4.5 Further Development

The team has considered the possibility of extending the ontology to represent the relationships between individuals who do not belong to any youth subculture for various reasons - e.g., they do not fall within an age range typically considered "youth" -, but who still sympathize with the values associated with these subcultures. This extension would allow for a more nuanced understanding of how subcultural values can transcend age boundaries and influence individuals outside the typical youth demographic. By incorporating this additional data, the ontology would provide richer, more diverse perspectives on how different generations interact with and perceive youth subcultures, offering insights into intergenerational value transmission and social dynamics.

In this context, the dataset could also be expanded to include such instances, adding a new column titled "Generation" to identify the age group of the individuals under consideration.

To further explore this idea, we have developed a survey aimed at older generations to gather data and extract relevant statistics. This survey will help to understand how subcultural values resonate with individuals beyond the typical youth demographic.

Google Form Survey

We requested the LLMs to refine the latest user scenario provided for the Second Level Modelling (section 4.3.1).

Intergenerational Attitudes Toward Youth Subcultures

Recently, Giulia has become curious about the intergenerational dynamics related to youth subcultures - specifically, how people outside the typical "youth" age range perceive these subcultures and whether they resonate with the values associated with them.

N.B.
The typical age range for "youth" can vary depending on cultural, social, and academic contexts. Generally, in many Western societies, youth is often considered to include individuals from around 10 to 24 years old, as defined by the World Health Organization (WHO). This range covers the teenage years and extends into early adulthood, capturing the period when individuals are often exploring and forming their identities, including their affiliations with various subcultures.
Cfr. https://en.wikipedia.org/wiki/Youth.

To facilitate her research, Giulia needs to explore how different subcultures are perceived by both insiders and outsiders, including those who, despite not being part of any subculture due to age, still hold positive attitudes towards them and how this affects the intergenerational transmission of cultural values.

To achieve these goals, Giulia aims to analyze how people outside the typical youth demographic perceive and relate to these subcultures.

Generational cohorts:

  • Silent Generation: 1928-1945

    Known for traditional values and resilience through major global events.

  • Baby Boomers: 1946-1964

    Influenced by post-war prosperity and significant social changes.

  • Generation X: 1965-1980

    Independent and tech-savvy, shaped by economic and political shifts.

  • Millennials (Generation Y): 1981-1996

    Digital natives, impacted by technology and global events.

  • Generation Z: 1997-2012

    Digital-first, socially conscious, and diverse.

  • Generation Alpha: 2013-present

    Growing up with advanced technology and smart devices.

Based on the refined scenario and the specifications aimed to capture, the team formulated the following competency questions:

"Which individuals over a certain age have a positive attitude towards specific youth subcultures?"

To test the ontology's ability to represent these dynamics, we added a fictitious individual to the populated ontology. Finally, we designed a targeted SPARQL query to evaluate the ontology's effectiveness in capturing the intended relationships and scenarios.

PREFIX youth: <http://www.semanticweb.org/ontologies/2024/YOUTH/>
PREFIX persp: <http://www.ontologydesignpatterns.org/ont/persp/perspectivisation.owl#>
PREFIX owl: <http://www.w3.org/2002/07/owl#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX xml: <http://www.w3.org/XML/1998/namespace>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>

SELECT ?individual ?name
WHERE {
  ?individual a ?type ;
              youth:hasViewpoint youth:ExternalViewpoint ;
              youth:hasAttitude youth:PositiveAttitude ;
              youth:hasPerceptionOf youth:skaters ;
              youth:hasName ?name .

FILTER (?type IN (youth:Participant, youth:NotParticipant))
}

"Which individuals have a negative attitude towards the skaters youth subculture and participate in it?"

PREFIX youth: <http://www.semanticweb.org/ontologies/2024/YOUTH/>
PREFIX persp: <http://www.ontologydesignpatterns.org/ont/persp/perspectivisation.owl#>
PREFIX owl: <http://www.w3.org/2002/07/owl#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX xml: <http://www.w3.org/XML/1998/namespace>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>

SELECT ?individual
WHERE {
  ?individual a youth:Participant ;
              youth:hasViewpoint youth:InternalViewpoint ;
              youth:hasAttitude youth:NegativeAttitude ;
}

Machine Learning x Visual Knowledge Extraction

Furthermore, knowledge extraction could be enhanced using specific Machine Learning algorithms. A potential pipeline for this process is outlined as follows.

First, images would be annotated to highlight specific regions depicting visual elements associated with the fashion style of a youth subculture - such as colors, makeup, haircut, and clothing. These annotated images would then serve as training data for a Machine Learning model designed to recognize and classify these elements.

The next step would involve training the model using supervised learning techniques to accurately identify these visual pattern components in a variety of images. Once the model is sufficiently trained, it would be tested and validated against a separate dataset to ensure accuracy and robustness. Following successful validation, the system could be deployed to automatically process and analyze new images, extracting knowledge about the visual and stylistic attributes that define the subculture.

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