Abstract
An effective, accurate algorithm for cardinality estimation of queries on ontology models of data is presented. The algorithm relies on the decomposition of queries into query pattern paths, where each path produces a set of values for each variable within the result form of the query. In order to estimate the total number of result set parameters for each path, a set of statistics is compiled on the properties of the ontology. Experimental analysis has shown that the algorithm produces estimates with high accuracy and with high correlation to actual values. Thus, this algorithm can be used as the cornerstone of an effective optimization strategy for queries on diverse, heterogeneous data sources modeled as ontologies.