Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

The necessity for scholarly knowledge mining and management has grown significantly as
academic literature and its linkages to authors produce enormously. Information extraction …

Science communication has a number of bottlenecks that include the rising number of
published research papers and its non-machine-accessible and document-based paradigm …

This paper presents a new challenging information extraction task in the domain of materials
science. We develop an annotation scheme for marking information on experiments related …

In recent years, we saw the emergence of several approaches for producing machine-
readable, semantically rich, interlinked description of the content of research publications …

Generative commonsense reasoning (GCR) in natural language is to reason about the
commonsense while generating coherent text. Recent years have seen a surge of interest in …

Graph convolutional networks (GCNs) are a powerful class of graph neural networks.
Trained in a semi-supervised end-to-end fashion, GCNs can learn to integrate node features …

Automatic construction of a taxonomy supports many applications in e-commerce, web
search, and question answering. Existing taxonomy expansion or completion methods …

Knowledge-based biomedical data science involves the design and implementation of
computer systems that act as if they knew about biomedicine. Such systems depend on …

This paper presents an overview of the Text2DT shared task 1 held in the CHIP-2022 shared
tasks. The shared task addresses the challenging topic of automatically extracting the …