arXiv:2602.21066v2 Announce Type: replace Abstract: Knowledge Graphs (KGs) enable the integration and representation of complex information across domains, but their semantic richness and structural complexity create substantial barriers for lay users without expertise in semantic web technologies. When encountering an unfamiliar KG, such users face a distinct orientation challenge: they do not know what questions are possible, how the knowledge is structured, or how to begin exploration. This paper identifies and theorises this phenomenon as the Initial Exploration Problem (IEP). Drawing on t
As Knowledge Graphs become more prevalent and complex, the challenge of initial user engagement is becoming a critical bottleneck, prompting academic research into solutions.
Improving user accessibility to complex data structures like Knowledge Graphs is crucial for broader adoption of AI-driven tools and for leveraging integrated information effectively.
The explicit identification and theoretical framing of the 'Initial Exploration Problem' provides a new lens for developing user-centric interfaces and tools for complex data systems.
- · AI interface developers
- · Data visualization companies
- · Knowledge graph platform providers
- · Companies with highly complex and opaque data systems
- · Users without data science expertise
Easier access to Knowledge Graphs for non-expert users.
Accelerated adoption and utility of advanced AI systems that rely on structured knowledge.
Democratization of complex data analysis capabilities beyond specialized roles.
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Read at arXiv cs.AI