Ontology Verification Framework to facilitate Domain Expert Involvement

Development of a comprehensive tool or framework designed to simplify ontology verification for domain experts.Include a user-friendly interface, visualization tools, and automated suggestions to assist in the review and refinement of ontologies.

• How can ontology verification processes be made accessible and efficient for domain experts who lack technical expertise in ontology engineering?
• What features are essential in a tool to facilitate effective collaboration between domain experts and ontology engineers?

verwandte Bereiche: Ontology Engineering, Human-Computer Interaction, Collaborative System

Allgemeine Anforderungen:

• Understanding of ontology development
• Skills in software development and user interface design
• Familiarity with collaborative tools and version control systems

Empfohlene Studienstufe: Master
Zeitrahmen: ab Juli 2024
Betreuerin: Kiara Ascencion

Systematic Evaluation Framework for Ontology Engineering

Development of a framework for systematical evaluation of ontologies. It includes establishing evaluation criteria, using automated tools for analysis, and conducting case studies for real-world testing.

• What criteria and metrics are most effective for evaluating the quality of constructed ontologies?
• How can automated tools be utilized to assess ontologies against these criteria?

verwandte Bereiche: Ontology Engineering, Knowledge Modelling, Data Quality Assessment

Allgemeine Anforderungen:

• Understanding of Ontology engineering and knowledge representation
• Skills in programming for developing or adapting evaluation tools.
• Analytical skills for comparative analysis (ontology evaluation and results interpretation)
• Competence in designing and conducting case studies and empirical research.

Empfohlene Studienstufe: Master
Zeitrahmen: ab Juli 2024
Betreuerin: Kiara Ascencion

The Evolution of Job Roles in the Technological Era: Development of a Semi-automated Approach for Requirement and Responsibility Tracking

• How can a semi-automated methodology be developed to track the emergence and evolution of job roles in response to technological advancements?
• What are the key skills and technologies driving the development of emerging job roles, and how have these changed over time?
• Can machine learning models predict future trends in job roles based on historical data and technological trajectories?

This topic delves into the evolution of job roles in response to technological advancements. A semi-automated approach is proposed to analyze large datasets containing job descriptions, skills requirements, and technological keywords. The thesis aims to provide insights into the changing landscape of job markets, helping educational institutions and job seekers prepare for future skill demands.

Allgemeine Anforderungen:

• Proficiency in Natural Language Processing (NLP) for job description analysis.
• Knowledge of machine learning algorithms for trend prediction.
• Familiarity with theories on job market evolution and technological innovation.

Empfohlene Studienstufe: Master
Zeitrahmen: ab Juli/August 2024
Betreuerin: Natalie Rubin

Bridging the Gap: A Semi-Automated Analysis of Skills Mismatch in Education and Industry

• How can a semi-automated approach be developed to analyze skills mentioned in job postings and align them with educational program curricula?
• What are the prevailing patterns of skills mismatch over time, and how do they vary across industries?
• Can machine learning models predict potential skills gaps based on current industry trends and educational offerings?

The thesis explores the persistent issue of skills mismatch between education and industry demands. A semi-automated methodology is proposed to bridge this gap by aligning the skills emphasized in job postings with the skills taught in relevant educational programs. The goal is to provide insights into improving educational curricula and addressing the dynamic needs of the job market.

Allgemeine Anforderungen:

• Familiarity with Natural Language Processing (NLP) and its application areas.
• Basic knowledge on labor market frameworks and dynamics.
• Knowledge on statistical data analysis and forecasting.

Empfohlene Studienstufe: Master
Zeitrahmen: ab Oktober/November 2024
Betreuerin: Natalie Rubin