SEOntology: A Domain Ontology for Semantic Modeling of Search Engine Optimization Workflows

Abstract

Search engine optimization (SEO) workflows remain difficult to automate and integrate because they span heterogeneous tools while relying on loosely structured data models that provide limited machine-interpretable representations of content, actions, and performance signals. This fragmentation hinders interoperability and makes provenance tracking across SEO systems brittle. We present SEOntology, a Schema.org-aligned OWL 2 ontology that models SEO workflows as knowledge graph structures to support structured querying and provenance-aware analysis. The ontology enables queries for content-quality inspection, entity-coverage analysis, and end-to-end tracing of analytical actions through their inputs, outputs, and generated signals. We assess SEOntology via structural ontology eval-uation and competency-question validation, and report supporting evidence from production deployment. These results indicate logical consistency, coverage of the defined requirements, and practical utility as a semantic integration layer for representing and querying SEO workflows. SEOntology is designed as a reusable semantic resource for knowledge-graph–driven and agent-assisted SEO systems, and is extensible toward emerging generative and AI-mediated search environments.