  Compared to the general news domain, information extraction (IE) from
biomedical text requires much broader domain knowledge. However, many previous
IE methods do not utilize any external knowledge during inference. Due to the
exponential growth of biomedical publications, models that do not go beyond
their fixed set of parameters will likely fall behind. Inspired by how humans
look up relevant information to comprehend a scientific text, we present a
novel framework that utilizes external knowledge for joint entity and relation
extraction named KECI (Knowledge-Enhanced Collective Inference). Given an input
text, KECI first constructs an initial span graph representing its initial
understanding of the text. It then uses an entity linker to form a knowledge
graph containing relevant background knowledge for the the entity mentions in
the text. To make the final predictions, KECI fuses the initial span graph and
the knowledge graph into a more refined graph using an attention mechanism.
KECI takes a collective approach to link mention spans to entities by
integrating global relational information into local representations using
graph convolutional networks. Our experimental results show that the framework
is highly effective, achieving new state-of-the-art results in two different
benchmark datasets: BioRelEx (binding interaction detection) and ADE (adverse
drug event extraction). For example, KECI achieves absolute improvements of
4.59% and 4.91% in F1 scores over the state-of-the-art on the BioRelEx entity
and relation extraction tasks.
