论文标题
可概括和可解释的时间问题的基准，回答知识基础
A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases
论文作者
论文摘要
涉及复杂推理的知识基础问题回答（KBQA）任务正在成为重要的研究方向。但是，大多数现有的KBQA数据集主要侧重于对明确事实的通用多跳跃推理，在很大程度上忽略了其他推理类型，例如时间，空间和分类学推理。在本文中，我们提出了一个用于时间推理的基准数据集Tempqa-WD，以鼓励研究扩展当前的方法，以针对一套更具挑战性的复杂推理任务。具体而言，我们的基准是一个时间问题，是一个暂时的答案数据集，具有以下优点：它基于Wikidata，它是Wikidata，它是最经常策划，公开可用的知识库，（b）它包括中间SPARQL查询，以促进基于KBQA的基于语义分析方法的中间sparql查询，以及（c）的一般知识。 TEMPQA-WD数据集可在https://github.com/ibm/tempqa-wd上找到。
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.
