The Future of Graph-based Spatial Pattern Matching (Vision Paper)

Nicole R. Schneider, Kent O'Sullivan, Hanan Samet

Abstract: Spatial Pattern Matching is an important problem in information retrieval that involves reasoning about the relative position, distance, and orientation of objects with respect to each other. Most spatial pattern matching approaches use large, complex graphs or multigraphs to explicitly encode rich spatial information. The downside of this complexity is that search over spatial patterns remains badly constrained by computationally intensive classes of algorithms, like subgraph matching and constraint satisfaction. This paper highlights the recent approaches to graph-based spatial pattern matching, and presents a vision of the way forward, using graph-based Artificial Intelligence as a flexible, approximate approach to the otherwise intractable problem.