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FIBO (Financial Industry Business Ontology) is a structured framework that bridges theoretical financial concepts and real‑world data, especially suited for fintech machine‑learning research. The dataset consists of triples (subject, predicate, object) representing relationships among financial concepts. Subjects denote financial entities, predicates denote relation types, and objects denote related entities. FIBO covers a wide range of concepts from derivatives to securities, designed on knowledge‑representation principles and expert financial knowledge, enabling deep understanding of financial instruments. Its structured approach decodes complex financial relationships so that ML algorithms can discover patterns in large‑scale data. FIBO also links concepts to real‑world financial data and controlled vocabularies, which is critical for applying theoretical insights in practical environments.
