Question Answering on Scholarly Knowledge Graphs
Mohamad Yaser Jaradeh, Markus Stocker, S\"oren Auer

TL;DR
This paper introduces JarvisQA, a BERT-based system designed to answer questions on scholarly knowledge graphs represented as tables, improving retrieval accuracy over existing methods and providing a reusable benchmark dataset.
Contribution
The paper presents JarvisQA, a novel BERT-based question answering system for scholarly tables, along with a new benchmark dataset for evaluating such systems.
Findings
JarvisQA outperforms baselines by 2-3 times in accuracy.
A new dataset of scholarly tables and questions is introduced.
The system effectively retrieves answers from diverse table formats.
Abstract
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…
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Taxonomy
MethodsLinear Layer · Weight Decay · Softmax · Adam · Multi-Head Attention · Dropout · Refunds@Expedia|||How do I get a full refund from Expedia? · Attention Dropout · Linear Warmup With Linear Decay · Dense Connections
