T e C o R e

Abstract

The management of uncertainty is crucial when harvesting structured content from unstructured and noisy sources. Knowledge Graphs ( kg s), maintaining both numerical and non-numerical facts supported by an underlying schema, are a prominent example. Knowledge Graph management is challenging because: (i) most of existing kg s focus on static data, thus impeding the availability of timewise knowledge; (ii) facts in kg s are usually accompanied by a confidence score, which witnesses how likely it is for them to hold. We demonstrate T e C o R e , a system for temporal inference and conflict resolution in uncertain temporal knowledge graphs ( utkg s). At the heart of T e C o R e are two state-of-the-art probabilistic reasoners that are able to deal with temporal constraints efficiently. While one is scalable, the other can cope with more expressive constraints. The demonstration will focus on enabling users and applications to find inconsistencies in utkg s. T e C o R e provides an interface allowing to select utkg s and editing constraints; shows the maximal consistent subset of the utkg , and displays statistics (e.g., number of noisy facts removed) about the debugging process.