  The number of published research papers has experienced exponential growth in
recent years, which makes it crucial to develop new methods for efficient and
versatile information extraction and knowledge discovery. To address this need,
we propose a Semantic Knowledge Graph (SKG) that integrates semantic concepts
from abstracts and other meta-information to represent the corpus. The SKG can
support various semantic queries in academic literature thanks to the high
diversity and rich information content stored within. To extract knowledge from
unstructured text, we develop a Knowledge Extraction Module that includes a
semi-supervised pipeline for entity extraction and entity normalization. We
also create an ontology to integrate the concepts with other meta information,
enabling us to build the SKG. Furthermore, we design and develop a dataflow
system that demonstrates how to conduct various semantic queries flexibly and
interactively over the SKG. To demonstrate the effectiveness of our approach,
we conduct the research based on the visualization literature and provide
real-world use cases to show the usefulness of the SKG.
  The dataset and codes for this work are available at
https://osf.io/aqv8p/?view_only=2c26b36e3e3941ce999df47e4616207f.

Information Retrieval;Artificial Intelligence;Graphics;Human-Computer Interaction