  Knowledge Base Question Answering (KBQA) tasks that involve complex reasoning
are emerging as an important research direction. However, most existing KBQA
datasets focus primarily on generic multi-hop reasoning over explicit facts,
largely ignoring other reasoning types such as temporal, spatial, and taxonomic
reasoning. In this paper, we present a benchmark dataset for temporal
reasoning, TempQA-WD, to encourage research in extending the present approaches
to target a more challenging set of complex reasoning tasks. Specifically, our
benchmark is a temporal question answering dataset with the following
advantages: (a) it is based on Wikidata, which is the most frequently curated,
openly available knowledge base, (b) it includes intermediate sparql queries to
facilitate the evaluation of semantic parsing based approaches for KBQA, and
(c) it generalizes to multiple knowledge bases: Freebase and Wikidata. The
TempQA-WD dataset is available at https://github.com/IBM/tempqa-wd.
