---
language:
- en
license: mit
tags:
- knowledge-graph
- rdf
- owl
- ontology
annotations_creators:
- expert-generated
pretty_name: FIBO
size_categories:
- 100K<n<1M
task_categories:
- graph-ml
dataset_info:
  features:
    - name: subject
      dtype: string
    - name: predicate
      dtype: string
    - name: object
      dtype: string
  config_name: default
  splits:
    - name: train
      num_bytes: 56045523
      num_examples: 236579
  dataset_size: 56045523
viewer: false
---

# FIBO: The Financial Industry Business Ontology

### Overview
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.

### Use-cases
- Comprehensive Data Structure: FIBO encompasses a wide range of
  financial concepts, from derivatives to securities. Its design ensures
  an in-depth understanding of financial instruments from experts
  in knowledge representation and the financial industry.
- 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 emergence of Large Language
  Models, especially when using Retrieval Augmented Generation (RAG),
  has the potential to transform financial data processing and
  interpretation.
- Document Classification: With the surge in financial documents,
  utilizing RAG to classify financial datasets based on FIBO concepts
  may help financial analysts get better accuracy and depth in data
  interpretation with smart prompting. 

#### Building and Verification:
1. **Construction**: The ontology was imported using the
   [AboutFIBOProd-IncludingReferenceData](https://github.com/edmcouncil/fibo/blob/master/AboutFIBOProd-IncludingReferenceData.rdf)
   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](https://jena.apache.org/documentation/tools/) was used to convert the
   result to ntriples, which was then compressed with gzip.

## Features
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. 

### Usage
First make sure you have the requirements installed:

```python
pip install datasets
pip install rdflib
```

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

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

#### 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.

### Example
Here is an example of a triple in the dataset:
- Subject: `"<https://spec.edmcouncil.org/fibo/ontology/FBC/FunctionalEntities/MarketsIndividuals/ServiceProvider-L-JEUVK5RWVJEN8W0C9M24>"` 
- Predicate: `"<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>`
- Object: `"<https://spec.edmcouncil.org/fibo/ontology/BE/FunctionalEntities/FunctionalEntities/FunctionalEntity>"`

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

---

## 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`,
  `<http://www.w3.org/1999/02/22-rdf-syntax-ns#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.

### Acknowledgements
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](https://github.com/edmcouncil/fibo/blob/master/CONTRIBUTING.md).

### Citation
```bibtex
@misc{fiboQ32023,
  title={Financial Industry Business Ontology (FIBO) Q32023 Release},
  author={EDM Council and Various Contributors},
  year={2023},
  note={Derived from the AboutFIBOProd-IncludingReferenceData.rdf},
  howpublished={\url{https://spec.edmcouncil.org/fibo/}},
  license={MIT License, \url{https://opensource.org/licenses/MIT}}
}
```