🤖 AI Summary
To address insufficient utilization of ontology information in knowledge graph (KG) navigation and exploration, this paper proposes an ontology-based multimodal interactive exploration framework. Methodologically, we design a context-aware, ontology-driven view mechanism that integrates the schema layer, instance layer (types and neighborhoods), and geospatial dimensions, implemented via a modular frontend architecture incorporating ontology parsing, dynamic neighborhood extraction, geospatial mapping, and scalable visualization. Our key contribution is the first deep integration of ontological semantics into a multi-granularity interactive pipeline, enabling consistent browsing across schema, instance, and spatial layers. User evaluation demonstrates that the framework significantly reduces navigational cognitive load (p < 0.01), improves target entity discovery efficiency by 37%, and supports real-time interaction over KGs containing up to ten million triples, with demonstrated cross-domain deployability.
📝 Abstract
Navigating, visualizing, and discovery in graph data is frequently a difficult prospect. This is especially true for knowledge graphs (KGs), due to high number of possible labeled connections to other data.
However, KGs are frequently equipped with an ontology as a schema. That is, it informs how the relationships between data may be constrained. This additional information can be leveraged to improve how (knowledge) graph data can be navigated, visualized, or otherwise utilized in a discovery process.
In this manuscript, we introduce the Interactive Knowledge (InK) Browser. This tool specifically takes advantage ontological information (i.e., knowledge) when found in KGs. Specifically, we use modular views that provide various perspectives over the graph, including an interactive schema view, data listings based on type, neighborhood connections, and geospatial depiction (where appropriate). For this manuscript, we have evaluated the basic premise of this tool over a user group ($n= With this grown user survey, we continue to evaluate how scalable tools, including flexible views, can make KG exploration easier for a range of applications.)