Mapping Mercer, Radford, WTW, and ONET Job Taxonomies

Introduction

Radford, Mercer, WTW, and O*NET-SOC job architectures are widely used in the HR industry to classify job roles and benchmark compensation. Each of these job architectures has its own unique taxonomy and leveling system to categorize job roles based on factors such as job function, seniority, education level, and industry. However, the lack of standardization across these taxonomies poses challenges for teams trying to maintain consistency across surveys and get the most out of the surveys they subscribe to. Moreover, these job architectures change every year, making it difficult to maintain mappings between different versions of the taxonomies.

ONET to Mercer Mapping

To access the O*NET to Mercer mapping, you may download the spreadsheet (.xlsx) here. This mapping provides a crosswalk between the 2019 O*NET-SOC taxonomy (opens in a new tab) (which includes 1,016 job titles) and the Mercer job taxonomy (opens in a new tab).

Custom Mapping

If you require custom mapping between Radford, Mercer, WTW, and ONET job taxonomies, please contact us for more information. Our team can help you build and dynamically maintain crosswalks between these taxonomies to ensure data consistency and accuracy across your HR systems.

Our team is happy to assist you in creating mappings between your job architecture and your survey providers. Simply upload a file with your job architecture and the survey provider's taxonomy, and we will generate a mapping for you.

Technical Details

Taylor's automatic taxonomy mapping streamlines the process of aligning taxonomies. Our approach combines the power of deep learning, natural language processing, and semantic analysis to automatically align categories, terms, and concepts across different taxonomies with high accuracy and efficiency. By automating the mapping process, Taylor's solution significantly reduces the time and effort required to build crosswalks, enabling businesses to achieve seamless data integration and interoperability across systems.

Taylor's methodology for mapping taxonomies:

Preprocessing

• Text Normalization: Taylor normalizes the terms within each taxonomy. This includes lowercasing, removing punctuation, stopword removal, and lemmatization. These help avoid discrepancies.
• Tokenization: Tokenize the terms into individual words or meaningful phrases. This step is crucial for subsequent matching steps.

Semantic Analysis

• Synonym Expansion: Taylor uses domain-specific thesauri to expand each term into its synonyms. This helps in identifying equivalent terms that may not be lexically identical.
• Embedding Representations: Taylor generates vector representations of the terms.
• Contextual Similarity: If taxonomies include terms that are context-dependent, we use contextual embeddings to capture the meaning of terms in different contexts. This is useful for polysemous words.

Initial Matching

• String Matching: Taylor performs basic string matching on the tokenized and normalized terms.
• Embedding Similarity Calculation: Taylor calculates and filters the cosine similarity between the vector embeddings of terms from the two taxonomies. Taylor tunes the threshold relatively to filter out weak matches.

Machine Learning and Rule-Based Refinement

• Unsupervised Learning: Taylor clusters similar terms and potential mappings.
• Rule-Based Enhancements: Domain-specific rules are applied to capture the nuances of the taxonomies. For instance, in the medical domain, certain prefixes or suffixes may have specific meanings that are important for accurate mapping.

Validation and Feedback Loop

• Confidence Scoring: Taylor assigns confidence scores to each mapping based on the strength of the match. This helps in prioritizing mappings for manual review or correction.
• User Feedback: Taylor provides a UI for business users to incorporate feedback from users or domain experts to validate and refine the mappings. This feedback loop ensures that the mappings are accurate and relevant to the specific use case.

Integration and Deployment

• To work with your mappings, Taylor's API exposes the relationships between taxonomies. The API allows for querying mappings, adding new mappings, retrieving mapping confidence scores, and triggering new mappings on new taxonomies.

Continuous Monitoring and Updating

• Monitoring: Taylor's monitoring systems track the performance of the mapping system in production.
• Dynamic Adaptation: As taxonomies evolve or new versions of taxonomies are released, Taylor's systems are robust and accommodate new terms or structures. For example, when a new version of a taxonomy is released, Taylor can automatically adapt the mappings to the new version.

For Business & Product Managers

Business and product teams can build automatic taxonomy mappings by logging in to the Taylor platform here (opens in a new tab). Taylor's user-friendly interface allows users to upload taxonomies, view suggested mappings, and edit mappings.

For Developers

Developers can integrate Taylor's automatic taxonomy mapping solution into their applications using the Taylor API. The API provides endpoints for uploading taxonomies, retrieving mappings, and validating mappings. Developers can also leverage the API to trigger new mappings, monitor mapping performance, and receive real-time updates on mapping changes.

Note: The Taylor Mapping API is currently available to select partners. If you are interested in integrating the automatic taxonomy mapping solution into your application, please contact us for more information.

Integrations & Deployment

Sign in here to start mapping your taxonomies with Taylor's automatic taxonomy mapping solution.