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The Initial Exploration Problem in Knowledge Graph Exploration

arXiv 2602.21066 ↗First posted 24 Feb 2026cs.AIcited by 0

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Abstract (arXiv)

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 theories from information behaviour and human-computer interaction, including ASK, exploratory search, information foraging, and cognitive load theory, we develop a conceptual framing of the IEP characterised by three interdependent barriers: scope uncertainty, ontology opacity, and query incapacity. We argue that these barriers converge at the moment of first contact, distinguishing the IEP from related concepts that presuppose an existing starting point or information goal. Analysing KG exploration interfaces at the level of interaction primitives, we suggest that many systems rely on epistemic assumptions that do not hold at first contact. This reveals a structural gap in the design space: the absence of interaction primitives for scope revelation, mechanisms that communicate what a KG contains without requiring users to formulate queries or interpret ontological structures. In articulating the IEP, this paper provides a theoretical lens for evaluating KG interfaces and for designing entry-point scaffolding that supports initial exploration.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 56 references that are in the map. First posted on arXiv 24 Feb 2026.

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References · 7

  1. 2020Knowledge Graphscited by 619
  2. 2023Information Retrieval: Recent Advances and Beyondcited by 49
  3. 2023KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Modelscited by 32
  4. 2024LLM-based SPARQL Query Generation from Natural Language over Federated Knowledge Graphscited by 22
  5. 2023Knowledge Graphs in Practice: Characterizing their Users, Challenges, and Visualization Opportunitiescited by 9
  6. 2024Unpacking Human-AI interactions: From interaction primitives to a design spacecited by 7
  7. 2025OnSET: Ontology and Semantic Exploration Toolkitcited by 1

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