update readme

README.md

@@ -1,3 +1,181 @@
 ---
+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}}
+}
+```

requirements.txt

@@ -0,0 +1 @@
+rdflib>=6.0.0