  We propose a pipeline for identifying important entities from intelligence
reports that constructs a knowledge graph, where nodes correspond to entities
of fine-grained types (e.g. traffickers) extracted from the text and edges
correspond to extracted relations between entities (e.g. cartel membership).
The important entities in intelligence reports then map to central nodes in the
knowledge graph. We introduce a novel method that extracts fine-grained
entities in a few-shot setting (few labeled examples), given limited resources
available to label the frequently changing entity types that intelligence
analysts are interested in. It outperforms other state-of-the-art methods.
Next, we identify challenges facing previous evaluations of zero-shot (no
labeled examples) methods for extracting relations, affecting the step of
populating edges. Finally, we explore the utility of the pipeline: given the
goal of identifying important entities, we evaluate the impact of relation
extraction errors on the identification of central nodes in several real and
synthetic networks. The impact of these errors varies significantly by graph
topology, suggesting that confidence in measurements based on automatically
extracted relations should depend on observed network features.
