Multi-language semantic intention matching system for AI-assisted recruitment

Abstract

The invention discloses a multilingual semantic intention matching system for AI-assisted recruitment, and belongs to the field of artificial intelligence and human resource management. According to the system, a DIKWP cognitive model is used for carrying out multilayer semantic analysis on candidate texts, and candidate language expressions are mapped from a concept space to a semantic space of post requirements. By constructing a cross-language post semantic knowledge base and a semantic matching engine, intelligent matching between the implicit ability of candidates and post competency requirements is realized. The system supports a multilingual environment, can identify equivalent skill intentions under different languages and culture expressions, and improves the precision and fairness of globalized recruitment. The system has a white-box interpretable characteristic, provides a transparent description of a matching basis, and is beneficial to trust and optimization of recruitment decisions.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of artificial intelligence and human resource management, and specifically relates to an AI-assisted talent recruitment semantic matching system, which can map the language expression of candidates to the semantic intention of job adaptation in a multilingual environment. The system also involves cross-technology such as natural language processing, knowledge graph, and cross-cultural semantic understanding. BACKGROUND

[0002] Under the trend of economic globalization and internationalization of talents, enterprises face challenges brought by multilingual and multicultural environments in recruitment. Traditional recruitment screening mainly relies on manual reading of resumes and keyword-based matching. When candidates and job descriptions use different languages or adopt different expression methods, recruiters often have difficulty in finding the right talents in a timely and accurate manner. This leads to the possibility of missing excellent candidates or reducing the efficiency of recruitment due to language barriers and semantic misunderstandings. In practice, global enterprises may need to spend a lot of time and cost on resume translation or hire recruitment specialists familiar with multiple languages to screen one by one, but this is difficult to implement efficiently in large-scale talent screening.

[0003] Some existing AI recruitment systems have begun to introduce natural language processing (NLP) and knowledge graph technology to assist in resume analysis, enabling multilingual collaborative processing, thereby breaking down language barriers and improving the efficiency of global talent acquisition and allocation. For example, with the help of artificial intelligence, enterprises can preliminarily screen tens of thousands of resumes containing multiple languages within a few hours, understand the hidden skills and abilities of candidates from the semantic understanding, and achieve cross-regional and multilingual collaborative recruitment. These systems usually convert resumes in different languages into a unified language through machine translation, and then perform keyword matching or screening based on predefined rules. To some extent, this method can improve the speed of preliminary screening and alleviate the processing difficulties brought by different language resumes. However, current intelligent recruitment screening is still mainly based on surface keywords and simple rule matching, and lacks effective means for precise correspondence between the deep semantic intention of candidate self-description and job requirements.

[0004] When there is a conceptual difference between the language expression of the candidate and the job requirements (such as industry term difference, cultural expression difference, etc.), the traditional method is difficult to identify the semantic equivalence of the two. This means that if the candidate uses different wording or language in the resume than the job description, even if the actual ability is consistent, it may not be identified, thereby there is a risk of potential person-job matching degree being underestimated or directly missed. For example, a candidate describes himself as "led a global project with a diverse team" in an English resume, while the job requirements in Chinese mention the need for "experience in managing cross-border project teams". Traditional keyword matching may not be able to correspond the two due to language and wording differences, but semantically the two are equivalent requirements. Similarly, different cultural backgrounds may also lead to misjudgment in expressing the same skill or experience. Therefore, the existing technology has obvious shortcomings in cross-language and cross-cultural talent recruitment screening, and an intelligent system that can understand the deep semantic intent of the candidate and match the job requirements across languages is urgently needed. SUMMARY

[0005] The present application provides an AI-assisted recruitment multi-lingual semantic intent matching system, aiming to solve the problem of inaccurate person-job matching in the multi-language and multi-expression scenarios in the prior art. The system is based on the data-information-knowledge-wisdom-intent (DIKWP) cognitive model, which migrates the text content provided by the candidate from the original concept space to the semantic space of the job requirements for deep cognitive fusion processing. Through semantic analysis of different language resumes, cover letters and interview answers, the job competence intent behind the candidate's expression is extracted layer by layer, and these intents are mapped to the standardized defined job requirement semantics. The core of the invention is to establish a multi-lingual and multi-domain general semantic intent representation layer, which converts the candidate's conceptual expression (such as skills, experience, self-evaluation, etc.) into a comparable intent representation in the semantic space of the job requirements. Using the "semantic sovereignty" idea, the system has built-in localized knowledge base and semantic rules to ensure that expressions in different languages and cultural backgrounds can be accurately and fairly understood and represented, thereby maintaining the organization's autonomy and consistency in semantic understanding. Through the above methods and strategies, the present application realizes the intelligent migration and fusion from the candidate's conceptual expression to the job semantic requirements, greatly improving the accuracy and fairness of cross-language person-job matching.

[0006] Compared with the prior art, the present application has the following beneficial effects:

[0007] 1. Improve cross-language matching accuracy: Through multilingual semantic embedding and knowledge reasoning, the invention can identify equivalent skills and intentions under different language expressions, significantly improving the matching accuracy of candidates and job requirements in cross-language situations, and reducing the missed selection caused by language differences.

[0008] 2. Maintain semantic understanding fairness and autonomy: Introduce semantic sovereignty mechanism, build localized semantic knowledge base and rules, realize consistent interpretation of multilingual semantics, avoid distortion of semantic understanding caused by external translation or third-party model bias, ensure fair and just recruitment process for candidates of different languages.

[0009] 3. Improve recruitment screening efficiency: With the help of AI automatic semantic analysis, the system can process a large amount of multilingual resumes and evaluation texts at high speed, intelligently connect the implicit ability of candidates with the job requirements. According to the trial statistics, the system can improve the efficiency of initial screening by more than 50%, greatly reducing the burden of manual screening. At the same time, the system can discover the "hidden talents" easily missed by traditional methods, and enhance the effect of talent acquisition of enterprises.

[0010] 4. Provide decision transparency and explainability: The invention has white-box explainable characteristics, and automatically gives semantic basis explanation of matching results. Recruitment personnel can see which experiences of candidates meet which requirements of the job, as well as the reasoning chain of cross-language semantic equivalence. This transparent explanation is conducive to improving the trust of decision-making, facilitating enterprise audit and regulatory compliance, and reducing the risk of algorithm discrimination.

[0011] In summary, the system of the invention can realize accurate matching of the semantic intention of the deep competence of the candidate and the job requirements in the multilingual and multicultural talent recruitment scene, and balance efficiency and fairness, providing a novel and effective technical solution for global recruitment.

[0012] To achieve the above invention content, the invention provides the following technical solutions:

[0013] (1) Multilingual semantic analysis module: using natural language processing technology, pre-processing and analyzing different language texts provided by candidates, including language automatic recognition, word segmentation, entity recognition and necessary preliminary translation alignment. This module supports multilingual and multi-modal input, which can process texts in multiple languages such as Chinese and English, and can also receive non-structured data such as voice transcription text and video interview subtitles and analyze them, so that various expression channels of candidates are included in unified analysis. In the analysis process, the system identifies key skills, experience, certificates, career intentions and other information elements from the text. After preprocessing, the multilingual semantic embedding model is used to map the above text content to a language-independent vector representation space, which maximizes the preservation of semantic information. A typical approach is to use a pre-trained cross-language deep language model to convert sentences into semantic vectors such that semantically similar sentences (regardless of language) are closer in the vector space. Through this cross-lingual vector embedding representation, the semantic parsing module achieves semantic alignment of different language expressions, laying the foundation for subsequent intent extraction.

[0014] (2) DIKWP intent extraction module: based on the DIKWP cognitive model, the parsed text information is subjected to multi-level semantic extraction, and the candidate's implied competence intent (such as Figure 1 As shown). First, the objective facts are extracted at the data layer, such as the candidate's educational background, certificates, work experience, etc. explicit data; then the context information is integrated at the information layer, and the basic experience elements are identified, for example, the candidate's work experience in a certain industry, the position held, etc.; then at the knowledge layer, the information layer results are reasoned and expanded in combination with domain knowledge, for example, according to the extracted information, the candidate's professional skill category or industry background is inferred, and the implied ability is upgraded to an abstract competence concept; further at the wisdom layer, through higher-level cognitive reasoning, the candidate's possible soft skills and comprehensive abilities are mined, for example, according to the candidate's performance in a team project, the abstract abilities such as "team collaboration ability" or "rapid learning ability" are inferred; finally, at the intent layer, the candidate's career motivation and job preference and other deep intentions are identified, for example, analyzing the candidate's self-description can extract his "pursuit of technical innovation" or "hope to lead the team to achieve business growth" and other career goal intentions. Through the gradual processing of the five levels, the DIKWP module refines the candidate's explicit and implicit information scattered in the text into a structured candidate semantic intent representation. For example, the candidate describes "leading the team to complete a cross-border project", the data layer records the objective elements of the experience (team size, project nature, etc.), and the knowledge layer and wisdom layer reason that the experience embodies "team leadership" "cross-cultural communication" and other abilities, and the intent layer further confirms that this implies that the candidate has the willingness to undertake international projects and management responsibilities. The above DIKWP multi-level processing process forms a comprehensive portrait of the candidate's competence intent.

[0015] (3) Position requirement semantic library: A standardized position semantic intent library is constructed to store the semantic requirements of various positions for skills, experience, and quality in the form of a knowledge graph. This semantic library integrates industry ontology and position competence models, and unifies different expression methods to a common position capability concept node. For example, "proficient in Java" and "mastering object-oriented programming" point to the same skill intent in semantics and can be merged into the same node in the knowledge graph. The core skills, experience, and quality nodes required by each position are defined in advance in the semantic library, and their association relationships are established. For example, the skill ontology is introduced as a cross-role and industry skill hierarchy, connecting skill concepts in different career fields and enabling similar or related skills to have associated paths. Through the construction of the position semantic library, not only is the standardized description of position requirements achieved, but also the foundation for cross-language and cross-domain semantic equivalence and analogy is provided. For example, different language position descriptions are linked to the same skill node, and different expressions of soft skills (such as "good at communication" and "strong communication skills") are unified into the same concept, thereby establishing a unified semantic standard. The semantic library can also extend new nodes or relationships as needed to adapt to evolving emerging skills and industry requirements.

[0016] (4) Semantic intent matching engine: This module is used to calculate the matching degree between the candidate's intent representation and the position requirement semantic node, and output the score and result of the person-job match. The matching engine uses a combination of semantic vector calculation and knowledge reasoning: on the one hand, the direct matching degree is measured based on the similarity between the candidate's intent vector and the position requirement vector The semantic similarity can be calculated using the cosine similarity formula, for example:

[0017]

[0018] where represents the semantic similarity between the candidate's intent representation and the position requirement representation and ​respectively. On the other hand, the matching engine utilizes the association relations in the knowledge base for reasoning matching: when the skills or experiences explicitly mentioned by a candidate do not directly correspond to the job requirements, the system infers the equivalence or transferability of the candidate's skills through the pre-set skill ontology relations, thereby identifying hidden matching opportunities. For example, if the job requires "project management" ability, and the candidate does not explicitly write this word, but his experience contains "team coordination", the knowledge graph defines that "project management" and "team coordination" belong to associated abilities, then the engine can infer that the candidate is partially matched on this requirement. Similarly, if the candidate is proficient in a programming language, the knowledge base infers that the language has a certain degree of correspondence with another language required by the job in terms of programming paradigm or technology stack, and it can also be counted as a certain matching score. The matching engine combines the semantic vector similarity calculation and the knowledge reasoning matching results according to a certain weight strategy to form a comprehensive matching score. In this process, the semantic sovereignty strategy is introduced: the system can dynamically adjust the weight coefficients of the matching rules according to local policies or enterprise preferences, in order to realize the customized optimization selection of talent screening. For example, an organization can increase the weight of a certain skill or educational background, so that the candidate who meets its employment orientation has a higher score. This semantic sovereignty adjustment mechanism ensures that the system matching results not only follow the general semantic criteria, but also reflect the autonomous preferences of the organization.

[0019] (5) Explanation and feedback module: the system has the characteristics of white-box explainable AI. For each matching result, the system will give the reason explanation supporting the matching score. For example, it points out that a certain experience in the candidate's resume embodies a certain ability required by the job, and the equivalent intention association under different language descriptions. This explainable matching explanation enables the recruitment personnel to understand and audit the automatic screening decision, which is crucial for compliance supervision, candidate communication and risk management. For example, the system may explain that the candidate's "overseas volunteer experience" corresponds to the "cross-cultural communication ability" required by the job, and his "computer major from a 985 university" meets the preference of the job for higher education background, etc. These explanations transparentize the semantic equivalence relations and reasoning links implied in the matching process, significantly improving the traceability and credibility of the results. In addition to providing explanations, this module also supports user feedback functions: recruitment personnel can evaluate and adjust the system's matching results and explanations, such as marking some recommendations as inaccurate, or adjusting the priority of some skill matching according to the latest enterprise policies. The system will incorporate these manual feedbacks into the model updating process, continuously optimizing the parameters of the semantic matching model, so that the matching results gradually meet the employment preferences and strategic goals of the enterprise. Through the feedback learning mechanism, the system "understands" the needs of the enterprise more and more in actual use, and the matching accuracy and decision fit also improve accordingly.

[0020] Through the cooperative operation of the above modules, the technical scheme provided by the application can realize deep semantic intention matching between candidates and posts. In a multi-language and multi-cultural recruitment situation, the system effectively improves the accuracy of matching and the efficiency of talent discovery, while ensuring the consistency of semantic understanding and the transparency and controllability of screening decisions. The architecture and data flow of the entire system are schematically shown in the accompanying Figure 2 The application will be further described below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 A schematic diagram of the hierarchical structure of the DIKWP cognitive model of the application;

[0022] Figure 2 A schematic diagram of the architecture of the multi-lingual semantic intention matching system of the application;

[0023] Figure 3 A flowchart of the system in the semantic matching between people and posts. DETAILED DESCRIPTION

[0024] The working process and application scenarios of the system of the application will be described in detail below in conjunction with the accompanying drawings and specific examples. It should be noted that the specific features and steps in the embodiments can be adjusted according to different application scenarios without departing from the principles of the application.

[0025] Example 1: Application of the enterprise multi-language recruitment platform

[0026] In a typical embodiment, the system of the application is deployed on a recruitment management platform of a multinational company, as shown in the accompanying Figure 2 The architecture contains various modules. Referring to the flowchart shown in the accompanying Figure 3 When a candidate submits a resume or participates in an online assessment and question answering through the platform, the system automatically completes multi-lingual semantic intention matching according to the following steps:

[0027] Step 1: Semantic analysis and preprocessing. The system first detects the language used in the materials provided by the candidate. For example, a candidate's resume contains mixed Chinese and English content, and the system can automatically identify which paragraphs in the text are in Chinese and which are in English. The multi-lingual semantic analysis module calls the corresponding processor for different languages: performs Chinese word segmentation, named entity recognition, and key point extraction on Chinese content, and directly analyzes English content by calling an English NLP model. All key information obtained through analysis is uniformly converted into a language-independent semantic vector representation as the basis for subsequent processing. In this example, the candidate's Chinese description extracts several skills and experience points through analysis, and the English description is converted into a representation in the same vector space as the Chinese part through a multi-language embedding model.

[0028] Step 2: DIKWP intent modeling. The parsed information is sequentially processed by each layer of the DIKWP intent extraction module. Starting from the data layer, the system extracts objective information from the candidate's resume: e.g., "graduated from a 985 university computer department" "has 5 years of back-end development experience" and so on. These information is further interpreted in the knowledge layer: the system understands from the built-in knowledge base that a 985 university graduation means a high educational background, 5 years of back-end development experience means senior development skills, so these implicit implications are upgraded to more abstract competence concepts (high education level, rich development experience). In the wisdom layer, the system infers the candidate's possible comprehensive abilities in combination with the context, for example, from his years of development experience and experience in leading team projects, it is inferred that he has "rapid learning ability" and "certain team cooperation experience". Finally in the intent layer, the system refines the candidate's career motivation and job-seeking intention: according to the content expressed in his resume, the candidate's deep intention of hoping to engage in technical innovation and being willing to lead a team is identified. After this step, the candidate's multi-language resume content is condensed by the system into a set of structured semantic intent collection.

[0029] Step 3: Post semantic matching calculation. The system selects a target job (e.g., "senior software engineer") from the current open positions of the enterprise and retrieves the semantic requirement data of the job. The matching engine queries the post requirement semantic library to obtain the pre-defined core competency intention set of the job, including: "master backend development (Java / .NET)", "have project management experience", "have team leadership ability", "fluent English communication", and other key requirements. Then, the matching engine compares and calculates the semantic intention set of the candidate with the job requirement intention set. For items with different languages but related semantics, the system can identify their equivalence relationship through the semantic library and determine that they match. For example, the job requirement clearly lists "fluent English communication", while the candidate mentions "once communicated abroad for a year" in the Chinese resume. Through the knowledge base, it is known that this represents the same ability intention - namely, cross-language communication ability, so the engine considers it as meeting the requirement. For another example, for the job requirement "familiar with Python or Java", if the candidate's resume mentions "master C++", the system can use the association relationship of programming languages in the skill ontology to infer that C++ belongs to the general programming language category as Python / Java, although they are not completely the same but have transferability in object-oriented programming ability, so it will also give a certain matching score. In the matching process, the system considers both the direct semantic similarity and the reasoning matching result: on the one hand, it calculates the similarity score of the candidate's intention vector and each job intention node vector, and on the other hand, it gives additional points according to the indirect matching relationship obtained through knowledge reasoning. For example, if the candidate does not mention a certain hard requirement but has similar ability through other conditions, it can give appropriate points. The matching engine combines these two parts of the score and calculates the overall fit degree score of the candidate and the job according to the pre-set weight strategy. For the candidate in this example, assume that the matching degree calculation result is high.

[0030] Step 4: Result output and explanation. After the matching score of the candidate and the target job is calculated, the system adds the matching result of the candidate to the candidate list and generates detailed matching explanation and presentation to the recruitment personnel. For example, the system may explain: "candidate A graduated from a 985 university, the education experience shows that his educational level meets the preference of the job for a prestigious school background; his 5 years of backend development experience corresponds to the senior development skills required by the job; in addition, the candidate has overseas exchange experience, meeting the job's demand for English communication ability", etc. These explanations clearly list the key points in the candidate's resume that correspond to the job requirements, allowing the recruitment manager to quickly understand the matching reasons. At the same time, the system presents the explanation in the local language (Chinese in this case) according to the semantic sovereignty principle, ensuring that the recruitment team can easily read and accept it. The matching result and explanation are provided to the recruitment personnel through the platform interface for deciding whether to push the candidate to the subsequent interview process.

[0031] Step 5: Feedback learning and model updating. In actual use, the recruitment personnel can provide feedback on the matching results and explanations given by the system. For example, if they find that some job requirements are underestimated or overestimated in importance, they can manually adjust the weights of the relevant matching rules; for the candidates recommended by the system, the recruiter can also mark which ones are ultimately proven to be good matches and which ones are not suitable. The system will collect this feedback data and use it for continuous training and optimization of the internal matching model. For example, if multiple feedbacks indicate that a certain skill is crucial for success in a certain type of job, the system will accordingly increase the weight of that skill match; conversely, if it is found that certain conditions lead to bias or misjudgment, the system will reduce their influence. Through this feedback mechanism, the system gradually adapts to the hiring preferences and actual requirements of the enterprise, continuously improving the accuracy of matching.

[0032] Through the above implementation, the system of the present application can efficiently process multilingual candidate information in the actual recruitment process of the multinational enterprise, and realize deep semantic matching of candidate ability and job requirements. In the trial run, after the company adopted the system, it realized the screening of different language resumes with unified standards, and the efficiency of recruitment screening was improved by more than 50% compared with before. At the same time, the system found some "hidden talents" that were missed in traditional screening due to language differences, significantly enhancing the effectiveness and fairness of global talent acquisition for the enterprise.

[0033] Example 2: Cross-language talent selection in the education field

[0034] The system of the present application is also applicable to the talent selection and admission process in the education field. For example, when selecting international students or scholarship candidates, an international university needs to evaluate application materials written in different languages from different countries. Under the traditional approach, the admissions committee may need to review the materials by experts in different languages, which is inefficient and inconsistent in scale. After introducing the system, this process can be greatly improved:

[0035] In this scenario, the multilingual semantic parsing module of the system can process the multilingual personal statements, recommendation letters, etc. submitted by the applicants, extracting elements such as academic background, research experience, and field of interest. The DIKWP intent extraction module further identifies deep intent information such as the applicant's academic aspirations, research interests, and overall quality. For example, the applicant expresses enthusiasm for "artificial intelligence ethics research" in the French motivation letter, and the system can extract their research interests and convert them into standard semantic representations. At the same time, the school side has previously defined the evaluation criteria for admission in the job requirement semantic library, such as the ability intentions emphasized by each scholarship program: academic performance, research potential, leadership, cross-cultural communication skills, etc. The matching engine matches the applicant's intent profile with the semantic requirements of the target program, giving a ranking of the degree of fit between each candidate and the program. For cases where the language is different but the essence is the same, such as an applicant being evaluated as "good at cooperation" in a Chinese recommendation letter, and "Teamwork Skills" being listed in the English project requirements, the system will recognize that this is an equivalent concept, ensuring that excellent applicants are not missed due to language reasons. Similarly, the explanation function provided by the system can explain to the admissions committee the strengths of each high-matching-degree applicant, such as: "Applicant B's undergraduate thesis award reflects their research ability; their volunteer experience demonstrates excellent leadership and organizational skills; the motivation mentioned in the French statement aligns with the project's purpose." These explanations help review committee members make decisions under a unified standard, regardless of their language background. In this embodiment, through AI-assisted multilingual intent matching, educational institutions can more fairly and efficiently select international students who meet the training objectives, achieving globalization and diversification in the recruitment process.

[0036] Embodiment 3: Government Agency Recruitment and Civil Servant Selection

[0037] The system of the present application can also be used in the multilingual recruitment or civil servant selection process of government agencies. In some government departments, positions may be open to talents from across the country or even the world, and candidates may use minority languages or foreign languages to write their application materials. At the same time, government recruitment usually has clear competency frameworks and policy orientations, such as requiring political awareness, language skills, and service consciousness. Traditional screening methods are difficult to meet consistent standards under multilingual backgrounds, while the present system can play an important role:

[0038] In government recruitment scenarios, the system first embeds or interfaces with the government's job competency model, converting the ability requirements listed in policy documents into standard nodes in the semantic library, such as "familiarity with the language and culture of a certain country", "public policy analysis ability", "integrity and dedication" and other concepts. For candidate information from different language backgrounds, the system extracts their education background, work experience, and information such as "volunteer service experience" that reflects their moral character and sense of responsibility through multilingual analysis and DIKWP extraction. The matching engine can apply semantic sovereignty strategies to meet the direction of government policies when calculating the matching degree, such as appropriately increasing the proportion of local language proficiency or relevant public service experience to ensure that the screening results meet the principles of government employment. In a specific example, a government agency is recruiting personnel for foreign-related positions, targeting both domestic bilingual talents and overseas job seekers. The system analyzes the Chinese and English resumes and qualifications submitted by the candidates, extracting their educational background, foreign language proficiency, and foreign-related work experience. During semantic matching, for "Passing the English Proficiency Test Level 8" mentioned in the resume of a domestic candidate, the system maps it to the "English proficiency" ability node; for "fluent in Mandarin" mentioned in the English resume of an overseas candidate, the system also matches it to the "Chinese proficiency" node. In this way, regardless of the candidate's mother tongue, the system can fairly assess their bilingual ability. For example, the job preference has overseas work experience, if a Chinese resume does not directly mention overseas experience but writes "many years of work experience in foreign companies", the system knowledge base can infer that this is close to the requirement of having an international work background, and the matching degree will also be increased accordingly. Finally, the system generates a candidate matching degree ranking and specific reasons, such as: "Candidate C is proficient in Uyghur language, meeting the language needs of the position in ethnic regions; has served in the grassroots for three years, demonstrating excellent service consciousness; his experience in community volunteer activities meets the requirements of the position for social responsibility", etc. This enables government recruitment to select qualified talents under uniform standards when facing candidates with multiple languages and diverse backgrounds, and ensures that the process is open, fair, and auditable.

[0039] Embodiment 4: Remote team recruitment and screening

[0040] With the rise of remote work and global virtual teams, enterprises need to select suitable members from talents with different national and cultural backgrounds when forming remote teams. Remote team recruitment pays special attention to the soft requirements of candidates' self-discipline, cross-cultural communication ability, and experience in adapting to different time zone collaboration. The system of the present invention can be used to improve the efficiency and accuracy of remote recruitment:

[0041] In this embodiment, a software company plans to recruit a global remote development team, including development engineers, product managers, etc. The company receives job applications from all over the world, including resumes and cover letters in multiple languages. The system performs multilingual semantic analysis on these materials, extracting key information such as the candidate's professional skills (programming languages, project experience), work mode preferences (remote work experience, self-management ability), etc. Then through the DIKWP model, the candidate's intent is analyzed in depth, such as whether they show strong willingness to work remotely, whether they have cross-cultural collaboration awareness, etc. At the same time, the enterprise defines special intent nodes required for remote teams in the human-occupation semantic library, such as "time zone flexibility", "online communication skills", "self-driven ability", etc. For an English resume that mentions "3 years of remote work experience, proficient in Slack / Zoom collaboration", the system maps it to the "remote collaboration experience" and "online communication skills" intent; for another Spanish resume that says "lideró un equipo de desarrollo distribuido internacionalmente" (led an internationally distributed development team), the system also identifies the "remote team leadership" ability intent. The matching engine considers both hard skill matching (such as programming ability and project experience) and soft skill matching (such as self-discipline, communication ability, etc.) to score. Since remote teams rely heavily on autonomy and communication, the company in this embodiment can use semantic sovereignty strategies to increase the weight of "self-driven" and "communication timeliness" intent in the matching score. The system's output not only indicates which candidates' skills fit, but also explains, for example: "Candidate D has two years of remote work experience, demonstrating strong self-management ability; he has participated in multiple cross-country projects, showing cross-cultural communication skills; technical stack is highly consistent with project requirements." Through this information, the recruitment team can quickly form an international team that is skill-complementary and accustomed to remote collaboration. The implementation results show that compared to traditional manual screening, this system can more accurately assess the remote work competence of candidates and ensure that selected team members can efficiently integrate into a virtual collaboration environment.

[0042] Embodiment 5: Application of Cross-border Freelancer Platform

[0043] Freelance online platforms (such as crowdsourcing service markets) need to match project demands with freelancers globally. Project postings and talent self-recommendations on such platforms often involve multiple languages, and clients not only pay attention to skill matching, but also to communication ability, reliability, etc. The system of the invention can be used to improve the intelligent level of cross-border freelancer matching:

[0044] Suppose a project requirement is posted on a freelancing platform: in English, a freelancer who is proficient in mobile application development and French is needed. Developers from all over the world submit proposals, some in English, some in French or other languages. The system performs multilingual analysis on the proposals and personal profiles, extracting information such as skill tags (e.g. programming languages, development platform experience), past project evaluations, language abilities, etc. The DIKWP module further infers the potential intent matching degree of each freelancer for the project, such as whether they have strong interest in this type of project or have deep experience in the relevant field. The platform's semantic library predefines the core intent nodes required by the project, such as "mobile application development skills", "French communication ability", "on-time delivery", etc. The matching engine can uniformly identify different language descriptions: a French freelancer writes in French profile "5 ans d'expérience endéveloppement d'applications mobiles" (5 years of mobile application development experience), which is identified by the system as highly matching "mobile application development skills"; another Indian freelancer describes himself in English "fluent in French and developed multiple iOS apps", the system extracts two highlights "fluent in French" and "multiple iOS application development experience", both of which match the project requirements. Then, the matching engine combines the platform's reputation evaluation (such as on-time rate, high evaluation project number, etc. These data can be considered as wisdom layer information) to comprehensively evaluate the fit degree of each candidate, and ranks the recommendations to the client. The system's explanation module will present the matching reasons for each candidate to the client, such as: "Freelancer E is good at iOS and Android development, has communicated with clients in French for projects, and meets technical ability and language requirements; its past project on-time delivery rate is 100%, and is highly reliable", helping the client make a wise choice. Through this system, cross-border freelancing platforms can achieve automated and intelligent multilingual matching, improving matching success rate and user satisfaction. In testing, it was found that after using the system, the platform's average response time for project-talent matching was reduced by 30%, and employers' satisfaction with the matching results was significantly improved, indicating that the invention has broad application potential in the freelancing market.

[0045] The above embodiments illustrate the application modes of the system in different fields and scenarios. It should be understood that these embodiments are intended to illustrate the feasible applications of the present application, but not to limit the protection scope of the present application. Various modifications and adjustments can be made to the specific implementation by those skilled in the art without departing from the idea of the present application, for example, customizing the knowledge base content for specific industries, fusing image or video analysis to enrich the candidate evaluation, etc., which should be considered to fall within the protection scope of the present application.

Claims

1. A multilingual semantic intent matching system for AI-assisted recruitment, comprising: The parsing module converts candidate texts in different languages ​​and formats into a unified semantic representation; The intent extraction module extracts multi-level features and semantic intent from candidate texts based on the Data-Information-Knowledge-Intelligence-Intent (DIKWP) model. The job requirement semantic library pre-stores standardized job competency semantic nodes and their relationships; The semantic matching engine is used to calculate the matching degree between the candidate's semantic intent and the semantic nodes of the job position, and to generate matching results; The explanation and feedback module generates explainable matching reasons based on semantic associations during the matching process and receives human feedback to adjust the matching model parameters.

2. The system according to claim 1, characterized in that: The semantic matching engine, combined with a knowledge reasoning mechanism, can infer the equivalence or transferability of a candidate's skills by using pre-defined skill ontology relationships when the skills explicitly mentioned by the candidate do not directly correspond to the job requirements. This helps identify hidden matching opportunities and improves the accuracy of cross-language and cross-domain semantic matching.

3. The system according to claim 1 or 2, characterized in that: The explanation and feedback module provides a white-box explanation of the matching decision, including key matching items, semantically equivalent reasoning links, and matching confidence, and allows users to adjust matching parameters according to organizational strategy needs to meet policy-oriented talent selection standards.

4. The system according to claim 1, characterized in that: The multilingual semantic parsing module supports multimodal input, including information provided by candidates such as text, voice, and video. For voice or video input, the system converts it into parsable text through speech recognition and subtitle extraction, and then performs language detection, word segmentation, and embedding representation processing to unify different forms of candidate expressions into the semantic representation layer.

5. The system according to claim 1, characterized in that: The multilingual semantic parsing module uses a pre-trained multilingual semantic embedding model to map texts in different languages ​​to a unified vector space representation, maintaining consistent semantic similarity. Through this cross-lingual embedding representation, equivalent expressions in different languages ​​are close in distance in the vector space, thereby achieving cross-lingual semantic fusion.

6. The system according to claim 1, characterized in that: The DIKWP model of the intent extraction module includes a data layer, an information layer, a knowledge layer, an intelligence layer, and an intent layer connected in sequence, used to extract semantic information from the candidate's text step by step; wherein the data layer extracts objective data from the candidate's resume, the information layer obtains basic experience and contextual information, the knowledge layer combines domain knowledge to infer implicit abilities, the intelligence layer infers high-level comprehensive abilities, and the intent layer identifies the candidate's career motivation and preference intentions, and outputs the candidate's semantic intent representation.

7. The system according to claim 1, characterized in that: The job requirement semantic database uses a knowledge graph structure to store job requirement semantic information, including competency nodes such as skills, experience, and qualities, and their semantic relationships. The semantic database integrates industry-wide ontologies and job-specific requirements, mapping requirements in different languages ​​or with different expressions to the same semantic node to support cross-language and cross-domain equivalence matching.

8. The system according to claim 1, characterized in that: The semantic matching engine includes a semantic sovereignty policy adjustment unit, which is used to adjust the rule weights in the matching calculation according to local policies or enterprise preferences. Through semantic sovereignty mechanisms, users can set the weight of certain skills or traits in the matching score, thereby aligning the matching results with the organization's talent selection guidelines.

9. The system according to claim 1, characterized in that: The explanation feedback module receives feedback from recruiters on the matching results and updates the semantic matching model accordingly. By incorporating human feedback into model training, the system can adaptively optimize the matching parameters, thereby continuously improving the accuracy and fit of subsequent matching results.

10. A multilingual semantic matching method for AI-assisted recruitment, characterized in that... Includes the following steps: a) Perform semantic parsing preprocessing on the multilingual texts submitted by candidates, converting texts in different languages ​​into a unified semantic vector representation; b) Based on the DIKWP model, perform multi-level intent extraction on the parsed text information to obtain a set of candidate semantic intents; c) Extract standardized semantic requirements for the target position, obtain the set of competency intentions required for the position from the semantic database, and calculate the matching degree between the candidate's intentions and the position requirements, including direct semantic similarity calculation and knowledge reasoning matching. d) Generate matching results between candidates and positions based on the matching degree, along with explanations; e) Receive user feedback on matching results and adjust the matching model accordingly to optimize subsequent matching processes.

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