add notes on features

README.md

@@ -51,7 +51,7 @@ Researchers and cybersecurity enthusiasts can leverage D3FEND to:
 - Gain insight into ontology development and modeling in the
   cybersecurity domain.
 
-### Preprocessing
+### Dataset construction and pre-processing
 
 ### Source:
 - [Dataset Repository - 0.13.0-BETA-1](https://github.com/d3fend/d3fend-ontology/tree/release/0.13.0-BETA-1)
@@ -63,7 +63,7 @@ Researchers and cybersecurity enthusiasts can leverage D3FEND to:
    built from the beta version of the D3FEND ontology referenced
    above using documented README in d3fend-ontology. This includes the
    CWE extensions. 
-2. **Importation and Reasoning**: Imported into Protege version 5.6.1,
+2. **Import and Reasoning**: Imported into Protege version 5.6.1,
    utilizing the Pellet reasoner plugin for logical reasoning and
    verification.
 3. **Coherence Check**: Utilized the Debug Ontology plugin in Protege
@@ -89,9 +89,39 @@ line tools. (https://jena.apache.org/documentation/tools/)
 4. **Compression**: Compressed the resulting ontology files using
    gzip.
 
-### How to Load the Dataset:
+## Features
+The D3FEND dataset is composed of triples representing the
+relationships between different cybersecurity countermeasures. Each
+triple is a representation of a statement about a cybersecurity
+concept or a relationship between concepts. The dataset includes the
+following features:
 
-You can load this dataset using the Hugging Face Datasets library with
+### 1. **Subject** (`string`)
+The subject of a triple is the entity that the statement is about. In
+this dataset, the subject represents a cybersecurity concept or
+entity, such as a specific countermeasure or ATT&CK technique.
+
+### 2. **Predicate** (`string`)
+The predicate of a triple represents the property or characteristic of
+the subject, or the nature of the relationship between the subject and
+the object. For instance, it might represent a specific type of
+relationship like "may-be-associated-with" or "has a reference."
+
+### 3. **Object** (`string`)
+The object of a triple is the entity that is related to the subject by
+the predicate. It can be another cybersecurity concept, such as an
+ATT&CK technique, or a literal value representing a property of the
+subject, such as a name or a description.
+
+### 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
@@ -99,6 +129,35 @@ from datasets import load_dataset
 dataset = load_dataset('wikipunk/d3fend', split='train')
 ```
 
+#### Note on Format:
+The subject, predicate, and object are stored in N3 notation, a
+verbose serialization for RDF. This allows users to unambiguously
+parse each component using `rdflib.util.from_n3` from the RDFLib
+Python library. For example:
+
+```python
+from rdflib.util import from_n3
+subject_node = from_n3(dataset[0]['subject'])
+predicate_node = from_n3(dataset[0]['predicate'])
+object_node = from_n3(dataset[0]['object'])
+```
+
+Once loaded, each example in the dataset will be a dictionary with
+`subject`, `predicate`, and `object` keys corresponding to the
+features described above.
+
+### Example
+
+Here is an example of a triple in the dataset:
+- Subject: `"<http://d3fend.mitre.org/ontologies/d3fend.owl#T1550.002>"`
+- Predicate: `"<http://d3fend.mitre.org/ontologies/d3fend.owl#may-be-associated-with>"`
+- Object: `"<http://d3fend.mitre.org/ontologies/d3fend.owl#T1218.014>"`
+
+This triple represents the statement that the ATT&CK technique
+identified by `#T1550.002` may be associated with the ATT&CK technique
+identified by `#T1218.014` in the D3FEND knowledge graph of
+cybersecurity countermeasures
+
 ### Acknowledgements
 This ontology is developed by MITRE Corporation and is licensed under
 the MIT license. I would like to thank the authors for their work