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.
@article{arxiv.2201.05793,
title = {A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases},
author = {Sumit Neelam and Udit Sharma and Hima Karanam and Shajith Ikbal and Pavan Kapanipathi and Ibrahim Abdelaziz and Nandana Mihindukulasooriya and Young-Suk Lee and Santosh Srivastava and Cezar Pendus and Saswati Dana and Dinesh Garg and Achille Fokoue and G P Shrivatsa Bhargav and Dinesh Khandelwal and Srinivas Ravishankar and Sairam Gurajada and Maria Chang and Rosario Uceda-Sosa and Salim Roukos and Alexander Gray and Guilherme Lima and Ryan Riegel and Francois Luus and L Venkata Subramaniam},
journal= {arXiv preprint arXiv:2201.05793},
year = {2022}
}
Comments
7 pages, 2 figures, 7 tables. arXiv admin note: substantial text overlap with arXiv:2109.13430