An Experimental Analysis of Geographic Knowledge Graph Completion Methods
Association for Artificial Intelligence 2026
Underline Science Inc.
Problems Identified (5)
Incomplete geographic knowledge graphs: Geographic knowledge graphs are often incomplete because they are typically derived from crowd-sourced data, making completion important.
Spatially unaware KGC methods: Most current knowledge graph completion methods are generic and do not account for the spatial nature of geographic knowledge graphs.
Impractical geo-specific KGC methods: Existing methods tailored to geographic knowledge graphs are described as computationally expensive or designed for an impractical closed-world setting.
Incomplete geographic knowledge graphs: Geographic knowledge graphs are often incomplete because they are typically derived from crowd-sourced data, making completion important.
Spatially unaware KGC methods: Most current knowledge graph completion methods are generic and do not account for the spatial nature of geographic knowledge graphs.
Proposed Solutions (4)
Benchmark evaluation of KGC methods: The paper evaluates existing state-of-the-art standard and geo-specific knowledge graph completion methods on a large dataset of geographic knowledge graphs.
Research direction recommendations: The paper suggests possible areas of future work for geographic knowledge graph completion.
Benchmark evaluation of KGC methods: The paper evaluates existing state-of-the-art standard and geo-specific knowledge graph completion methods on a large dataset of geographic knowledge graphs.
Research direction recommendations: The paper suggests possible areas of future work for geographic knowledge graph completion.
Results (3)
Poor KGC method performance:
Open research problem remains:
Poor KGC method performance:
Research Domain
Geographic knowledge graph completion