DH^CH / Ramon Erdem-Sanchez

I am a master’s student in Political Science and Digital Humanities, with a professional background in education, civic advocacy, and community-based initiatives. My academic work focuses on economic inequality, political disaffection, and digital technology.
My current research uses RDF Linked Open Data to investigate how spatial and economic factors influence political engagement, building on earlier work that analyzed these dynamics through statistical and spatial methods. I am also exploring machine learning for my master’s thesis to assess how existing structural inequalities affect or disrupt political attitudes.
I bring a critical, interdisciplinary lens to computational research, emphasizing both technical competence and ethical reflection. I am particularly passionate about ensuring that digital innovation in the humanities remains socially grounded, inclusive, and critically informed by historical context and power dynamics.

This contribution explores how RDF-based linked open data (LOD) infrastructures, when combined with AI methods, can serve as ethical tools to identify both presence and absence in social science research. Drawing from my political science seminar research on economic inequality and political disaffection in California, and my current work developing a semantic RDF model of walkability and housing data from U.S. federal sources, I argue that research datasets should be embedded in semantic structures that expose where knowledge is dense, and where it is missing.
Rather than treating data absence as a passive outcome, this project proposes a proactive framework for identifying and visualizing underrepresented populations, concepts, or geographies across studies. AI systems are envisioned not only as tools for analysis but also as reflexive agents that help researchers detect epistemic gaps, bias propagation, and structural omissions in existing data regimes. By enabling such a diagnostic layer in the research process, RDF and AI can help shift study design toward greater transparency, inclusivity, and ethical responsibility.
This approach sits at the intersection of critical data studies, digital humanities, and computational social science, proposing a design-oriented vision for mitigating structural bias through the very infrastructures that govern how data is described, connected, and reused.

• RDF-based linked open data (LOD) infrastructures
• AI methods
• Ethical social science research

• Charting Absences: towards an RDF and ai-supported framework for ethical research design