# Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization

arXiv: https://arxiv.org/abs/2403.16222 · HTML: https://mlsanity.com/paper/2403.16222/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 24 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [On the Use of ArXiv as a Dataset](https://mlsanity.com/paper/1905.00075/) (2019)
- [Domain-specific Knowledge Graphs: A survey](https://mlsanity.com/paper/2011.00235/) (2020)
- [AceKG: A Large-scale Knowledge Graph for Academic Data Mining](https://mlsanity.com/paper/1807.08484/) (2018)
- [RelExt: Relation Extraction using Deep Learning approaches for Cybersecurity Knowledge Graph Improvement](https://mlsanity.com/paper/1905.02497/) (2019)
- [Neural Attention-Aware Hierarchical Topic Model](https://mlsanity.com/paper/2110.07161/) (2021)
- [SeNMFk-SPLIT: Large Corpora Topic Modeling by Semantic Non-negative Matrix Factorization with Automatic Model Selection](https://mlsanity.com/paper/2208.09942/) (2022)
- [An Analysis of COVID-19 Knowledge Graph Construction and Applications](https://mlsanity.com/paper/2110.04932/) (2021)
- [Recurrent Hierarchical Topic-Guided RNN for Language Generation](https://mlsanity.com/paper/1912.10337/) (2019)
- [Topic Analysis of Superconductivity Literature by Semantic Non-negative Matrix Factorization](https://mlsanity.com/paper/2201.00687/) (2021)

## Built on by (most cited first, 1 of 1)

- [Binary Bleed: Fast Distributed and Parallel Method for Automatic Model Selection](https://mlsanity.com/paper/2407.19125/) (2024)

## Abstract (arXiv)

Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document organization methods becomes increasingly crucial for extracting actionable insights hidden within large text datasets. Knowledge Graphs (KGs) serve as a means to store factual information in a structured manner, providing explicit, interpretable knowledge that includes domain-specific information from the cybersecurity scientific literature. One of the challenges in constructing a KG from scientific literature is the extraction of ontology from unstructured text. In this paper, we address this topic and introduce a method for building a multi-modal KG by extracting structured ontology from scientific papers. We demonstrate this concept in the cybersecurity domain. One modality of the KG represents observable information from the papers, such as the categories in which they were published or the authors. The second modality uncovers latent (hidden) patterns of text extracted through hierarchical and semantic non-negative matrix factorization (NMF), such as named entities, topics or clusters, and keywords. We illustrate this concept by consolidating more than two million scientific papers uploaded to arXiv into the cyber-domain, using hierarchical and semantic NMF, and by building a cyber-domain-specific KG.