# PeCoQ: A Dataset for Persian Complex Question Answering over Knowledge Graph

arXiv: https://arxiv.org/abs/2106.14167 · HTML: https://mlsanity.com/paper/2106.14167/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 27 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [The Web as a Knowledge-base for Answering Complex Questions](https://mlsanity.com/paper/1803.06643/) (2018)
- [Large-scale Simple Question Answering with Memory Networks](https://mlsanity.com/paper/1506.02075/) (2015)
- [Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases](https://mlsanity.com/paper/1903.02188/) (2019)
- [ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters](https://mlsanity.com/paper/1809.09528/) (2018)
- [Lambda Dependency-Based Compositional Semantics](https://mlsanity.com/paper/1309.4408/) (2013)

## Built on by (most cited first, 4 of 4)

- [A Survey on non-English Question Answering Dataset](https://mlsanity.com/paper/2112.13634/) (2021)
- [A Persian Benchmark for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/2303.00408/) (2023)
- [IslamicPCQA: A Dataset for Persian Multi-hop Complex Question Answering in Islamic Text Resources](https://mlsanity.com/paper/2304.11664/) (2023)
- [A Knowledge-based Approach for Answering Complex Questions in Persian](https://mlsanity.com/paper/2107.02040/) (2021)

## Abstract (arXiv)

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 textitPeCoQ, 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.