| --- |
| 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. |
|
|
| #### Open Source |
| The FIBO ontology is developed on GitHub at |
| https://github.com/edmcouncil/fibo/. |
|
|
| ### Use-cases |
| - 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](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](https://www.michaeldebellis.com/post/export-inferred-axioms). |
| 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. This |
| compressed artifact is downloaded and extracted by the Hugging Face |
| datasets library to yield the examples in the dataset. |
|
|
| ### 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') |
| ``` |
|
|
| ## 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. |
|
|
| #### 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: |
| `<https://spec.edmcouncil.org/fibo/ontology/SEC/Equities/EquitiesExampleIndividuals/XNYSListedTheCoca-ColaCompanyCommonStock>` |
| 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: |
| `<https://spec.edmcouncil.org/fibo/ontology/SEC/Securities/SecuritiesListings/isTradedOn>` |
| 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: |
| `<https://spec.edmcouncil.org/fibo/ontology/FBC/FunctionalEntities/NorthAmericanEntities/USMarketsAndExchangesIndividuals/NewYorkStockExchange>` |
| represents the New York Stock Exchange where the aforementioned |
| Coca-Cola common stock is traded. |
|
|
| #### Continued |
| Here is an another 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](https://spec.edmcouncil.org/fibo/ontology/FBC/FunctionalEntities/MarketsIndividuals/ServiceProvider-L-JEUVK5RWVJEN8W0C9M24) |
| has a type of |
| [FunctionalEntity](https://spec.edmcouncil.org/fibo/ontology/BE/FunctionalEntities/FunctionalEntities/FunctionalEntity). |
|
|
| #### Note: |
| 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](https://spec.edmcouncil.org/fibo/ontology) to |
| explore the ontology on the web. One of the coolest features about |
| FIBO is that entities with a prefix of |
| https://spec.edmcouncil.org/fibo/ontology/ 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`, |
| `<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{fibo2023Q3, |
| title={Financial Industry Business Ontology (FIBO)}, |
| author={Object Management Group, Inc. and EDM Council, Inc. and Various Contributors}, |
| year={2023}, |
| 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.}, |
| howpublished={\url{https://spec.edmcouncil.org/fibo/}}, |
| 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{https://opensource.org/licenses/MIT}} |
| } |
| ``` |
|
|
