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There is a growing need for methods which can capture uncertainties andanswer queries over graph-structured data. Two common types of uncertainty areuncertainty over the attribute values of nodes and uncertainty over theexistence of edges. In this paper, we combine those with identity uncertainty.Identity uncertainty represents uncertainty over the mapping from objectsmentioned in the data, or references, to the underlying real-world entities. Wepropose the notion of a probabilistic entity graph (PEG), a probabilistic graphmodel that defines a distribution over possible graphs at the entity level. Themodel takes into account node attribute uncertainty, edge existenceuncertainty, and identity uncertainty, and thus enables us to systematicallyreason about all three types of uncertainties in a uniform manner. We introducea general framework for constructing a PEG given uncertain data at thereference level and develop highly efficient algorithms to answer subgraphpattern matching queries in this setting. Our algorithms are based on two novelideas: context-aware path indexing and reduction by join-candidates, whichdrastically reduce the query search space. A comprehensive experimentalevaluation shows that our approach outperforms baseline implementations byorders of magnitude.

Subgraph Pattern Matching over Uncertain Graphs with Identity Linkage Uncertainty