Knowledge Graph Extraction for Cyber incidents
This model has been finetuned with SecureBERT (https://arxiv.org/abs/2204.02685) on the CASIE dataset (https://ieeexplore.ieee.org/document/9776031). We have implemented the approach described in the CASIE paper.
Model Description
The following description is taken from the CASIE paper:
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An event nugget is a word or phrase that most clearly expresses the event occurrence. These differ from event triggers in that they can be multi-word phrases.
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An event argument is an event participant or property value. They can be taggable entities involved in the event, such as person or organization, or attributes that specify important information, such as time or amount.
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A role is a semantic relation between an event nugget and an argument. Each event type specifies the roles it can have and constraints on the arguments that can fill them.
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A realis value specifies whether or not an event occurred and can be one of the three values: Actual (event actually happened), Other (failed event, future event), or Generic (an undetermined/non-specific event, such as referring to the concept of phishing attacks).
Example
Usage
from transformers import AutoModelForTokenClassification
model = AutoModelForTokenClassification.from_pretrained("CyberPeace-Institute/Cybersecurity-Knowledge-Graph", trust_remote_code=True)
input_text = "This is a Cybersecurity-related text."
output = model(input_text)
IMPORTANT! : To get the Argument to Role coreferences, use the dedicated space! You can download the models under "arg_role_models/".