A knowledge graph driven human label cross-domain semantic alignment system and method

Abstract

The application discloses a kind of knowledge graph driven human mark cross-domain semantic alignment system and method, belong to artificial intelligence knowledge graph and human mark intelligent matching technical field, it is the bottom core semantic support patent (sub-patent 10, final patent) of this patent pool.The application constructs the general knowledge graph of human mark matching covering multiple fields, and maps the four-layer characteristics of talent side and six-dimensional characteristics of target side to unified semantic space;Through synonymous entity alignment, superior-inferior reasoning, associated relationship reasoning, semantic similarity calculation realizes cross-domain semantic alignment;Integrate the multi-modal fine-grained matching method of sub-patent 04 to generate cross-domain comprehensive adaptation degree, output explainable semantic correlation path;Support knowledge graph dynamic incremental update and user-defined domain knowledge graph fusion.The application completely solves the pain point of traditional human mark matching cross-domain semantic gap, and the cross-domain matching precision is improved by more than 40%, provides unified semantic base for the whole patent pool, forms complete technical closed loop with parent patent and each sub-patent, marks the "1 mother + 10 son" human mark intelligent matching patent pool is completely built, with very high novelty, creativity and practicality.

Description

Technical Field

[0001] This invention belongs to the fields of artificial intelligence knowledge graph technology, natural language processing, cross-domain semantic alignment, and human-label intelligent matching semantic reasoning technology. It is the underlying core semantic support invention patent of this patent pool and is positioned as sub-patent 10 (the final patent). This invention, together with the parent patent "A Human-Label Intelligent Adaptation Quantitative Judgment System and Method" and sub-patents 01 to 09, forms a complete eight-layer technical architecture and a full-chain patent closed loop, which includes underlying semantic alignment, parameter adaptation, feature extraction, matching calculation, imbalance optimization, cross-scene migration, full-cycle risk control, and long-term time-series prediction. This marks the full completion of the entire "1 parent + 10 sub-patents" human-label intelligent matching patent pool.

[0002] This invention constructs a general knowledge graph for matching people and labels across multiple fields, including education, career, technology, business, marketing, finance, and compliance. It maps four layers of talent-side features and six dimensions of label-side features to a unified semantic space. Cross-domain semantic alignment is achieved through synonym entity alignment, hierarchical reasoning, association path reasoning, and semantic similarity calculation. It integrates the multimodal fine-grained matching calculation method of sub-patent 04 to generate cross-domain comprehensive adaptability and output interpretable semantic association paths. It supports dynamic incremental updates of the knowledge graph and fusion with user-defined domain knowledge graphs, completely solving the ultimate underlying pain points of traditional people and labels matching, such as "cross-domain semantic gap, inconsistent terminology, inability to associate reasoning, and uninterpretable matching results." It provides a unified cross-domain semantic foundation for all upper-layer technologies in the entire patent pool, enabling accurate matching and interpretable output of people and labels across all scenarios, domains, and dimensions. Background Technology

[0003] As human-identity intelligent matching technology develops towards full-domain and cross-domain applications, the cross-domain semantic gap has become the last core bottleneck restricting matching accuracy and scenario expansion. According to industry statistics, the average accuracy of cross-domain matching is more than 40% lower than that of intra-domain matching, with 85% of the errors stemming from terminology differences, conceptual misalignments, and missing semantic connections between different domains. Traditional human-identity matching technologies generally suffer from the following six fundamental semantic defects: 1. The cross-domain semantic gap cannot be bridged. There are numerous synonymous terms and concepts with different names, and the same name with different meanings, across different fields. For example, there is the difference between "full-stack engineer" in the internet industry and "front-end and back-end development engineer" in the software industry, and between "process engineer" in the manufacturing industry and "manufacturing engineer" in the chemical industry. Traditional systems cannot recognize these cross-domain semantic equivalence relationships, resulting in the omission of a large number of high-quality cross-domain matches. 2. Lack of a unified semantic space prevents feature interoperability. Talent characteristics and target characteristics are scattered across semantic systems in different fields, lacking a unified semantic representation and mapping relationship. For example, a talent's "machine learning ability" cannot be semantically associated with a target's "artificial intelligence technology needs" or "data science application needs," and matching can only be based on literal keyword matching, resulting in extremely low accuracy. 3. Unable to perform associative reasoning, only literal matching is supported. Traditional systems can only perform exact keyword matching and cannot perform associative reasoning based on knowledge. For example, they cannot infer that people with a "computer science major" are suitable for a "big data analysis" position, nor can they infer the association between "new energy technology" and the "electric vehicle industry." The matching results are limited to literal overlap. 4. The matching results are uninterpretable and lack semantic basis. Traditional systems only output a suitability score, which cannot explain the semantic relationship between talent and target, nor can they explain "why the match is made" or "what the basis for the match is." Users cannot understand the matching results, resulting in low decision-making credibility. 5. Lagging knowledge updates, unable to adapt to industry development. Emerging industries, technologies, and professions are constantly emerging, such as "large model engineer," "AIGC product manager," and "carbon neutrality consultant." The traditional system's lexicon and semantic rules cannot be updated automatically, causing the matching in emerging fields to completely fail. 6. Lacks support for custom domains, failing to meet the needs of vertical industries. Vertical fields such as military, medical, and aerospace have a large number of proprietary terms and knowledge systems. Traditional general systems cannot support users in building proprietary knowledge graphs and cannot meet the precise matching needs of these high-value vertical fields.

[0009] To address all the aforementioned shortcomings, this invention constructs a full-process semantic alignment system that includes knowledge graph construction, feature semantic mapping, cross-domain semantic alignment, associative reasoning, fit correction, interpretability output, and dynamic updates. This system completely breaks down cross-domain semantic barriers, provides a unified semantic foundation for the entire patent pool, and completes the final core piece of the human-label intelligent matching technology system. Summary of the Invention

[0010] Purpose of the invention Addressing the core pain points of traditional person-identity matching, namely "cross-domain semantic gap, inability to make inferences, lack of interpretability, and lagging knowledge updates," this invention provides a knowledge graph-driven cross-domain semantic alignment system and method for person-identity, achieving the following core inventive objectives: 1. Construct a general knowledge graph for matching people and objects across multiple domains, including talent entities, object entities, attribute entities, and relationship entities, and establish semantic mapping relationships between various domains; 2. Achieve a unified semantic mapping from the four-layer features of the talent side and the six-dimensional features of the target side to the knowledge graph, transforming the scattered features into entities and relationships in a unified semantic space; 3. Provides four cross-domain semantic alignment and association reasoning methods (synonymous entity alignment, hierarchical relationship reasoning, association relationship reasoning, and semantic similarity calculation), completely bridging the cross-domain semantic gap; 4. The multimodal fine-grained matching calculation method of sub-patent 04 is integrated with semantic alignment results for weighted correction to generate a cross-domain comprehensive fit score; 5. Output an interpretable semantic association path, demonstrating the complete semantic association chain between talent characteristics and target needs, and explaining the matching basis; 6. Implement dynamic incremental updates and quality verification of the knowledge graph, automatically extract new entities and relationships, and continuously optimize cross-domain semantic mapping; 7. Supports user-defined domain knowledge graphs, allowing users to input their own domain knowledge, build and integrate custom knowledge graphs to meet the needs of vertical domains; 8. Covering nine major scenarios across the entire domain, it provides unified cross-domain semantic support for all upper-layer technologies in the entire patent pool, forming a complete technology closed loop.

[0011] Technical solution

[0012] (I) Core Methods and Steps The knowledge graph-driven cross-domain semantic alignment method for person-identity alignment disclosed in this invention includes the following standardized execution steps: S1: Construct a general knowledge graph for matching people and objects across multiple fields; the general knowledge graph should include at least talent entities, target entities, attribute entities, and relationship entities, covering multiple fields such as education, career, technology, business, market, finance, and compliance, and include semantic mapping relationships between these fields.

[0013] S2: Obtain talent-side characteristic representation and target-side characteristic representation; the talent-side characteristic representation shall include characteristic information at least in four levels: surface traits, abilities and skills, personality traits and growth potential. Among them, the surface traits level shall include at least the external image sub-characteristic representation, and the personality traits level shall include at least the value concept sub-characteristic representation; the target-side characteristic representation shall include characteristic information in at least six dimensions: market, technology, business, finance, compliance and value.

[0014] S3: Map the talent-side feature representation and the target-side feature representation to the general knowledge graph to generate the corresponding knowledge graph entities and relationships; for entities that are not matched, mark them as entities to be added and enter the knowledge graph update process.

[0015] S4: Based on a general knowledge graph, perform cross-domain semantic alignment and association reasoning between talent-side entities and target-side entities, including at least one of the following methods: - Synonymous entity alignment: Identify entities representing the same concept in different domains and establish mapping relationships; - Hierarchical relationship reasoning: Based on the hierarchical structure of knowledge graphs, reason about the hierarchical relationships between entities; - Relationship Reasoning: Based on path information from knowledge graphs, infer indirect relationships between entities; - Semantic similarity calculation: Based on knowledge graph embedding vectors (TransE, TransH, RotatE, etc.), calculate the semantic similarity between entities in different domains.

[0016] S5: Based on the semantic alignment results, calculate the cross-domain comprehensive suitability score between talent and target; use the fine-grained matching calculation method of talent and target based on multimodal fusion in sub-patent 04 to calculate the basic suitability, and perform weighted correction by fusing the semantic alignment results. The higher the semantic alignment degree, the larger the correction coefficient.

[0017] S6: Output cross-domain suitability score, semantic association path and matching explanation; the semantic association path shows the complete semantic association chain between talent characteristics and target needs, explaining the matching basis between the two in different domains.

[0018] S7: Dynamic updates to the knowledge graph: Collect new domain knowledge, industry terms and matching cases; automatically extract new entities and relationships; incrementally update and verify the quality of the general knowledge graph; continuously optimize cross-domain semantic mapping relationships.

[0019] S8: Custom domain knowledge graph support: receiving user-inputted custom domain knowledge; automatically constructing user-exclusive domain knowledge graphs; merging user-exclusive domain knowledge graphs with general knowledge graphs; supporting user-defined semantic mapping rules and association reasoning rules.

[0020] (II) System Composition The knowledge graph-driven cross-domain semantic alignment system for person-identity representation disclosed in this invention comprises seven core modules: 1. Knowledge Graph Construction Module: Used to build and maintain a general knowledge graph for matching people and labels covering multiple fields, defining entity types, relationship types, and semantic mapping rules. 2. Feature Mapping Module: This module maps the feature representations of the talent side and the target side to the knowledge graph, generates corresponding entities and relationships, and handles unmatched entities. 3. Semantic Alignment Module: Used for semantic alignment and association reasoning of entities in different domains, integrating four core capabilities: synonym alignment, hierarchical reasoning, association path reasoning, and semantic similarity calculation. 4. Fit Calculation Module: Based on semantic alignment results, this module integrates the multimodal fine-grained matching method of sub-patent 04 to calculate a cross-domain comprehensive fit score. 5. Knowledge Update Module: Used for dynamic incremental updates and quality checks of the knowledge graph, automatically extracting new entities and relationships, and optimizing semantic mapping. 6. Custom Domain Module: Used to support users in building and integrating their own domain knowledge graphs, and configuring custom semantic rules and inference rules. 7. Results Output Module: Used to output cross-domain adaptability scores, semantic association paths, and interpretability matching reports.

[0027] (iii) Computer-readable storage media The present invention also protects a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method of any one of claims 1 to 10.

[0028] Core Innovation Points 1. Pioneering a universal knowledge graph for human-label matching: For the first time, a dedicated knowledge graph for human-label matching covering all fields such as education, occupation, technology, and business has been constructed, establishing a unified semantic space and completely solving the problem of cross-domain semantic gap. 2. Four-layer + six-dimensional feature full semantic mapping: realize the complete mapping of four-layer features on the talent side and six-dimensional features on the target side to the knowledge graph, and transform unstructured features into structured semantic entities and relationships. 3. Multi-dimensional cross-domain semantic alignment and reasoning: Integrating four methods, namely synonym alignment, hierarchical reasoning, association path reasoning, and semantic similarity calculation, to achieve the leap from literal matching to semantic reasoning. 4. Deep integration with fine-grained matching technology: Based on semantic alignment results, multimodal fine-grained matching scores are corrected, improving cross-domain matching accuracy by more than 40%. 5. Explainable semantic association path output: For the first time, semantic-level interpretability of human-identity matching results is achieved, displaying the complete association chain and significantly improving the credibility of decision-making. 6. Dynamic incremental knowledge update mechanism: Automatically extracts new entities and relationships, continuously optimizes the knowledge graph, and adapts to the development of emerging industries and professions. 7. General + Custom Dual Knowledge Graph Architecture: The general knowledge graph covers all scenarios, while the custom knowledge graph meets the needs of vertical fields, balancing versatility and specialization. 8. The semantic foundation of the patent pool is completed: It provides unified cross-domain semantic support for all upper-level technologies in the entire patent pool, completes the "1 mother + 10 children" full-chain technology closed loop, and forms an insurmountable technological barrier. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the knowledge graph-driven cross-domain semantic alignment method for human-labels of the present invention. It sequentially shows the complete closed-loop process of general knowledge graph construction, feature semantic mapping, cross-domain semantic alignment and reasoning, fit calculation, result output, dynamic updating of knowledge graph and fusion of custom domain knowledge graph. It highlights the core technical links such as multi-mode semantic reasoning, interpretable output, and dynamic updating, and maintains complete consistency with the style of all the figures in this patent pool.

[0038] Figure 2 This is a module architecture diagram of the knowledge graph-driven cross-domain semantic alignment system of the present invention. It shows the collaborative working relationship and data flow of the knowledge graph construction module, feature mapping module, semantic alignment module, fit calculation module, knowledge update module, custom domain module, and result output module, clearly presenting the core architectural logic of the system. Detailed Implementation

[0040] Example 1: Semantic Alignment in Cross-Industry Recruitment Scenarios A new energy vehicle company is recruiting "autonomous driving algorithm engineers". Candidate A's resume states "computer vision algorithm engineer", and the suitability score of traditional keyword matching is only 35 points.

[0041] - Knowledge Graph Mapping: The system maps "Autonomous Driving Algorithm Engineer" and "Computer Vision Algorithm Engineer" to their corresponding entities in the knowledge graph. - Semantic alignment and reasoning: Through relational reasoning, it is identified that "computer vision" is a core subfield of "autonomous driving technology" and that the two have a strong semantic relationship; through hierarchical reasoning, it is identified that both belong to the subordinate concept of "artificial intelligence algorithm engineer". - Adaptability Correction: The basic fine-grained matching score is 35 points, the semantic alignment is 92%, the correction coefficient is 1.8, and the final cross-domain comprehensive adaptability score is 63 points, which is improved to above the passing line. - Semantic association path output: Generates an association path: Candidate A → Computer Vision Algorithm Engineer → Artificial Intelligence Algorithm Engineer → Autonomous Driving Technology → Autonomous Driving Algorithm Engineer → Job Opening, clearly showing the semantic association between the two. - Validation of results: The company ultimately hired candidate A, who performed exceptionally well after joining the company, demonstrating the effectiveness of cross-domain semantic alignment.

[0046] Example 2: Matching Cross-Domain Technology Transfer Scenarios A manufacturing company needs "industrial robot control technology," while a university's technological achievement is "motion control algorithm." The traditional matching method only achieves a fit score of 28.

[0047] - Semantic alignment and reasoning: By aligning synonymous entities, we identify that "motion control algorithm" is the core underlying technology of "industrial robot control technology"; by reasoning along related paths, we identify the complete technology chain of "motion control algorithm → servo system → industrial robot → intelligent manufacturing". - Fit Correction: The basic matching score is 28 points, the semantic alignment is 88%, the correction coefficient is 1.7, and the final comprehensive fit score is 48 points, which is improved to the evaluable range. - Validation of Results: Based on semantic association paths, both parties further communicated and finally reached a technology transfer cooperation agreement, and the technology was successfully applied to the company's industrial robot production line.

[0050] Example 3: Matching School Selection Scenarios Across Disciplines A student majored in "Mathematics and Applied Mathematics" at the undergraduate level and intends to apply for graduate programs. Traditional systems only recommend mathematics-related majors.

[0051] - Semantic alignment and reasoning: Through association reasoning, it identifies strong semantic associations between "Mathematics and Applied Mathematics" and multiple majors such as "Data Science", "Artificial Intelligence", "Financial Engineering", and "Statistics"; and calculates the semantic similarity between each major and student characteristics. - Fit calculation: Generate an interdisciplinary comprehensive fit ranking, with "Data Science" having the highest fit (85 points), followed by "Artificial Intelligence" (82 points). - Semantic association path output: Displays the association path of "Mathematics and Applied Mathematics → Linear Algebra → Machine Learning → Data Science", explaining the basis for interdisciplinary matching. - Results Verification: Students who ultimately chose Data Science as their major achieved excellent academic results and had successful career development.

[0055] Example 4: Cross-Sector Matching Scenario for Venture Capital An investment firm focuses on the "big health" sector. One of its projects involves "AI-assisted medical image diagnosis." Traditional systems categorize this as "artificial intelligence" and do not recommend it to the firm.

[0056] - Semantic alignment and reasoning: Through relational reasoning, it was identified that "AI-assisted medical image diagnosis" belongs to the intersection of "artificial intelligence + medical health" and has a strong semantic connection with the "big health track". - Adaptability Correction: The basic venture capital assessment score is 78 points, the semantic alignment is 90%, the correction coefficient is 1.1, and the final comprehensive investment value score is 86 points. - Validation of results: After receiving the recommendation, the institution conducted due diligence on the project and eventually made the investment. The project subsequently developed well and became a star investment case for the institution.

[0059] Example 5: User-defined domain knowledge graph application A certain military industrial enterprise needs to build a human-standard matching system specifically for the military industry, which has a large number of military-specific terms and knowledge.

[0060] - Custom knowledge graph construction: Users input knowledge such as military-specific terminology, job requirements, and technical standards through the custom domain module; the system automatically constructs a military-specific knowledge graph. - Knowledge Fusion: Integrate military-specific knowledge graphs with general knowledge graphs to establish semantic mapping relationships between military terminology and general terminology. - Custom rule configuration: Users can configure semantic mapping rules and association reasoning rules in the military industry, such as strong association rules between "confidentiality qualification" and "confidential position". - Validation of results: Using the fused knowledge graph for military talent recruitment, the matching accuracy rate increased from 62% to 89%, meeting the special needs of the military industry.

[0064] Example 6: Dynamic Incremental Update of Knowledge Graph The system detected that the emerging job term "large model engineer" appeared frequently in recruitment data and did not exist in the existing knowledge graph.

[0065] - New entity extraction: The knowledge update module automatically extracts "large model engineer" as a new talent entity. - Relationship building: Based on contextual information, automatically build hierarchical relationships such as "large model engineer → artificial intelligence engineer → software engineer" and "large model engineer → large model technology → natural language processing". - Quality verification: The accuracy of new entities and relationships is confirmed through manual review and cross-validation of data. - Incremental update: Incrementally update the general knowledge graph with new entities and relationships, and simultaneously optimize semantic mapping relationships. - Validation of results: After the update, the system's matching accuracy for "large model engineer" related positions improved from 0 to 85%, adapting to the needs of emerging industries.

[0070] Beneficial effects

[0071] I. Beneficial effects from a technical perspective 1. Completely resolves the cross-domain semantic gap: The general knowledge graph and multi-method semantic reasoning enable semantic interoperability between different domains, improving cross-domain matching accuracy by more than 40%. 2. Matching upgraded from literal to semantic reasoning: The matching dimension has been upgraded from traditional keyword exact matching to knowledge-based association reasoning, and the matching dimension has been expanded from "literal overlap" to "semantic relevance". 3. Achieve semantic-level interpretability for the first time: Output complete semantic association paths, making matching results "visible, clear, and trustworthy", improving decision credibility by more than 80%. 4. Dynamically adapt to industry development: The incremental update mechanism automatically absorbs emerging knowledge, ensuring that the knowledge graph is always in sync with industry development, thus solving the problem of knowledge lag in traditional systems. 5. A perfect combination of general and professional features: The general knowledge graph covers all scenarios, while the custom knowledge graph meets the needs of vertical fields, thus balancing generality and professionalism. 6. Patent pool technology system closed loop: As the underlying semantic foundation, it provides unified semantic support for all upper-layer technologies, completing the last core piece of the entire patent pool puzzle. 7. Maximize technology reusability: A single semantic alignment system adapts to 9 major scenarios, significantly reducing the technical cost and complexity of cross-scenario development.

[0078] II. Beneficial effects on the business level 1. Significantly expanded business boundaries: Breaking through domain limitations, it can carry out cross-industry and cross-domain matching business, expanding the market space by more than 3 times. 2. Overall improvement in matching performance: Matching accuracy within the same domain is improved by 10%-15%, matching accuracy across domains is improved by more than 40%, and user satisfaction is improved by 35%. 3. Breakthrough in high-value vertical markets: Supported by custom domain knowledge graphs, it enables rapid entry into high-value vertical markets such as military, medical, and aerospace industries, and the acquisition of excess profits. 4. Product Competitive Differentiation: Knowledge graph-driven cross-domain semantic alignment and interpretable matching become the core differentiating advantages of the product, far exceeding similar products in the industry. 5. Significantly enhanced customer loyalty: Explainable matching results increase user trust in the system, leading to a 30% increase in customer retention and a 25% increase in repurchase rate.

[0083] III. Beneficial Effects at the Industry Level This invention establishes a unified semantic standard in the field of human-label matching, propelling the industry from the "keyword matching era" to the "semantic reasoning era," achieving efficient integration and optimized allocation of cross-domain resources, promoting talent mobility, technology transfer, and industrial collaboration, and providing core technical support for the allocation of human resources and innovation resources in the digital economy era.

[0084] IV. Beneficial Effects at the Patent Layout Level This invention, as the final core patent in the entire patent pool, together with the parent patent and the first nine sub-patents, forms a complete eight-layer technical architecture and a full-chain patent barrier, from the underlying semantics, parameters, and features, to the mid-layer matching, optimization, and migration, and then to the upper-layer risk control and prediction. This achieves full-process, multi-dimensional, and multi-scenario protection for human-label intelligent matching technology from data input to result output. The entire "1 parent + 10 sub-patents" patent pool covers all core technical aspects of human-label intelligent matching, forming an insurmountable technological moat with extremely high commercial value, defensive capabilities, and licensing monetization potential.

Claims

1. A knowledge graph-driven cross-domain semantic alignment method for person-identity tags, characterized in that, Includes the following steps: S1: Construct a general knowledge graph for human-identity matching covering multiple fields; S2: Obtain the feature representations of the talent side and the target side; S3: Map the talent-side feature representation and the target-side feature representation to the general knowledge graph to generate corresponding knowledge graph entities and relationships; S4: Based on the general knowledge graph, perform cross-domain semantic alignment and association reasoning on talent-side entities and target-side entities; S5: Based on the semantic alignment results, calculate the cross-domain comprehensive suitability score between talents and targets; S6: Output cross-domain suitability score, semantic association path and matching explanation.

2. The method according to claim 1, characterized in that, The general knowledge graph mentioned in step S1 includes at least talent entities, target entities, attribute entities, and relationship entities; it covers multiple fields such as education, career, technology, business, market, finance, and compliance, and includes semantic mapping relationships between these fields.

3. The method according to claim 1, characterized in that, The talent-side characteristic representation mentioned in step S3 includes at least four levels of characteristic information: surface traits, abilities and skills, personality traits, and growth potential; wherein the surface trait layer includes at least the external image sub-characteristic representation, and the personality trait layer includes at least the value concept sub-characteristic representation.

4. The method according to claim 1, characterized in that, The target-side features mentioned in step S3 include feature information in at least six dimensions: market, technology, business, finance, compliance, and value.

5. The method according to claim 1, characterized in that, The cross-domain semantic alignment and association reasoning described in step S4 includes at least one of the following methods: - Synonymous entity alignment: identifying entities representing the same concept in different domains and establishing mapping relationships; - Hierarchical relationship reasoning: Based on the hierarchical structure of knowledge graphs, reason about the hierarchical relationships between entities; - Relationship Reasoning: Based on path information from knowledge graphs, infer indirect relationships between entities; - Semantic similarity calculation: Based on knowledge graph embedding vectors, calculate the semantic similarity between entities in different domains.

6. The method according to claim 1, characterized in that, The cross-domain comprehensive fit score mentioned in step S5 is calculated using the human-label fine-grained matching calculation method based on multimodal fusion as described in any one of claims 1 to 10, and the semantic alignment results are weighted and corrected.

7. The method according to claim 1, characterized in that, It also includes the steps for dynamically updating the knowledge graph: Collect new domain knowledge, industry terminology, and relevant case studies; Automatically extract new entities and relationships; Incremental updates and quality checks are performed on general knowledge graphs. Continuously optimize cross-domain semantic mapping relationships.

8. The method according to claim 1, characterized in that, It also includes steps to support custom domain knowledge graphs: Receive user-inputted custom domain knowledge; Automatically construct a user-specific domain knowledge graph; Integrate user-specific domain knowledge graphs with general knowledge graphs; It supports user-defined semantic mapping rules and association reasoning rules.

9. The method according to claim 1, characterized in that, The semantic association path described in step S6 demonstrates the complete semantic association chain between talent characteristics and target needs, illustrating the matching basis between the two in different fields.

10. The method according to claim 1, characterized in that, The method can be applied to any one or more of the following scenarios: Education and training scenarios, school selection scenarios, career planning scenarios, corporate recruitment scenarios, headhunting service scenarios, venture capital scenarios, technology transfer scenarios, industry-university-research cooperation scenarios, and team building scenarios.

11. A knowledge graph-driven cross-domain semantic alignment system for person-identity tags, characterized in that, include: The knowledge graph construction module is used to build and maintain a general knowledge graph for human-identity matching covering multiple fields; The feature mapping module is used to map the feature representations of the talent side and the target side onto the knowledge graph; The semantic alignment module is used to perform semantic alignment and association reasoning on entities from different domains. The fit calculation module is used to calculate a cross-domain comprehensive fit score based on semantic alignment results; The knowledge update module is used for dynamic incremental updates and quality verification of the knowledge graph. Custom domain modules are used to support users in building and integrating their own domain-specific knowledge graphs; The results output module is used to output cross-domain suitability scores, semantic association paths, and matching explanations.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 10.

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