The Dataset Viewer has been disabled on this dataset.

FIBO: The Financial Industry Business Ontology


In the world of financial technology, the vastness of data and the complexity of financial instruments present both challenges and opportunities. The Financial Industry Business Ontology (FIBO) offers a structured framework that bridges the gap between theoretical financial concepts and real-world data. I believe machine learning researchers interested in the financial sector could use the relationships in FIBO to innovate in financial feature engineering to fine-tune existing models or build new ones.

Open Source

The FIBO ontology is developed on GitHub at


  • Comprehensive Data Structure: FIBO offers a broad spectrum of financial concepts, ranging from derivatives to securities. This design, rooted in expert knowledge from both the knowledge representation and financial sectors, ensures a profound understanding of financial instruments.
  • Decoding Complex Relationships: The financial domain is characterized by its intricate interdependencies. FIBO's structured approach provides clarity on these relationships, enabling machine learning algorithms to identify patterns and correlations within large datasets.
  • Linkage with Real-world Data: A distinguishing feature of FIBO is its capability to associate financial concepts with real-world financial data and controlled vocabularies. This connection is crucial for researchers aiming to apply theoretical insights in practical contexts in financial enterprises with their existing data.
  • Retrieval Augmented Generation: The advent of Large Language Models, particularly in conjunction with Retrieval Augmented Generation (RAG), holds promise for revolutionizing the way financial data is processed and interpreted.
  • Document Classification: With the surge in financial documents, utilizing RAG to categorize financial datasets classifed by FIBO concepts can assist financial analysts in achieving enhanced accuracy and depth in data interpretation, facilitated by intelligent prompting.

Building and Verification:

  1. Construction: The ontology was imported from AboutFIBOProd-IncludingReferenceData into Protege version 5.6.1.
  2. Reasoning: Due to the large size of the ontology I used the ELK reasoner plugin to materialize (make explicit) inferences in the ontology.
  3. Coherence Check: The Debug Ontology plugin in Protege was used to ensure the ontology's coherence and consistency.
  4. Export: After verification, inferred axioms, along with asserted axioms and annotations, were exported using Protege.
  5. Encoding and Compression: Apache Jena's riot was used to convert the result to ntriples, which was then compressed with gzip. This compressed artifact is downloaded and extracted by the Hugging Face datasets library to yield the examples in the dataset.


First make sure you have the requirements installed:

pip install datasets
pip install rdflib

You can load the dataset using the Hugging Face Datasets library with the following Python code:

from datasets import load_dataset
dataset = load_dataset('wikipunk/fibo2023Q3', split='train')


The FIBO dataset is composed of triples representing the relationships between different financial concepts and named individuals such as market participants, corporations, and contractual agents.

Note on Format:

The subject, predicate, and object features are stored in N3 notation with no prefix mappings. This allows users to parse each component using rdflib.util.from_n3 from the RDFLib Python library.

1. Subject (string)

The subject of a triple is the primary entity or focus of the statement. In this dataset, the subject often represents a specific financial instrument or entity. For instance: <> refers to the common stock of The Coca-Cola Company that is listed on the NYSE.

2. Predicate (string)

The predicate of a triple indicates the nature of the relationship between the subject and the object. It describes a specific property, characteristic, or connection of the subject. In our example: <> signifies that the financial instrument (subject) is traded on a particular exchange (object).

3. Object (string)

The object of a triple is the entity or value that is associated with the subject via the predicate. It can be another financial concept, a trading platform, or any other related entity. In the context of our example: <> represents the New York Stock Exchange where the aforementioned Coca-Cola common stock is traded.


Here is an another example of a triple in the dataset:

  • Subject: "<>"
  • Predicate: "<>
  • Object: "<>"

This triple represents the statement that the market individual ServiceProvider-L-JEUVK5RWVJEN8W0C9M24 has a type of FunctionalEntity.


The dataset contains example individuals from the ontology as reference points. These examples provide a structured framework for understanding the relationships and entities within the financial domain. However, the individuals included are not exhaustive. With advancements in Large Language Models, especially Retrieval Augmented Generation (RAG), there's potential to generate and expand upon these examples, enriching the dataset with more structured data and insights.

FIBO Viewer

Use the FIBO Viewer to explore the ontology on the web. One of the coolest features about FIBO is that entities with a prefix of can be looked up in the web just by opening its URL in a browser or in any HTTP client.

Ideas for Deriving Graph Neural Network Features from FIBO:

Graph Neural Networks (GNNs) have emerged as a powerful tool for machine learning on structured data. FIBO, with its structured ontology, can be leveraged to derive features for GNNs.

Node Features:

  • rdf:type: Each entity in FIBO has one or more associated rdf:type, <>, that indicates its class or category. This can serve as a primary node feature to encode.

  • Entity Attributes: Attributes of each entity, such as names or descriptions, can be used as additional node features. Consider embedding descriptions using a semantic text embedding model.

Edge Features:

  • RDF Predicates: The relationships between entities in FIBO are represented using RDF predicates. These predicates can serve as edge features in a GNN, capturing the nature of the relationship between nodes.

Potential Applications:

  1. Entity Classification: Using the derived node and edge features, GNNs can classify entities into various financial categories, enhancing the granularity of financial data analysis.

  2. Relationship Prediction: GNNs can predict potential relationships between entities, aiding in the discovery of hidden patterns or correlations within the financial data.

  3. Anomaly Detection: By training GNNs on the structured data from FIBO and interlinked financial datasets, anomalies or irregularities in them may be detected, ensuring data integrity and accuracy.


We extend our sincere gratitude to the FIBO contributors for their meticulous efforts in knowledge representation. Their expertise and dedication have been instrumental in shaping a comprehensive and insightful framework that serves as a cornerstone for innovation in the financial industry.

If you are interested in modeling the financial industry you should consider contributing to FIBO.


  title={Financial Industry Business Ontology (FIBO)},
  author={Object Management Group, Inc. and EDM Council, Inc. and Various Contributors},
  note={Available as OWL 2 ontologies and UML models compliant with the Semantics for Information Modeling and Federation (SMIF) draft specification. Contributions are open on GitHub, consult the repository for a list of contributors.},
  abstract={The Financial Industry Business Ontology (FIBO) is a collaborative effort to standardize the language used to define the terms, conditions, and characteristics of financial instruments; the legal and relationship structure of business entities; the content and time dimensions of market data; and the legal obligations and process aspects of corporate actions.},
  license={MIT License, \url{}}
Downloads last month
Edit dataset card