Uncertain Temporal Knowledge Graphs

Abstract

Temporal data can be found in various sources from patient histories, purchase histories, employee histories, to web logs. Recent advances in open information extraction have paved the way for automatic construction of knowledge graphs (kgs) from such sources. Often the extraction tools used to construct kgs produce facts and rules along with their confidence scores, leading to the notion of uncertain temporal kgs. The facts and rules contained in these graphs tend to be noisy and erroneous due to either the accuracy of the extraction tools or uncertainty in the source data. In this work, we use a numerical extension of Markov logic networks to provide formal syntax and semantics for uncertain temporal kgs. Moreover, we propose a set of datalog constraints with inequalities, that extend the underlying schema of the kgs and help in resolving conflicting facts. Finally, we characterize the complexity of two important queries, maximum a-posteriori and conditional probability inference, for uncertain temporal kgs.

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