Social Network Extraction Unsupervised

TL;DR

This paper presents an unsupervised stream of methods for extracting social networks from large information sources, aiming to improve integration, simplification, and enrichment of results in data science and AI contexts.

Contribution

It introduces a novel unsupervised approach for social network extraction that combines various methods to enhance result integration and effectiveness.

Findings

Multiple approaches can be integrated for better social network extraction.

Unsupervised methods simplify and enrich the extraction process.

The approach addresses contradictions in AI-based knowledge extraction.

Abstract

In the era of information technology, the two developing sides are data science and artificial intelligence. In terms of scientific data, one of the tasks is the extraction of social networks from information sources that have the nature of big data. Meanwhile, in terms of artificial intelligence, the presence of contradictory methods has an impact on knowledge. This article describes an unsupervised as a stream of methods for extracting social networks from information sources. There are a variety of possible approaches and strategies to superficial methods as a starting concept. Each method has its advantages, but in general, it contributes to the integration of each other, namely simplifying, enriching, and emphasizing the results.