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fibo2023Q3

Tasks:
Graph Machine Learning
Languages:
English
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Tags:
knowledge-graph
rdf
owl
ontology
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metadata
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 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.

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:

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')

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.

Citation 

@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}}
}