Knowledge Modeler / Ontologist Manager

We Are:

Accenture is a global professional services and solutions company that helps leading organizations reinvent with digital, cloud, data, and AI capabilities, drawing on its large global workforce, industry expertise, and ecosystem partnerships to create 360° value.

By combining hands-on engineering with deep industry context, we help organizations build their digital core, modernize operations, unlock value from data and AI, and deliver tangible business outcomes at speed and scale.

You Are:

We are seeking an experienced Knowledge Modeler / Ontologist to contribute to domain ontology design, governance, and delivery for enterprise knowledge graph initiatives. Working collaboratively within a team and under the guidance of an ontology governance framework, this role bridges business stakeholders, data engineering, and AI/ML teams - translating complex business domains into rigorous, standards-compliant semantic models that power intelligent applications.

Key Responsibilities:

Ontology Design & Development:

• Lead the full ontology lifecycle supporting a defined project scope: requirements gathering, conceptual modeling, formal specification (OWL 2, RDFS, SKOS), validation, and deployment

• Design T-Box (schema) structures and contribute to A-Box (instance) hydration patterns across structured and unstructured data sources

• Apply and uphold established naming conventions, URI strategies, and modular ontology architecture principles

• Apply advanced modeling patterns where appropriate (e.g., reification, n-ary relations, punning) in consultation with senior team members

• Create and evolve ontologies, taxonomies, and vocabularies using standards including OWL, RDF, RDFS, and SKOS

• Map traditional data structures (e.g., relational databases) into semantic frameworks

• Build and maintain knowledge graphs to support reasoning, discovery, and advanced analytics

• Ensure models align with business goals, regulatory requirements, and data governance practices

• Coach the business (SMEs and stakeholders) in developing and validating competency questions to guide and test ontology scope and correctness

• Maintain awareness of relevant upper ontologies (e.g., BFO, schema.org) and apply alignment patterns as appropriate

Stakeholder Engagement:

• Facilitate business SME workshops with BA support to elicit domain concepts, relationships, and competency questions

• Translate ambiguous business language into precise, machine-readable semantics

• Communicate modeling decisions to non-technical stakeholders in accessible terms

• Support feedback loops between business validation and ontology iteration

Technical Contribution:

• Apply ontology governance standards: version control, change management, and quality gates (W3C compliance, SHACL/ShEx validation)

• Define and enforce Definition of Done criteria for ontology deliverables in collaboration with the senior team

• Apply R2RML / RML mapping strategies for structured data integration

• Collaborate with Entity Extraction / NLP teams on ontology-guided information extraction from unstructured sources

• Collaborate with knowledge graph hydration, testing, and validation against competency questions

Team Collaboration & Delivery:

• Work effectively within cross-functional delivery teams spanning Data Engineering, AI/ML, Product, and Business SMEs

• Collaborate with and support junior team members, sharing knowledge and modeling approaches

• Meet delivery timelines and proactively flag dependencies or risks to senior ontologist or project lead

• Provide accurate and achievable estimates for ontology-related work and contribute to roadmap planning as part of the broader project scope

Key Skills and Competencies:

• Ontology Engineering: Proficiency in OWL 2, RDF, RDFS, SKOS, SHACL, and SPARQL for building and validating semantic models

• Semantic Web Standards: Working knowledge of W3C standards and best practices for linked data and knowledge representation

• Knowledge Graph Development: Experience designing and querying knowledge graphs using tools such as GraphDB, Stardog, or Neo4j

• Tooling: Hands-on experience with ontology authoring tools such as Protégé or Ontotext

• Data Integration: Familiarity with R2RML or RML for mapping relational or semi-structured sources into semantic models

• Competency Question Development: Ability to formulate and use competency questions to scope, validate, and iterate on ontological models

• Upper Ontology Awareness: Familiarity with upper ontologies such as BFO, DOLCE, or schema.org and their role in interoperability

• Communication & Collaboration: Ability to translate complex technical concepts into business-friendly language and engage effectively with both technical and non-technical stakeholders

This role is hybrid in nature and will require time in office and traveling to client locations. Travel can be between 20-80%