---
language:
- en
license: mit
tags:
- knowledge-graph
- rdf
- owl
- ontology
- cybersecurity
annotations_creators:
- crowdsourced
- expert-generated
pretty_name: D3FEND
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: 46899451
      num_examples: 231842
  dataset_size: 46899451
viewer: false
---

# D3FEND 

Branch: https://github.com/d3fend/d3fend-ontology/tree/release/0.13.0-BETA-1

Commit: 3dcc495879bb62cee5c4109e9b784dd4a2de3c9d

CWE extension:
https://github.com/d3fend/d3fend-ontology/tree/release/0.13.0-BETA-1/extensions/cwe

After building d3fend-full.owl I imported the ontology into Protege
version 5.6.1 with the Pellet reasoner plug-in. First I use the Debug
Ontology plugin to check the ontology for consistency and
coherency. If it all checks out, I move onto exporting the inferences.

In Protege navigate to File>Export Inferred Axioms as ontology and
make sure to check all of the checkboxes including asserted axioms and
annotations. See this blog post for more
information: https://www.michaeldebellis.com/post/export-inferred-axioms.

Once you have the materialized ontology you can filter it with
d3fend.sparql. 

``` shell
arq --query=d3fend.rq --data=d3fend.owl --results=turtle > d3fend.ttl
```

``` shell
riot --output=nt d3fend.ttl > d3fend.nt
```

The d3fend.owl and d3fend.nt files then are compressed using gzip.