Data Extraction on Credentials

This document provides comprehensive information about the data extraction object used for analyzing credentials, including sections on the NICE Framework, O-NET Classifications, and Sentiment Analysis. These three documented extraction types are part of a broader set of 32 extraction types employed by the Human Cloud Competence engine to evaluate and understand various competencies and skills.

Table of Contents

1. NICE Framework
2. O-NET Classifications
3. Sentiment Analysis

1. NICE Framework

Introduction

The NICE (National Initiative for Cybersecurity Education) Cybersecurity Workforce Framework is a reference structure that categorizes and describes cybersecurity work and the knowledge, skills, and tasks required to perform roles in various categories. It helps organizations identify necessary competencies for cybersecurity roles. This framework is one of the 32 extraction types used by the Human Cloud Competence engine to extract and analyze skills from credentials.

Object Structure

The NICE framework data is structured into categories, each containing subcategories with specific skills, tasks, or knowledge areas. Each subcategory is rated on a Likert scale.

Example

{
  "name": "NICE (National Initiative for Cybersecurity Education) Cybersecurity Workforce Framework",
  "id": "nice",
  "data": [
    {
      "name": "Analyze (AN)",
      "id": "nice.analyze",
      "overallRating": 4.5,
      "data": [
        {
          "name": "Research and Analysis Skills",
          "id": "nice.analyze.research_and_analysis_skills",
          "type": "Skill",
          "value": 5,
          "scale": "likert_5"
        }
      ]
    }
  ]
}

Schema data type definitions

2. O-NET Classifications

Introduction

O-NET (Occupational Information Network) is a comprehensive database of worker attributes and job characteristics. It provides detailed information about skills, knowledge, and tasks required for various occupations, helping individuals and organizations understand job requirements and career development. This classification is one of the 32 extraction types used by the Human Cloud Competence engine to extract and analyze skills from credentials.

Object Structure

The O-NET classification data is organized into categories and subcategories, each containing specific skills. Each skill is rated on a Likert scale.

Example

{
  "name": "O-NET Classifications",
  "id": "onet",
  "data": [
    {
      "name": "Research and Analysis",
      "id": "onet.research_and_analysis",
      "data": [
        {
          "name": "Analyzing Data or Information",
          "id": "onet.research_and_analysis.analyzing_data_or_information",
          "type": "Skill",
          "value": 5,
          "scale": "likert_5"
        }
      ]
    }
  ]
}

Schema data type definitions

3. Sentiment Analysis

Introduction

Sentiment analysis involves evaluating the emotional tone of a text, determining whether it is positive, negative, or neutral. This analysis helps understand the overall sentiment conveyed in different sections of a document. Sentiment analysis is one of the 32 extraction types used by the Human Cloud Competence engine to extract and analyze sentiments from credentials.

Object Structure

The sentiment analysis data includes an overall sentiment rating and detailed sentiment values for specific sections of the document. Each section's sentiment is expressed as a numerical value and categorized.

Example

{
  "name": "Sentiment Analysis",
  "id": "sentiment_analysis",
  "overallRating": {
    "name": "Overall Sentiment",
    "id": "sentiment_analysis.overall_sentiment",
    "value": 75,
    "valueExtraction": "Positive",
    "scale": "0-100"
  },
  "data": [
    {
      "name": "Abstract",
      "id": "sentiment_analysis.abstract",
      "value": 90,
      "valueExtraction": "Very Positive",
      "scale": "0-100",
      "keywords": ["widely used", "several key challenges", "propose", "superiority", "improvement"]
    },
    {
      "name": "Introduction",
      "id": "sentiment_analysis.introduction",
      "value": 70,
      "valueExtraction": "Positive",
      "scale": "0-100",
      "keywords": ["challenges", "importance", "proposed solutions", "impact"]
    },
    {
      "name": "Methodology",
      "id": "sentiment_analysis.methodology",
      "value": 55,
      "valueExtraction": "Neutral",
      "scale": "0-100",
      "keywords": ["technical", "detailed", "implementation", "advantages"]
    },
    {
      "name": "Results",
      "id": "sentiment_analysis.results",
      "value": 85,
      "valueExtraction": "Very Positive",
      "scale": "0-100",
      "keywords": ["superior performance", "significant margin", "best performer overall"]
    },
    {
      "name": "Conclusion",
      "id": "sentiment_analysis.conclusion",
      "value": 80,
      "valueExtraction": "Very Positive",
      "scale": "0-100",
      "keywords": ["novel", "effective", "outperforming"]
    }
  ]
}

Schema data type definitions

Note

Data extraction on credentials is run multiple times and can vary in results based on other connected data to the user's other credentials. A data extraction cannot be used directly from one credential but must be extracted in the context of a Verified Presentation along with a context that takes the complete competency graph into account. The Verified Presentation will weigh in trust for each data extraction to make it more accurate and limit gamification of results.