Computing quality scores and uncertainty for approximate pattern matching in geospatial semantic graphs

Geospatial semantic graphs provide a robust foundation for representing and analyzing remote sensor data. In particular, semantic graphs support a variety of pattern search operations that capture the spatial and temporal relationships among the objects and events in the data. However, in the presence of large data corpora, even a carefully constructed search query can return a large number of unanticipated or spurious matches. This work considers the problem of calculating a quality score for each match to the query, given that the underlying data are uncertain. We present preliminary algorithms for determining both match quality scores and associated uncertainty bounds, illustrated in the context of an example problem. Finding Activities of Interest in Imagery