A well-designed taxonomy, ontology, and schema are fundamental to teaching machines to understand financial data and information patterns. Similarly, Subject Matter Experts (SMEs), clean data, and logic-based engines are indispensable for making sense of financial data.
Taxonomies, ontologies, schemas, and data models are essential for leveraging Artificial Intelligence (AI). They provide the structure that enables machines to read, interpret, make sense of, and understand unstructured data. Likewise, SMEs with domain knowledge, skill, expertise, and training are critical for correctly building taxonomies, ontologies, knowledge graphs, schemas, theories, and models.
Furthermore, clean data is essential for the pattern detection and documentation process. This process helps determine the deeper meaning, significance, or essence of the collective experience for those within an area of knowledge. This yields machine-readable taxonomies, ontologies, schemas, knowledge graphs, theories, models, and meta-models that make AI work, scale, and perform critical tasks.
The machine-readable information has to work seamlessly with a logic/rules/reasoning-based engine that is tuned for scrutinizing and working with financial reports to make sense of financial data and information. Over time, detecting patterns from financial data and information will improve as machine-readable information and tools evolve.
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