  Question answering systems may find the answers to users' questions from
either unstructured texts or structured data such as knowledge graphs.
Answering questions using supervised learning approaches including deep
learning models need large training datasets. In recent years, some datasets
have been presented for the task of Question answering over knowledge graphs,
which is the focus of this paper. Although many datasets in English were
proposed, there have been a few question-answering datasets in Persian. This
paper introduces \textit{PeCoQ}, a dataset for Persian question answering. This
dataset contains 10,000 complex questions and answers extracted from the
Persian knowledge graph, FarsBase. For each question, the SPARQL query and two
paraphrases that were written by linguists are provided as well. There are
different types of complexities in the dataset, such as multi-relation,
multi-entity, ordinal, and temporal constraints. In this paper, we discuss the
dataset's characteristics and describe our methodology for building it.
