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$55.5K

$118.7K

$173.5K

How much do knowledge graph semantic jobs pay per year?

As of Sep 11, 2026, the average yearly pay for knowledge graph semantic in the United States is $118,674.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,500.00 and $133,500.00 per year, depending on experience, location, and employer.

What are knowledge graph semantics?

Knowledge Graph Semantics refer to the meaning and relationships among the concepts and entities represented within a knowledge graph. They define how data is connected, interpreted, and used to infer new knowledge, often by using ontologies and standardized vocabularies. Semantic technologies enable machines to understand the context and intent behind data, making it possible to perform advanced reasoning and query answering. This is crucial for applications like search engines, recommendation systems, and AI assistants that rely on structured and meaningful information.

What are the key skills and qualifications needed to thrive as a knowledge graph semantic engineer?

To thrive as a Knowledge Graph Semantic Engineer, you need expertise in ontology modeling, semantic web standards (like RDF, OWL, and SPARQL), and a background in computer science or information science. Familiarity with tools such as Protégé, graph databases (e.g., Neo4j, Stardog), and experience with data integration platforms or semantic reasoning engines is typically required. Strong analytical thinking, problem-solving, and the ability to communicate complex technical concepts to non-technical stakeholders are valuable soft skills in this role. These capabilities are essential for building, optimizing, and maintaining knowledge graphs that enable intelligent data connections and drive advanced analytics in organizations.

What are some common challenges faced when working as a knowledge graph semantic engineer, and how can they be addressed?

One of the main challenges for Knowledge Graph Semantic Engineers is integrating data from diverse sources while maintaining consistency and semantic accuracy. This often involves resolving conflicting information, mapping different data schemas, and ensuring that the graph reflects real-world relationships accurately. Collaboration with domain experts and data owners is crucial, as is staying updated on ontology standards and best practices. Regular validation, automated testing, and clear documentation can help address these challenges and ensure the integrity of the knowledge graph.

What is the difference between Knowledge Graph Semantic vs Data Scientist?

AspectKnowledge Graph SemanticData Scientist
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of ontologies and semantic technologiesDegree in Statistics, Computer Science, or related; proficiency in programming and statistical analysis
Work EnvironmentResearch and development teams, data integration projects, semantic web applicationsData analysis, modeling, and visualization in various industries like finance, tech, healthcare
Employer & Industry UsageTech companies, AI firms, knowledge management organizationsTech companies, consulting firms, research institutions

Knowledge Graph Semantic specialists focus on structuring data using ontologies and semantic technologies to enable intelligent data retrieval. Data Scientists analyze and interpret data to inform business decisions. While both roles work with data, Knowledge Graph Semantic roles emphasize data organization and semantics, whereas Data Scientists focus on analysis and modeling.

What other helpful pages are available for Knowledge Graph Semantic?

Other pages related to Knowledge Graph Semantic:

Infographic showing various Knowledge Graph Semantic job openings in the United States as of September 2026, with employment types broken down into 100% Full Time. Highlights an 75% In-person, and 25% Hybrid job distribution, with an average salary of $118,674 per year, or $57.1 per hour.

Ontology / Knowledge Graph Engineer

Iselin, NJ • On-site

2T Consulting
IT Services • 51 - 200 employees
Learn more about 2T Consultingexternal

Full-time

Posted 6 days ago

Job description

We are seeking an experienced Ontology / Knowledge Graph Engineer with strong expertise in ontology engineering, semantic modeling, and knowledge graph development. The ideal candidate will have hands-on experience designing and implementing ontology-driven knowledge graphs using standards such as OWL, RDF, SPARQL, SHACL, and JSON-LD.

Required Skills
  • Strong experience in Ontology Engineering and ontology-driven knowledge graph design.
  • Expertise in Knowledge Modeling and Semantic Modeling.
  • Hands-on experience with OWL, RDF, SPARQL, SHACL, and JSON-LD.
  • Experience with Ontological Inference and consistency checking.
  • Strong knowledge of Knowledge Graphs, RDF Graphs, and Property Graphs.
  • Experience with Graph Data Modeling and Graph Analytics.
  • Hands-on experience with Entity Resolution.
  • Ability to design and implement scalable semantic and knowledge graph solutions.
Technologies / Tools
  • Protégé
  • TopBraid Composer
  • OntoStudio
  • Neo4j
  • Stardog
  • GraphDB
  • Apache Jena
  • Fuseki
  • Blazegraph
  • Virtuoso
Key Responsibilities
  • Design, develop, and maintain enterprise ontologies and semantic models.
  • Build ontology-driven Knowledge Graph (KG) solutions aligned with business and technical requirements.
  • Develop and manage RDF-based knowledge graphs using OWL, RDF, SPARQL, SHACL, and JSON-LD.
  • Implement ontological inference and reasoning capabilities.
  • Perform consistency checking and validation of ontologies and knowledge graph data.
  • Develop graph data models and support both RDF and property graph architectures.
  • Implement entity resolution and semantic relationships across disparate data sources.
  • Perform graph analytics to derive insights from connected data.
  • Use ontology and knowledge graph tools such as Protégé, TopBraid Composer, Stardog, GraphDB, Neo4j, and Apache Jena.
  • Collaborate with data engineers, architects, and business stakeholders to define semantic requirements and modeling standards.
  • Establish best practices for ontology governance, versioning, validation, and knowledge graph quality.

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