As organizations implement AI systems, many find that their investments fail to deliver reliable, contextual results—often because the underlying data lacks the semantic structure these systems require. The Ontology Pipeline is a systematic framework, derived from library science methodologies, that guides organizations through the iterative construction of semantic knowledge systems: from controlled vocabularies that clean and disambiguate data, through metadata standards that normalize entity-value pairs, to taxonomies that establish hierarchical structure, thesauri that encode associative relationships, ontologies that introduce logical reasoning, and finally knowledge graphs that synthesize these layers into queryable, visual representations. 

Jessica will walk through the Ontology Pipeline and demonstrate how the Ontology Pipeline transforms what has traditionally been a “black box” process into a tangible, scoped workflow that enables organizations to accurately estimate investment requirements, measure returns, and build the semantically rich data ecosystems that improve performance in RAG implementations, entity resolution, information retrieval, and machine learning classification tasks.