  Knowledge graphs (KGs) have increasingly become the backbone of many critical
knowledge-centric applications. Most large-scale KGs used in practice are
automatically constructed based on an ensemble of extraction techniques applied
over diverse data sources. Therefore, it is important to establish the
provenance of results for a query to determine how these were computed.
Provenance is shown to be useful for assigning confidence scores to the
results, for debugging the KG generation itself, and for providing answer
explanations. In many such applications, certain queries are registered as
standing queries since their answers are needed often. However, KGs keep
continuously changing due to reasons such as changes in the source data,
improvements to the extraction techniques, refinement/enrichment of
information, and so on. This brings us to the issue of efficiently maintaining
the provenance polynomials of complex graph pattern queries for dynamic and
large KGs instead of having to recompute them from scratch each time the KG is
updated. Addressing these issues, we present HUKA which uses provenance
polynomials for tracking the derivation of query results over knowledge graphs
by encoding the edges involved in generating the answer. More importantly, HUKA
also maintains these provenance polynomials in the face of updates---insertions
as well as deletions of facts---to the underlying KG. Experimental results over
large real-world KGs such as YAGO and DBpedia with various benchmark SPARQL
query workloads reveals that HUKA can be almost 50 times faster than existing
systems for provenance computation on dynamic KGs.
