论文标题
关于学术知识图的问题回答
Question Answering on Scholarly Knowledge Graphs
论文作者
论文摘要
回答有关包括文本和其他文物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，目前几乎不可能查询学术知识和检索合适的答案：在出版物中的机器不可能，模棱两可和非结构化内容。我们提出了基于BERT的系统Jarvisqa，可回答有关学术知识图的表格观点的问题。这些表可以在学术文献中的多种形状中找到（例如，调查，比较或结果）。我们的系统可以将直接答案检索到文章中的表格数据中提出的各种不同问题。此外，我们提供了相关表和相应的自然语言问题的初步数据集。该数据集用作我们系统的基准，其他人可以重复使用。此外，与其他基线相比，在两个数据集上评估了JARVISQA，并且与相关方法相比，性能的两到三倍改进。
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.
