# Implementation of a noisy hyperlink removal system: A semantic and relatedness approach

arXiv: https://arxiv.org/abs/2303.03321 · HTML: https://mlsanity.com/paper/2303.03321/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 6 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain-based IIoT Networks](https://mlsanity.com/paper/2204.09829/) (2022)
- [Security Considerations for Virtual Reality Systems](https://mlsanity.com/paper/2201.02563/) (2022)
- [The Dichotomy of Cloud and IoT: Cloud-Assisted IoT From a Security Perspective](https://mlsanity.com/paper/2207.01590/) (2022)
- [Web3 Challenges and Opportunities for the Market](https://mlsanity.com/paper/2209.02446/) (2022)

## Built on by (most cited first, 2 of 2)

- [Automatic summarisation of Instagram social network posts Combining semantic and statistical approaches](https://mlsanity.com/paper/2303.07957/) (2023)
- [Building an Effective Email Spam Classification Model with spaCy](https://mlsanity.com/paper/2303.08792/) (2023)

## Abstract (arXiv)

As the volume of data on the web grows, the web structure graph, which is a graph representation of the web, continues to evolve. The structure of this graph has gradually shifted from content-based to non-content-based. Furthermore, spam data, such as noisy hyperlinks, in the web structure graph adversely affect the speed and efficiency of information retrieval and link mining algorithms. Previous works in this area have focused on removing noisy hyperlinks using structural and string approaches. However, these approaches may incorrectly remove useful links or be unable to detect noisy hyperlinks in certain circumstances. In this paper, a data collection of hyperlinks is initially constructed using an interactive crawler. The semantic and relatedness structure of the hyperlinks is then studied through semantic web approaches and tools such as the DBpedia ontology. Finally, the removal process of noisy hyperlinks is carried out using a reasoner on the DBpedia ontology. Our experiments demonstrate the accuracy and ability of semantic web technologies to remove noisy hyperlinks