Abstract: Embodiments are directed to managing courses. Course information for a course may be provided. Skill terms may be determined for the course based on the course information and skill models. Candidate skills may be determined based on the skill terms and a unified skill dictionary such that the skill terms may be mapped to the candidate skills based on the unified skill dictionary. A summary of the course information and the candidate skills may be displayed to a subject matter expert. if the subject matter expert confirms that a candidate skill may be taught by the course, the candidate skill may be associated with a course profile for the course that includes the confirmed candidate skills and proficiency scores for the confirmed skills.

Abstract: Embodiments are directed to managing skill proficiencies. Declared skills may be determined based on a job description and natural language processing (NLP) actions declared in one or more extraction models. An inference prompt for a large language model (LLM) may be generated based on the job description such that the job description and the declared skills may be included in the inference prompt. The LLM may be trained with the inference prompt to generate a response such that the inference prompt may be iteratively updated based on validations of the response. The LLM may be retrained with the updated inference prompt to generate an updated response that includes the inferred skills that may be separate from the declared skills. A job profile that corresponds to the job description may be updated to include the declared skills and the separate inferred skills.

Abstract: Embodiments are directed to managing data for a predictive learner recommendation platform. A platform that includes applications hosted in an application layer may be provided. The applications may be employed to provide a request to determine a pathway prediction for a learner such that the pathway prediction may be associated with a role offered by employers. Prediction engines associated with the request may be determined based on the service layer interface and the request such that the request may be provided to the determined prediction engines via the service layer interface. The prediction engines may be employed to generate the pathway prediction based on a learner profile that corresponds with the learner, a role success profile that corresponds to the employers, and models that are trained to predict matches between the learner profile and the role success profile.

Abstract: Embodiments are directed to managing data correlation over a network. Role success models that correspond to roles and to success criteria may be provided. A student profile that includes skill vectors may be provided based on student information. Role success models may be employed to determine intermediate scores based on the skill vectors and the success criteria. A predictive score for the student that corresponds with a predicted performance of the student in the roles may be generated based on the one or more intermediate scores. Actions for the student may be determined based on a mismatch of the skill vectors and role skill vectors that correspond to the roles. In response to the student performing the actions: updating the one or more skill vectors based on a completion of the actions; and updating the predictive score based on the role success models and the updated skill vectors.

Abstract: Embodiments are directed to managing data for unified graph representation of skills and acumen. Information associated with one or more subjects may be classified to provide profile information that conforms to a unified schema. Fields of the profile information may be classified as facts, fact-relationships, actions, skills, or skill-relationships based on the unified schema. A plurality of profile graphs may be generated based on map models and the facts, the fact-relationships, the actions, the skills, or the skill-relationships such that the map models include one or more directives for associating the facts, the fact-relationships, the actions, the skills, or the skill-relationships with one or more nodes or one or more edges in the plurality of profile graphs. In response to query information provided by one or more analysis applications, classifying a portion of the plurality of profile graphs based on the query information.

Abstract: Embodiments are directed to data ingestion over a network. Raw data and integrated data associated with a plurality of separate data sources may be provided such that the raw data includes content associated with a plurality of subjects. Categorization models may be employed to categorize the raw data based on various features, such as, format, structure, data source, variability, volume, or associated entities. Matching models may be determined based on the categorization of the of the raw data, the integrated data and the content associated with the plurality of subjects. Matching models may generate a plurality of unified facts based on the raw data and the integrated data such that each unified fact is associated with a score associated with a quality of its match with a unified schema.

Abstract: Embodiments are directed to managing data correlation over a network. Student information may be provided. Position information based on potential employers may be provided. Student profiles may be generated based on translation models and the student information. The student information may be translated into unified facts included in the student profiles. Position profiles may be generated based on the translation models and the position information. The position information may be translated into other unified facts in the position profiles. The student profiles may be correlated with the position profiles based on recommendation models, the unified facts, and the other unified facts. Each student profile and position profile pair may be associated with a score based on a strength of the correlation. Reports may be provided that include each pair of the student profile. A plurality of pairs may be ordered based on the score associated with each pair.

Abstract: Embodiments are directed to managing data correlation over a network. Student information may be provided. Position information based on potential employers may be provided. Student profiles may be generated based on translation models and the student information. The student information may be translated into unified facts included in the student profiles. Position profiles may be generated based on the translation models and the position information. The position information may be translated into other unified facts in the position profiles. The student profiles may be correlated with the position profiles based on recommendation models, the unified facts, and the other unified facts. Each student profile and position profile pair may be associated with a score based on a strength of the correlation. Reports may be provided that include each pair of the student profile. A plurality of pairs may be ordered based on the score associated with each pair.

Abstract: Embodiments are directed to managing data correlation over a network. Role success models that correspond to roles and to success criteria may be provided. A student profile that includes skill vectors may be provided based on student information. Role success models may be employed to determine intermediate scores based on the skill vectors and the success criteria. A predictive score for the student that corresponds with a predicted performance of the student in the roles may be generated based on the one or more intermediate scores. Actions for the student may be determined based on a mismatch of the skill vectors and role skill vectors that correspond to the roles. In response to the student performing the actions: updating the one or more skill vectors based on a completion of the actions; and updating the predictive score based on the role success models and the updated skill vectors.

Abstract: Embodiments are directed to data ingestion over a network. Raw data and integrated data associated with a plurality of separate data sources may be provided such that the raw data includes content associated with a plurality of subjects. Categorization models may be employed to categorize the raw data based on various features, such as, format, structure, data source, variability, volume, or associated entities. Matching models may be determined based on the categorization of the of the raw data, the integrated data and the content associated with the plurality of subjects. Matching models may generate a plurality of unified facts based on the raw data and the integrated data such that each unified fact is associated with a score associated with a quality of its match with a unified schema.