On what basis Domain is extracted by JD Parser?

RChilli’s Job Description (JD) Parser determines the domain based on a combination of AI-driven contextual analysis, taxonomy-based enrichment, and sectional parsing of job descriptions. Here's how it works in detail:

1. Domain Extraction Through Taxonomy and Ontology

RChilli uses its proprietary Taxonomy 3.0, which includes:

• 3 million+ skills

• 2.4 million+ job profiles

• Categorized into industries, domains, and job roles across multiple languages.

When a job description is parsed, RChilli maps the extracted job titles, skills, and responsibilities against its taxonomy. This allows the parser to classify the job into a relevant domain like IT, Healthcare, Finance, etc. The process involves:

• Identifying key skills and job profiles

• Mapping them to pre-defined domain clusters

• Assigning the most appropriate domain based on confidence score and frequency match

This taxonomy mapping ensures standardized and structured domain data, making it easier to match jobs with the right candidates.

2. AI & Deep Learning-Based Pattern Recognition

RChilli’s JD Parser uses AI/ML models to analyze the semantic meaning of job titles and descriptions. These models:

• Recognize natural language patterns in job responsibilities and required skills

• Detect contextual clues to infer domain even when keywords are not explicitly mentioned

• Ensure accurate classification even with diverse or unconventional job descriptions

This helps in extracting domains with contextual awareness, rather than relying solely on keyword matching.

3. Section-Based Analysis of JDs

The parser examines specific sections of a job description such as:

• Job Title

• Responsibilities

• Required Skills

• Preferred Qualifications

Each section contributes differently to domain inference. For example:

• A job title like "DevOps Engineer" paired with tools like Jenkins and AWS strongly maps to the IT/Software domain.

• A title like “Financial Analyst” with skills like Excel and forecasting maps to the Finance domain.

4. Optional Enhancements via Taxonomy API

While the JD Parser can auto-extract the domain, users looking for enhanced standardization (like synonyms or related terms) can make a separate call to the Taxonomy API to retrieve:

• Comparable terms

• Alternate job titles

• Related skills within the same domain.

Summary

The domain extraction in RChilli’s JD Parser is based on:

• Structured classification via Taxonomy 3.0

• Contextual understanding through AI models

• Parsing of specific JD sections

• Optional enrichment using Taxonomy APIs

This combination ensures highly accurate, industry-aligned domain classification of job descriptions.

If you'd like to see a sample JSON response or schema layout including the domain field, I can help with that too.

For any custom implementation or support, feel free to contact support@rchilli.com.