Marrying Uncertainty and Time in Knowledge Graphs

The management of uncertainty is crucial when harvest- ing structured content from unstructured and noisy sources. Knowledge Graphs (KGs) are a prominent example. KGs maintain both numerical and non-numerical facts, with the support of an underlying schema, usually accompanied by a confidence score that witnesses how likely is for a fact to hold. Despite their popularity, most of existing KGs focus on static data thus impeding the availability of timewise knowl- edge. What is missing is a comprehensive solution for the management of uncertain and temporal data in KGs. The goal of this paper is to fill this gap. We rely on two main ingre- dients. The first is a numerical extension of Markov Logic Networks (MLNs) networks that provide the necessary un- derpinning to formalize the syntax and semantics of uncertain temporal KGs. The second is a set of Datalog constraints with inequalities that extend the underlying schema of the KGs and help to detect inconsistencies. From a theoretical point of view, we discuss the complexity of two important classes of queries, maximum a-posteriori and conditional probabil- ity inference, for uncertain temporal KGs. Due to the hard- ness of both these problems and the fact that MLN solvers do not scale well, we also explore the usage of Probabilistic Soft Logics (PSL) as a practical tool to support our reasoning tasks. We report on an experimental evaluation comparing the MLN and PSL approaches.