Analyzing real-world SPARQL queries in the light of probabilistic data
Jörg Schönfisch, Heiner Stuckenschmidt · MADOC (University of Mannheim) · 2016
Handling uncertain knowledge – like information extracted from unstructured text, with some probability of being correct – is crucial for modeling many real world domains. Ontologies and ontology-based data access (OBDA) have proven to be versatile methods to capture this knowledge. Multiple systems for OBDA have been developed and there is theoretical work towards probabilistic OBDA, namely identifying efficiently processable (safe) queries. However, there is no analysis on the safeness of probabilistic queries in real-world applications, or in other words the feasibility of fulfilling users’ information needs over probabilistic data. In this paper we investigate queries collected from several public SPARQL endpoints and determine the distribution of safe and unsafe queries. This analysis shows that many queries in practice are safe, making probabilistic OBDA feasible and practical to fulfill real-world users’ information needs.