Answering questions on scholarly knowledge comprising text and other artifacts is a vital part of any research life cycle. Querying scholarly knowledge and retrieving suitable answers is currently hardly possible due to the following primary reason: machine inactionable, ambiguous and unstructured content in publications. We present JarvisQA, a BERT based system to answer questions on tabular views of scholarly knowledge graphs. Such tables can be found in a variety of shapes in the scholarly literature (e.g., surveys, comparisons or results). Our system can retrieve direct answers to a variety of different questions asked on tabular data in articles. Furthermore, we present a preliminary dataset of related tables and a corresponding set of natural language questions. This dataset is used as a benchmark for our system and can be reused by others. Additionally, JarvisQA is evaluated on two datasets against other baselines and shows an improvement of two to three folds in performance compared to related methods.

中文翻译：

---

学术知识图谱问答

回答有关包含文本和其他人工制品的学术知识的问题是任何研究生命周期的重要组成部分。由于以下主要原因，目前很难查询学术知识并检索合适的答案：出版物中的机器无法操作、模棱两可和非结构化的内容。我们提出了 JarvisQA，这是一个基于 BERT 的系统，用于回答有关学术知识图的表格视图的问题。此类表格可以在学术文献（例如，调查、比较或结果）中以各种形式出现。我们的系统可以检索对文章中表格数据提出的各种不同问题的直接答案。此外，我们提供了相关表的初步数据集和一组相应的自然语言问题。该数据集用作我们系统的基准，可以被其他人重复使用。