust their classification labels or structural connections, ensuring that the entire hierarchical semantic graph is clear, semantically accurate, and logically closed-loop. This semantic graph will serve as the input basis for subsequent processes such as aligning legal provisions with enterprise business entities, calculating the applicability of regulations, and determining contextual consistency, effectively improving the modeling depth and recognition accuracy of the regulatory context in the entire legal compliance review process.

[0060] The core function of this step is to transform legal provisions expressed in natural language into a hierarchical expression model with semantic structure and contextual logic. This provides clear semantic boundaries and structural support for subsequent key stages such as interpreting the intent of the regulations, matching corporate entities, and determining the applicability of the regulations. In traditional legal compliance review systems, legal provisions are usually stored in plain text form, lacking structured semantic division, making it difficult to identify the functional positioning of each legal sentence in the context. For example, "applicable to listed companies" is a limiting condition, and "must disclose annual reports" is a behavioral obligation. Without clear hierarchical expression, the semantic analysis process can easily lead to the misgeneralization of local obligations to inapplicable objects, resulting in the problem of "context drift."

[0061] This step divides the legal framework into four functional semantic blocks: title semantic blocks, body semantic blocks, limiting semantic blocks, and constraint semantic blocks. This constructs a hierarchical logical structure for the legal content, effectively resolving issues of semantic overlap, structural complexity, and overlapping conditions in legal texts. Furthermore, by constructing a structured hierarchical semantic graph, it not only clearly reveals the contextual dependencies between the semantic blocks but also provides an operational logical path for subsequent rule-based reasoning, semantic alignment, and contextual backtracking analysis, significantly improving the determineability and automated processing capabilities of legal applicability. Therefore, this step plays a crucial role in laying the semantic foundation for the entire compliance review process in this invention, and is a prerequisite for achieving contextual consistency control and accurate output of regulatory recommendations.

[0062] S200, based on the hierarchical semantic graph, performs semantic entity alignment processing, uses structured information in the enterprise knowledge graph to annotate the subject objects, behavioral obligations and applicable premises in the legal semantic graph, establishes a one-to-one mapping relationship between legal semantic units and enterprise business entities, and constructs a legal-entity semantic mapping matrix.

[0063] To achieve accurate matching between the semantic content of regulations and enterprise business entities, and to avoid misapplication of regulatory obligations due to semantic ambiguity or abstract expressions, a processing method based on hierarchical semantic graphs for semantic entity alignment is proposed. This method, based on a constructed hierarchical semantic graph of regulations, identifies and labels the subject objects, behavioral obligations, and applicable preconditions in the regulatory semantic graph by calling upon structured information resources in the enterprise knowledge graph. Based on this, a one-to-one correspondence is established between regulatory semantic units and enterprise business entities, ultimately forming a regulatory-entity semantic mapping matrix that can be used for subsequent applicability reasoning. The entire process includes the following steps:

[0064] Key semantic units are extracted from the hierarchical semantic graph, and core entity phrases contained in the title semantic block, body semantic block, limiting semantic block, and constraint semantic block are located respectively. For subjects or parties bearing rights and obligations appearing in the body semantic block, such as "securities company," "government department," and "internet platform," and for the behavioral objects or actions described in the constraint semantic block, such as "disclosing information," "establishing mechanisms," and "conducting audits," preliminary semantic unit segmentation is completed using a legal terminology dictionary and part-of-speech tagging methods. Based on this, a candidate pool of semantic units is established. This candidate pool includes not only explicit entities but also implicit entities that need to be inferred from the context due to pronouns, quotations, or semantic omissions, to ensure the semantic integrity and contextual coherence of entity recognition.

[0065] Structured information usable by the enterprise during compliance audits is extracted from the enterprise knowledge graph, including enterprise name, organization type, industry, business scope, licenses and qualifications, shareholding relationships, management processes, contract performance, and key job responsibilities. All information must be provided in the form of entity nodes and attribute paths within the graph, and standardized entity naming and synonym normalization are used to ensure consistency between the graph representation and the natural language descriptions in regulatory texts. To enhance the ability to recognize cross-domain expressions, a terminology expansion mechanism is introduced, integrating an industry ontology and regulatory lexicon on top of the original knowledge graph. This enables synonym expansion and hierarchical concept linking, thereby increasing tolerance for incomplete matching descriptions and coverage of semantic relationships.

[0066] Based on the generated semantic unit candidate pool and standardized enterprise entity set, semantic alignment calculation is performed. A bidirectional semantic matching strategy is adopted to perform semantic association calculations in two directions: matching from regulations to enterprises and vice versa. Context-enhanced semantic encoding is introduced during the calculation process, considering the hierarchical connection structure of semantic units in the hierarchical semantic graph and the type of their semantic block. Combined with a multi-granularity semantic vector expression model, indicators such as semantic similarity, contextual consistency, and applicability weight between each semantic unit and enterprise entity are extracted. A unified scoring function is used to comprehensively calculate the semantic matching score for each pair of regulations-entity combinations. When a regulatory semantic unit and an entity node in the enterprise graph meet preset threshold requirements in multiple dimensions, semantic alignment is considered successful, and its semantic path index and contextual dependency information are retained in the alignment record.

[0067] A two-way semantic matching strategy refers to performing semantic association calculations in two directions—"regulation to enterprise" and "enterprise to regulation"—when semantically aligning regulatory semantic units with enterprise business entities, thereby enhancing the accuracy and robustness of the matching. In this method, its role is to prevent semantic omissions or asymmetries caused by unidirectional reasoning, ensuring comprehensive and consistent semantic associations whether searching for business scenarios from a regulatory perspective or tracing back regulatory obligations from an enterprise entity perspective.

[0068] In its implementation, the system first performs a "regulation-to-enterprise" matching process: a semantic unit (e.g., "financial information to be disclosed") is extracted from the hierarchical semantic graph of regulations and used as a query vector. The semantic similarity between this unit and each entity in the enterprise knowledge graph (e.g., "annual report disclosure obligation" and "financial statement submission node") is calculated sequentially to find the closest matching object. Then, a "enterprise-to-regulation" matching process is performed: starting from the entity nodes in the enterprise graph, a reverse search is conducted to find semantically alignable entries in the regulatory semantic graph, and their similarity and contextual fit are calculated accordingly. The matching scores calculated in both directions are then fused using a unified scoring function. Semantic alignment is considered successful only when the regulatory unit and the enterprise entity reach a preset similarity threshold in both directions. This strategy effectively prevents biases and mismatches that may result from one-sided semantic matching and improves the ability to identify complex, ambiguous, and hierarchical concepts.

[0069] Context-enhanced semantic encoding methods refer to the introduction of hierarchical structure, semantic block type, and adjacency information in the semantic graph when representing legal semantic units and enterprise entities as vectors, thereby enhancing the context-awareness of semantic representation. In this method, its role is to improve the model's ability to understand semantic dependency structures and business scenario contexts, avoiding misjudging terms with similar literal expressions but in different contexts as semantically equivalent terms.

[0070] In its implementation, the method first uses pre-trained language models (such as BERT and LegalBERT) to obtain basic word vector representations of legal semantic units and corporate entities. Then, "contextual structure encoding" is introduced: extracting the node position of the semantic unit in the hierarchical semantic graph, its semantic block type (such as subject, constraint, or limitation), and upstream and downstream dependency path information (such as whether it depends on a subject or is bound to a limiting condition). These contextual features are then transformed into structural vector representations and concatenated or fused with the original word vectors. Further, through multi-granularity semantic encoding methods (such as fusing phrase-level, sentence-level, and graph path-level semantic vectors), the final context-enhanced semantic representation is formed. This representation not only captures the meaning of the words themselves but also expresses their logical position and dependencies within the legal structure, making semantic alignment calculations more accurate and interpretable. This method is highly adaptable to common structures in legal texts such as ellipsis, logical jumps, and contextual limitations.

[0071] All aligned semantic units and their corresponding enterprise entities are uniformly summarized to construct a regulatory-entity semantic mapping matrix. This matrix uses regulatory semantic units as row vectors and enterprise entities as column vectors. Each element records the semantic relevance score between the two entities, the entity category identifier, the semantic block number it belongs to, and its path position information in the hierarchical semantic graph. This semantic mapping matrix not only enables the adaptation and verification of regulatory obligations to enterprise scenarios but also provides a precise and quantifiable semantic foundation for subsequent core processes such as applicability judgment, confidence score calculation, and context drift detection.

[0072] The core function of this step is to achieve a precise semantic connection between legal provisions and the enterprise's business environment, thereby avoiding regulatory mismatches caused by semantic ambiguity or generalized application. In traditional legal compliance review systems, regulatory content is usually presented in natural language descriptions, while enterprise business information exists in the form of structured data, process documents, or graphical entities. The lack of a direct semantic link between the two leads to problems such as incorrect identification of applicable subjects, unclear attribution of obligations, or missing preconditions when performing regulatory matching or compliance judgments, ultimately affecting the accuracy and effectiveness of compliance recommendations. Therefore, establishing a one-to-one mapping relationship between legal provisions and actual business entities is a key prerequisite for achieving intelligent compliance review.

[0073] This step, based on a hierarchical semantic graph of regulations, extracts the semantics of key semantic units in the regulations—such as subject objects, behavioral obligations, and applicable prerequisites—and performs semantic alignment using structured information already modeled in the enterprise knowledge graph. Through techniques such as contextual semantic computation, word sense disambiguation, and entity linking, it achieves precise matching between semantic units and business entities. This mapping process not only clarifies which specific corporate behaviors and responsible units the obligations in legal clauses apply to, but also identifies whether the scope of application described in the regulations covers the current operational status of the enterprise, thereby effectively avoiding compliance risks such as semantic drift and obligation generalization. Finally, by constructing a regulation-entity semantic mapping matrix, a quantifiable and reasonable semantic relationship foundation is formed, supporting subsequent advanced processing flows such as applicability judgment, confidence score calculation, contextual consistency verification, and semantic correction. In summary, this step, while achieving deep integration of the semantic structure of legal provisions with the enterprise knowledge graph, provides a high-precision and highly reliable semantic support foundation for the entire compliance review process, playing a crucial role and representing significant technological advancement.

[0074] S300, based on the regulatory-entity semantic mapping matrix, performs applicability condition logic verification processing, combines explicit and implicit conditions in the limited semantic block and the constraint semantic block, performs boundary reasoning on each behavioral obligation item, and constructs a regulatory applicability confidence scoring model suitable for the current business context of the enterprise.

[0075] To achieve precise adaptation between regulatory provisions and actual business scenarios, and to further improve the accuracy and applicability of regulatory compliance recommendations, this paper proposes an applicability condition logic verification process based on a regulatory-entity semantic mapping matrix. By identifying and analyzing the explicit and implicit conditions contained in the limiting and constraining semantic blocks of regulatory provisions, and combining this with the enterprise's current structured business information, boundary reasoning is performed on each regulatory obligation item. This leads to the construction of a quantifiable applicability confidence scoring model to support the automated determination of whether regulatory provisions are applicable to the current enterprise scenario. This process includes the following steps:

[0076] Extract the limiting and constraining semantic blocks corresponding to the legal obligations, and extract the conditional expressions within them in a structured manner. Explicit conditions mainly include preconditions directly stated in the regulations, such as "listed companies shall..." or "licensed financial institutions shall comply with...", which have clear boundaries of application. Implicit conditions include restrictive content indirectly expressed through sentence structure, contextual references, or logical structures, such as "the above provisions apply to specific business situations" or "as described in the preceding article," which requires inference and analysis based on semantic dependency paths and context graph structures. By constructing a regulatory condition dependency graph, all conditional expressions are hierarchically organized and logically categorized, laying the semantic foundation for boundary reasoning.

[0077] Based on the constructed regulatory-entity semantic mapping matrix, enterprise business entity information corresponding to each regulatory entry is extracted from the enterprise knowledge graph and then processed into feature vectors. The extracted information includes specific structured elements such as enterprise organization type, industry, business activities, management mechanisms, licenses and qualifications, and financial behavior, further generating an enterprise business background vector. To enhance tolerance for fuzzy matching and cross-semantic category matching, a semantic similarity calculation method is introduced to perform vector-level alignment calculations between regulatory condition expressions and enterprise business features. This is combined with predefined hierarchical relationships and synonym expansion rules in the domain knowledge graph to improve the semantic coverage breadth and accuracy of condition matching.

[0078] Semantic similarity calculation methods refer to methods that convert words, phrases, or sentences in natural language into vector representations and calculate the degree of semantic similarity between them based on mathematical models. In this invention, the method is used to compare the semantic relevance between conditional expressions in regulatory provisions and business features in enterprise knowledge graphs, thereby determining whether the two belong to the "same concept" or "approximate concept," even if they are not completely consistent in linguistic expression. Its core significance lies in enhancing tolerance for ambiguous expressions (such as "financial institutions" and "securities companies") and cross-semantic category expressions (such as "information to be disclosed" and "publishing annual reports"), thereby improving the robustness and coverage of matching.

[0079] To achieve the above objectives, firstly, the restrictive conditional phrases in regulations and the structured entity names in the enterprise knowledge graph are encoded using pre-trained language models (such as BERT, LegalBERT, etc.) to generate their respective context-enhanced semantic vectors. Secondly, using similarity indicators such as cosine similarity or Euclidean distance, pairwise calculations are performed on the regulatory semantic vectors and enterprise semantic vectors to generate a semantic similarity score matrix. Thirdly, combining the hierarchical concept relationships in the industry knowledge graph (e.g., "financial institution" is the hierarchical concept of "securities company") and synonym expansion rules (e.g., "financial report" ≈ "annual report"), rule-enhanced processing is applied to the semantic similarity scores, reassigning confidence correction weights to results that are within the semantic score boundaries but not fully matched. Finally, whether a valid semantic alignment is constituted is determined based on whether the matching score exceeds a set threshold, and the matching path and similarity score are recorded in the regulation-entity semantic mapping matrix.

[0080] By introducing semantic similarity calculation methods, the matching problem caused by inconsistencies in language style, professional terminology, and expression habits between regulations and enterprises can be effectively solved. This enables the system to maintain high accuracy and generalization ability when dealing with non-standardized regulatory descriptions or diverse enterprise expressions. It is one of the key supporting technologies for achieving intelligent compliance reasoning and contextual consistency recognition in this step.

[0081] The application of regulations is processed through boundary reasoning. The conditional constraint structure of the regulatory obligations and the enterprise's feature vector are input into the boundary reasoning engine. Based on the rule logic chain and vector similarity calculation results, it is determined whether the regulatory item meets the application conditions. Specifically, based on the logical scope of each condition, its necessity, and whether it is a mutually exclusive condition, corresponding elements in the enterprise's background are matched one by one, and a Boolean logic model is used to comprehensively determine whether all application prerequisites are met. Cases with uncertainty or where only some conditions are met are marked as "low-confidence fit," and the corresponding missing conditions or conflict information are recorded for subsequent drift detection or semantic correction processing.

[0082] A boundary reasoning engine is an intelligent reasoning mechanism used to determine whether a regulatory obligation is applicable within a specific business context. Its core function is to automatically determine and reason about the boundaries of regulatory applicability based on the conditional constraint structure of regulatory provisions and the feature representations in the enterprise knowledge graph, comprehensively utilizing both rule logic chains and vector similarity information. In this invention, the boundary reasoning engine simulates the logical judgment and semantic understanding process performed by legal experts when reviewing the applicability of regulations, thereby ensuring that regulatory obligations are activated only within their legally applicable business context, effectively avoiding compliance risks such as misuse and mismatch.

[0083] The so-called rule logic chain refers to the result of logical modeling the conditional structure composed of limiting and constraining semantic blocks in legal provisions. This includes logical types such as preconditions, necessary conditions, sufficient conditions, and exclusionary conditions, as well as the logical connections between them (e.g., AND, OR, NOT). This chain expresses the legal logic structure of the applicable legal obligations, reflecting "under what conditions" a clause has legal effect. Vector similarity, on the other hand, uses natural language processing technology to vectorize the legal condition expressions and enterprise characteristics separately, calculating their distance or angle in a high-dimensional semantic space to obtain a measure of their semantic correlation. The combination of these two methods reflects the synergistic effect of the semantic layer and the rule layer in the reasoning process.

[0084] In its implementation, the following steps are taken: First, the conditional expressions of regulatory obligations are structurally analyzed to construct their corresponding rule logic chains, including the logical types, nesting levels, and dependencies of the conditions. Then, relevant entity features in the current business context are extracted from the enterprise knowledge graph and encoded using a language model to generate enterprise feature vectors. Second, corresponding semantic vectors are generated for the regulatory condition expressions, and a semantic matching score between each enterprise feature and each regulatory condition is calculated using cosine similarity. Third, the aforementioned similarity scores are input as variables into the logic chain, and Boolean or fuzzy logic rules are used to determine whether all applicable constraints set by the regulatory item are met under the current enterprise conditions. Finally, based on the logical judgment results, the applicability status of the behavioral obligation item in the current scenario is output, while missing paths and low-confidence matches that do not meet the conditions are marked for subsequent context drift detection and correction.

[0085] This reasoning engine can simulate the cognitive process of "reading regulations, understanding conditions, checking background, and making judgments" in manual compliance review, and upgrade from static semantic matching to dynamic scenario applicability judgment. It is a key decision-making mechanism to support the intelligent applicability of regulations, and has significant creativity and practical application effectiveness.

[0086] Finally, based on the boundary reasoning results described above, a regulatory applicability confidence scoring model is constructed. This model calculates the applicability probability score for each behavioral obligation item within a specific business context. Scoring factors include: condition matching degree, semantic consistency score, contextual logical coherence, and entity alignment confidence. The model uses a weighted scoring function to standardize and fuse these factors, ultimately outputting an applicability confidence score and its interpretation path. The scoring results serve as the basis for determining whether a regulatory item is recommendable in the current business scenario and whether further contextual verification is needed, marking a crucial transformation from semantic matching to business adaptation.

[0087] This step aims to automatically determine and quantify the applicability of regulatory obligations in specific business contexts, addressing the difficulties in defining "applicability" and quantifying "degree of applicability" in traditional legal compliance audits. In actual compliance audit scenarios, the applicability conditions in regulatory provisions are often hidden within complex sentence structures, including not only explicit expressions (such as "applicable to financial institutions" or "must possess a qualification license") but also numerous implicit conditions (such as time limits, effective scope, geographical areas, or special circumstances defined by context). Relying solely on keyword matching or manual interpretation can easily overlook implicit constraints or lead to incorrect generalizations of application, resulting in serious problems such as mismatches, application errors, and compliance deviations.

[0088] This step first establishes a semantic connection between regulations and enterprises using a regulatory-entity semantic mapping matrix. Then, by combining all applicable conditions in the limiting and constraining semantic blocks, logical verification is performed to determine whether the regulatory entries truly match the enterprise's current structure, business, qualifications, industry, and other elements. Especially when complex logical structures exist (such as multiple nested conditions, mutually exclusive relationships, and necessary and sufficient conditions), boundary reasoning is used to further identify the "activation boundaries" of regulatory obligations and determine whether all applicable restrictions are met. Based on this, an applicability confidence scoring model is constructed, transforming each judgment result into structured scoring indicators, such as the condition satisfaction rate, semantic similarity, and logical consistency, to form an interpretable and quantifiable confidence score that characterizes the applicability of the regulatory entries in the current business context.

[0089] This scoring model not only improves the accuracy and consistency of regulatory applicability judgments, but also provides quantitative evidence for subsequent context drift detection and correction screening, realizing the transformation of the compliance review process from a vague judgment of "applicability" to a scientific decision based on "degree of applicability." Therefore, this step plays a crucial role in this invention as the logical hub connecting semantic matching and regulatory output, ensuring the accuracy, contextual consistency, and enterprise adaptability of regulatory recommendations.

[0090] S400, based on the regulatory applicability confidence scoring model, performs context drift discrimination processing on behavioral obligation items in the critical confidence interval, calls the context consistency backtracking mechanism, performs context source analysis on the constraint semantic chain in the corresponding hierarchical semantic graph, and marks behavioral obligation items with context shift risk.

[0091] To further ensure the semantic contextual consistency and applicability accuracy of regulatory compliance recommendations, a context-based backtracking approach is introduced to handle behavioral obligations with ambiguous judgments. Building upon the aforementioned applicability confidence scoring model, regulatory items with scores within the critical confidence interval undergo focused review. Contextual source analysis of the constraint semantic chains in the hierarchical semantic graph of the regulations is performed to identify and mark behavioral obligations with potential context-shift risks, thereby preventing erroneous compliance recommendations due to biased understanding of the context. This process includes the following steps:

[0092] First, behavioral obligations whose scores fall within the critical range are selected from the applicability confidence scoring model. Specifically, items with scores within a preset range (e.g., 0.4–0.6 or a range that can be set by the company) are classified as "high uncertainty" items. These items have a partial match between the company's background and regulatory conditions, but have not yet reached a complete agreement, and are prone to misjudgment due to unclear constraints or logical understanding. This step constructs an initial set of suspected regulatory obligations through a confidence interval division mechanism, providing a target range for subsequent in-depth contextual analysis.

[0093] A context-consistency backtracking mechanism is invoked to locate the constraint semantic chain structure within the hierarchical semantic graph for each regulatory obligation item marked as high-uncertainty. This semantic chain consists of the obligation item and its associated limiting conditions, applicable scenarios, and preceding subjects. Structural backtracking is performed through the established upward and downward path relationships in the graph. During the backtracking process, the limiting semantic blocks to which the obligation is attached are progressively traced to identify whether there are cross-clause references, condition jumps, or subject omissions, thereby identifying potential contextual drift risks such as semantic disconnection, referential confusion, or mismatch of conditions. For example, if a clause uses "it," "the aforementioned," or "similar subjects" to refer to the subject, failure to accurately identify the original subject may lead to the application of the regulation to the wrong subject, resulting in a risk of deviation.

[0094] Based on the source tracing results, a contextual consistency analysis is performed to determine whether the behavioral obligation item has semantic conflicts, inconsistencies, or inconsistent contextual semantic coverage in its upstream and downstream semantic paths. Combining the logical consistency rule set, hierarchical terminology constraint rules, and semantic role annotation results, structural consistency verification is performed on the obligation item, including determining whether the cited limiting conditions still apply to the current enterprise context, whether the constraining subject has changed, and whether the behavioral verb has been misinterpreted as a different type of obligation. Items with broken semantic paths, unclosed contextual limiting relationships, or significant semantic ambiguity are identified as having a risk of contextual deviation.

[0095] Explicitly mark behavioral obligations identified as having a risk of contextual deviation, and record the marking information in the regulatory review path log for reference in subsequent semantic correction processes. The marking content includes key fields such as item identifier, deviation type, context path number, source and end points, and potentially conflicting semantic units. This marking result can be used not only for subsequent semantic correction and suggestion filtering but also to provide high-risk alerts in the manual review process, assisting compliance personnel in quickly locating suspected sources of application errors and achieving human-machine collaboration.

[0096] This step serves to further verify and correct the applicability of legal obligations at the contextual level, particularly targeting marginal items that fall within the "critical range" in the applicability confidence score. This aims to prevent "contextual drift" caused by misunderstandings, semantic jumps, or omissions in conditions. In legal texts, behavioral obligations are often nested within complex contextual structures. Their applicability is not always explicit but relies on contextual information such as preceding limiting descriptions, subject attribution, preconditions, and logical references. Therefore, when a legal obligation has a low semantic match with the company's business context but is not entirely excluded, a more granular contextual analysis mechanism must be introduced to determine whether the obligation truly applies to the current scenario.

[0097] This step, based on a confidence scoring model, focuses on behavioral obligations whose scores fall within the critical range. It invokes a contextual consistency backtracking mechanism to trace the contextual path of these obligations within the corresponding limiting semantic blocks and constraint semantic chains in the regulatory hierarchical semantic graph. This systematically analyzes whether the obligation risks deviating from its applicable context. By identifying issues such as reference jumps, ambiguous referencing, omitted subjects, and logical breaks in the semantic path, it can be discovered that some obligations, while superficially similar to corporate behavior, are actually only applicable to specific types of companies, specific business processes, or specific legal contexts. Failure to identify these hidden conditions can lead to the erroneous generalization of inapplicable regulations, severely interfering with compliance judgments.

[0098] This step explicitly marks behavioral obligations at risk of contextual drift, providing a foundation for subsequent semantic correction and compliance suggestion filtering. It prevents erroneous regulatory advice from being delivered to enterprise users, further improving the accuracy and interpretability of regulatory application judgments. This mechanism not only achieves consistent control over the semantic path of regulations but also provides automated processing capabilities for resolving complex contextual issues such as ambiguous regulations, cross-clause citations, and contextual ambiguity. It is one of the key steps in this invention to ensure the contextual consistency and applicability accuracy of regulatory suggestion output.

[0099] S500, for behavioral obligation items marked as having contextual deviation risk, calls a predefined set of semantic correction rules, performs regulatory obligation output correction processing, performs semantic filtering and adaptation correction on semantic deviation items, so as to ensure that the generated regulatory compliance recommendations meet contextual consistency and have applicable accuracy.

[0100] To address the contextual deviation issues caused by semantic ambiguity, omitted conditions, or mismatched contexts in regulatory provisions, and to further improve the applicability and contextual consistency of regulatory compliance recommendations, a regulatory obligation output correction process based on a predefined semantic correction rule set is proposed. For behavioral obligations identified as having a risk of contextual deviation, a rule-driven semantic correction mechanism is used to perform semantic filtering, adaptation correction, and output reconstruction operations. This ensures that the regulatory recommendations ultimately pushed to users only include obligations that are truly applicable in the specific business context, have clear semantic boundaries, and high adaptability. The processing method includes the following steps:

[0101] The semantic correction rule set is initialized, and all behavioral obligation entries marked as having a risk of contextual deviation are loaded. The semantic correction rule set is a predefined set of logical rules by legal knowledge engineers based on practical compliance audit experience, regulatory structural characteristics, and mismatch cases. It covers different types of semantic correction strategies, including rules for completing limiting conditions, rules for restoring subject boundaries, rules for resolving semantic jumps, and rules for identifying logical contradictions. This rule set is organized and managed through rule priority and scope labels, and has a high expressive ability for ambiguous regulatory structures and contextual dependencies. During the initialization phase, these rules are initially associated with the entries to be corrected so that they can be matched and executed sequentially in subsequent steps.

[0102] Semantic filtering is performed on each context-shift risk item. Based on the filtering rules in the rule set, the limiting semantic block, main semantic block, and upstream and downstream constraints to which the item is attached are extracted from the regulatory semantic graph, and compared to see if there are any semantic conflicts that are inconsistent with the enterprise's business context. For example, if an item applies to "financial holding company" but the enterprise is only a general "private internet company," it will be marked as "subject condition not met"; or if the constraints include "licensed qualification" and the enterprise has no relevant information to match, it will be marked as "qualification missing." For these items that do not meet the adaptation boundary, the output rejection operation is directly performed to prevent them from appearing incorrectly in the suggestions. Semantic filtering, as the first logical valve, effectively reduces the scope of invalid suggestions caused by semantic drift.

[0103] For entries that are not directly removed but have some inconsistencies, semantic adaptation correction rules are invoked for semantic reconstruction. These rules are used to correct regulatory entries with unclear semantic boundaries or incomplete expression, including methods such as explicit subject definition, completion of limiting conditions, and clarification of constraint paths. Taking the entry "must fulfill information disclosure obligations" as an example, if the preceding text contains the limiting condition "applicable to listed companies," but this is omitted in the current entry, the premise will be completed based on a backtracking mechanism to "in cases where the company is a listed company, it should fulfill information disclosure obligations," and a revised suggested entry will be generated accordingly. Through rule-driven semantic adaptation, potentially biased entries can retain their compliance value while enhancing the clarity and interpretability of their applicable context.

[0104] All filtering and correction results are integrated, the regulatory obligation output set is reconstructed, and the compliance suggestion generation path is updated. Items to be completely eliminated are removed from the suggestion output at this stage, and corrected items replace the original versions. The correction path and contextual explanations are included in the suggestion description to enhance the transparency and traceability of the suggestion results. Simultaneously, the rule numbers, semantic conflict types, and handling methods invoked during this round of correction are recorded in the context optimization log to provide training data for subsequent feedback optimization learning processes.

[0105] This step aims to further semantically correct behavioral obligations identified as having a risk of contextual misalignment, ensuring that the final regulatory compliance recommendations are consistent at the contextual level and truly applicable to the target company's actual business context at the semantic level. Regulatory provisions often suffer from structural issues such as contextual references, omitted subjects, and scattered and unfocused limiting conditions. This leads to a "contextual drift" phenomenon during automatic parsing and semantic matching, where some behavioral obligations, even if partially corresponding to the company's scenario at the linguistic level, fail to accurately identify their preceding constraints or the true applicable objects. Such deviations can easily result in inapplicable or even erroneous compliance recommendations, interfering with the company's compliance judgment and even misleading actual operations. Therefore, a correction mechanism must be established to remove, correct, or supplement entries with semantic mismatch risks.

[0106] This step involves invoking a predefined set of semantic correction rules to perform two types of processing on behavioral obligations marked as having deviation risks: semantic filtering identifies and removes items that are significantly inconsistent with the company's actual conditions; semantic adaptation correction uses rule-driven logical analysis to complete omitted or implicit constraints, clarify subject attribution, and restore contextual dependency paths, thereby semantically reconstructing and adapting the original items. These two methods effectively compress the risk range of erroneous recommendations, improving the accuracy and credibility of compliance suggestions. Simultaneously, every rule invocation and correction action is recorded during the process, providing a data foundation for subsequent feedback learning and evolution.

[0107] In summary, this step is not only the core link in ensuring the accuracy and quality of regulatory recommendations in this invention, but also a highly adaptable strategy in the face of the complexity of regulatory language and semantic uncertainty. It realizes a closed-loop logic from semantic recognition, risk labeling to deviation correction, and provides key technical support for achieving contextual consistency control, credible modeling of compliance logic, and stability of recommendation output.

[0108] After completing the correction of the legal obligation output, the S600 performs feedback optimization learning processing based on the context feature parameters, rule call logs and judgment results recorded in this round of context drift recognition and semantic correction. It updates the context drift discrimination model and semantic correction rule set in real time to achieve dynamic evolution and automatic adaptation to the subsequent legal review context.

[0109] To enhance the intelligent adaptability and self-optimization capabilities of the regulatory compliance review system during long-term operation, and addressing the dynamic evolutionary needs of context drift detection and semantic correction operations, an optimization learning process based on a feedback mechanism is proposed. By collecting contextual feature parameters, rule call logs, and judgment results involved in the current round of regulatory obligation correction, an incremental learning mechanism based on historical correction data is established to update the context drift detection model and semantic correction rule set in real time, continuously enhancing the system's understanding and automatic adaptation capabilities to complex regulatory contexts. This process includes the following steps:

[0110] Core data collected during the identification and correction of contextual drift in legal obligations in this round includes, but is not limited to: contextual path features of behavioral obligations identified as having a risk of deviation (such as the depth of the limited semantic block, subject reference distance, and length of the hypertext chain), actual rule invocation (such as matching rule number, execution path, and success / failure status), and changes in scores and applicability labels before and after correction. All data, after structured processing, are uniformly stored in the feedback learning data pool as raw semantic samples for subsequent model training and rule evolution.

[0111] The contextual features and judgment results in the data pool are labeled to construct a semantic feature sample set for training the context drift discrimination model. This feature set uses behavioral obligation items as the basic unit, integrating their semantic block structure, semantic path graph information, enterprise entity matching, and correction rule invocation. By labeling items as either "context drift exists" or "no context drift," a sample set is established for iterative model training. During training, supervised learning methods are employed, introducing context encoding models (such as bidirectional attention mechanisms and graph structure representation models) to model contextual features, thereby improving the model's ability to perceive complex structures such as cross-sentence reference, semantic jumps, and ellipsis reasoning.

[0112] The semantic correction rule set is dynamically maintained and expanded. Based on statistical indicators recorded in the correction processing log, such as rule hit frequency, coverage failure cases, redundancy trigger rate, and correction effectiveness, the original rules are selected and optimized. For high-frequency rules with a high misjudgment rate, a manual review and marking process is automatically triggered to assist in correcting the rule expression or applicable conditions. For newly emerging but uncovered semantic structures, candidate correction suggestions are generated through cluster analysis and co-occurrence pattern mining and submitted to the rule management list. In this way, the semantic correction rule set continuously evolves, enriches, and converges, effectively maintaining its adaptability to changes in regulatory language and new expression patterns.

[0113] The updated context drift discrimination model and optimized correction rule set are reloaded into the regulatory review process, forming a closed-loop feedback path. In the next round of regulatory review, higher-precision drift discrimination and semantic correction can be performed based on the new model and rules, and the handling of similar contexts will have stronger generalization ability and processing stability. In addition, the evaluation index system is updated simultaneously, including drift recognition accuracy, correction success rate, and suggestion output stability, to measure the optimization effect and drive the triggering of the next learning cycle.

[0114] The purpose of this step is to introduce a feedback learning mechanism, enabling the regulatory compliance review system to possess the ability to self-optimize, self-adapt, and dynamically evolve, thereby continuously improving the depth of understanding of complex legal context structures and the accuracy of identifying context drift issues. In the practical application of legal texts, the language of regulations is constantly evolving, with highly diverse and uncertain expressions. Furthermore, different companies have significantly different business structures, industry standards, and compliance needs, making it difficult for static semantic rules and fixed discrimination models to adapt to newly emerging regulatory structures or contextual patterns in the long term. Therefore, it is essential to establish an optimization mechanism that can continuously learn and adjust based on historical operational processes. This will enable the system to more accurately identify potential context shift issues when dealing with new regulatory provisions or new corporate backgrounds, and to promptly correct inapplicable regulatory recommendations.

[0115] Specifically, this step involves collecting and structurally recording key data during the correction process of regulatory obligations—including contextual feature parameters, rule call logs, and matching judgment results—to analyze and model information such as semantic structure types, correction paths, and judgment errors encountered in this round of processing, forming training samples and the basis for rule revision. On the one hand, this data will be used to optimize the training set of the context drift discrimination model, enabling the model to gradually learn more complex contextual patterns, cross-sentence relationships, semantic jumps, or implicit constraints, thereby improving its ability to predict context drift risks. On the other hand, this data will drive the updating and expansion of the semantic correction rule set, including the discovery of new rules, the revision of high-error rules, and the elimination of inefficient rules, thus ensuring that the semantic correction mechanism always maintains the latest business adaptability and regulatory understanding capabilities.

[0116] Through this feedback-optimized learning and processing mechanism, the regulatory review system no longer relies on static rules set at one time or a discrimination model completed in a single training session. Instead, it enters a closed-loop operation process of "identification-correction-learning-evolution," dynamically adapting to changes in different regulatory contexts and corporate backgrounds during continuous operation, significantly improving the robustness, accuracy, and intelligence of compliance review.

[0117] This invention constructs a hierarchical semantic graph to achieve structured parsing of regulatory provisions, thereby precisely aligning business entities in the enterprise knowledge graph with regulatory semantic units, and building a reasonable and interpretable regulatory-enterprise semantic mapping relationship. Based on this, boundary reasoning and confidence scoring mechanisms are introduced to quantitatively judge the applicability of each regulatory obligation item, and potential application errors are identified and corrected through context backtracking and semantic correction mechanisms. Finally, dynamic evolution of the context model and correction rules is achieved through feedback optimization learning, enabling the system to continuously learn and adapt to changes in different regulatory language. Overall, this method not only improves the contextual consistency and applicability accuracy of regulatory recommendations, but also enhances the intelligence and practicality of compliance review, significantly reducing the risk of misuse and the burden of manual review, demonstrating good engineering implementation value and innovation.

[0118] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0119] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0120] It should be noted that, in this document, the use of relational terms such as "first" and "second" is merely for distinguishing one entity or operation from another, and does 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 the element.

[0121] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0122] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0123] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0124] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0125] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0126] 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.

[0127] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A legal compliance audit response method driven by enterprise knowledge graph, characterized in that, Includes the following steps: S100 performs semantic parsing on the legal text, constructs a context-layered expression model, divides each legal provision into a title semantic block, a main semantic block, a limiting semantic block, and a constraint semantic block, and generates a layered semantic graph. S200, based on a hierarchical semantic graph, uses structured information in the enterprise knowledge graph to annotate semantic units in title semantic blocks, main semantic blocks, limited semantic blocks and constraint semantic blocks, establishes a one-to-one mapping relationship between legal semantic units and enterprise business entities, and constructs a legal-entity semantic mapping matrix. S300, based on the regulatory-entity semantic mapping matrix, combines explicit and implicit conditions in the limiting and constraining semantic blocks to perform applicability condition logic verification, perform boundary reasoning, and construct a regulatory applicability confidence scoring model. Step S300 includes: Extract the conditional expressions from the limiting semantic blocks and constraint semantic blocks of the legal behavioral obligations, and construct a legal condition dependency graph; The corresponding business entities in the enterprise knowledge graph are processed into feature vectors to generate enterprise business background vectors. Input the regulatory condition structure and enterprise feature vector into the boundary reasoning engine, and determine whether the regulatory item meets the applicable conditions based on the rule logic chain and vector similarity calculation. Based on the reasoning results, a confidence scoring model for the applicability of regulations is constructed, which outputs the applicability score and judgment path of the behavioral obligation items; S400, based on the regulatory applicability confidence scoring model, performs context drift judgment on behavioral obligation items with confidence scores in the critical range, calls the context consistency backtracking mechanism to perform context source analysis, and marks behavioral obligation items with context shift risk. S500, for behavioral obligation items with risk of contextual deviation, calls the semantic correction rule set, performs regulatory obligation output correction processing, performs semantic filtering and adaptation correction, and generates compliance recommendations that meet contextual consistency and have applicable accuracy; Step S500 includes: Initialize the semantic correction rule set and associate it with the behavioral obligations marked as having the risk of contextual deviation; Based on semantic filtering rules, identify semantically conflicting items that are inconsistent with the enterprise's business context and perform output removal operations; Based on semantic adaptation correction rules, semantically incomplete entries are processed by subject explicitation, condition completion, and constraint path clarification. Integrate all processing results, reconstruct the set of regulatory obligations outputs, and update the compliance suggestion generation path and context optimization logs; After completing the correction of the legal obligation output, the S600 performs feedback optimization learning processing based on the context feature parameters, rule call logs and judgment results recorded during the context drift discrimination and semantic correction process, updates the context drift discrimination model and semantic correction rule set, and realizes the dynamic evolution and automatic adaptation of the legal review context.

2. The enterprise knowledge graph-driven legal compliance audit response method according to claim 1, characterized in that, Step S100 includes: Obtain the set of legal texts, and perform sentence segmentation and grammatical element annotation; A multi-level text attention mechanism is used to generate context-enhanced word vector representations; Word vectors are input into a conditional random field method for semantic block category prediction; A structured hierarchical semantic graph is generated based on the prediction results, and semantic integrity and structural legality are verified.

3. The enterprise knowledge graph-driven legal compliance audit response method according to claim 1, characterized in that, Step S200 includes: Extract core semantic units from the title semantic block, body semantic block, limiting semantic block, and constraint semantic block, and establish a semantic unit candidate pool; Extract structured entity information from the enterprise knowledge graph and complete entity standardization and semantic expansion processing; A bidirectional semantic matching strategy is adopted, which combines context-enhanced semantic coding method and unified scoring function to calculate the matching score between semantic units and enterprise entities; The semantically aligned mapping relationships are constructed into a legal entity semantic mapping matrix, which records the semantic matching results and path information.

4. The enterprise knowledge graph-driven legal compliance audit response method according to claim 1, characterized in that, Step S400 includes: By screening behavioral obligations whose scores fall within the critical range in the applicability confidence scoring model, a set of questionable legal obligations is constructed. Invoke the context consistency backtracking mechanism to locate the constraint semantic chain of the behavioral obligation item and perform structural backtracking; Based on the backtracking results, perform context consistency analysis to determine whether there are semantic conflicts or potential context drift risks. The identified context-biased risk behaviors and obligations are explicitly marked and the offset information field is recorded.

5. The enterprise knowledge graph-driven legal compliance audit response method according to claim 1, characterized in that, Step S600 includes: Collect context path features, rule call logs, and judgment results recorded during the context drift recognition and semantic correction process, and construct a feedback learning data pool; A semantic feature sample set is generated based on the data pool, and context offset labels are labeled. A supervised learning method is used to train the context drift discrimination model. Based on the correction processing logs, the semantic correction rule set is maintained and expanded to generate correction suggestions and update the rule management list. The updated context drift discrimination model and semantic correction rule set are loaded into the regulatory review process to complete the feedback loop.

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