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We propose a pipeline for identifying important entities from intelligencereports that constructs a knowledge graph, where nodes correspond to entitiesof fine-grained types (e.g. traffickers) extracted from the text and edgescorrespond to extracted relations between entities (e.g. cartel membership).The important entities in intelligence reports then map to central nodes in theknowledge graph. We introduce a novel method that extracts fine-grainedentities in a few-shot setting (few labeled examples), given limited resourcesavailable to label the frequently changing entity types that intelligenceanalysts are interested in. It outperforms other state-of-the-art methods.Next, we identify challenges facing previous evaluations of zero-shot (nolabeled examples) methods for extracting relations, affecting the step ofpopulating edges. Finally, we explore the utility of the pipeline: given thegoal of identifying important entities, we evaluate the impact of relationextraction errors on the identification of central nodes in several real andsynthetic networks. The impact of these errors varies significantly by graphtopology, suggesting that confidence in measurements based on automaticallyextracted relations should depend on observed network features.

From low resource information extraction to identifying influential nodes in knowledge graphs