Resolving Temporal Conflicts in Inconsistent RDF Knowledge Bases

Recent trends in information extraction have allowed us to not only extract large semantic knowledge bases from structured or loosely structured Web sources, but to also extract additional annotations along with the RDF facts these knowledge bases contain. Among the most important types of annotations are spatial and temporal annotations. In particular the latter temporal annotations help us to reflect that a majority of facts is not static but highly ephemeral in the real world, i.e., facts are valid for only a limited amount of time, or multiple facts stand in temporal dependencies with each other. In this paper, we present a declarative reasoning framework to express and process temporal consistency constraints and queries via first-order logical predicates. We define a subclass of first-order constraints with temporal predicates for which the knowledge base is guaranteed to be satisfiable. Moreover, we devise efficient grounding and approximation algorithms for this class of first order constraints, which can be solved within our framework. Specifically, we reduce the problem of finding a consistent subset of time-annotated facts to a scheduling problem and give an approximation algorithm for it. Experiments over a large temporal knowledge base (T-YAGO) demonstrate the scalability and excellent approximation performance of our framework.