  We present a Wikidata-based framework, called KIF, for virtually integrating
heterogeneous knowledge sources. KIF is written in Python and is released as
open-source. It leverages Wikidata's data model and vocabulary plus
user-defined mappings to construct a unified view of the underlying sources
while keeping track of the context and provenance of their statements. The
underlying sources can be triplestores, relational databases, CSV files, etc.,
which may or may not use the vocabulary and RDF encoding of Wikidata. The end
result is a virtual knowledge base which behaves like an "extended Wikidata"
and which can be queried using a simple but expressive pattern language,
defined in terms of Wikidata's data model. In this paper, we present the design
and implementation of KIF, discuss how we have used it to solve a real
integration problem in the domain of chemistry (involving Wikidata, PubChem,
and IBM CIRCA), and present experimental results on the performance and
overhead of KIF
