  In the last decade, a large number of Knowledge Graph (KG) information
extraction approaches were proposed. Albeit effective, these efforts are
disjoint, and their collective strengths and weaknesses in effective KG
information extraction (IE) have not been studied in the literature. We propose
Plumber, the first framework that brings together the research community's
disjoint IE efforts. The Plumber architecture comprises 33 reusable components
for various KG information extraction subtasks, such as coreference resolution,
entity linking, and relation extraction. Using these components,Plumber
dynamically generates suitable information extraction pipelines and offers
overall 264 distinct pipelines.We study the optimization problem of choosing
suitable pipelines based on input sentences. To do so, we train a
transformer-based classification model that extracts contextual embeddings from
the input and finds an appropriate pipeline. We study the efficacy of Plumber
for extracting the KG triples using standard datasets over two KGs: DBpedia,
and Open Research Knowledge Graph (ORKG). Our results demonstrate the
effectiveness of Plumber in dynamically generating KG information extraction
pipelines,outperforming all baselines agnostics of the underlying KG.
Furthermore,we provide an analysis of collective failure cases, study the
similarities and synergies among integrated components, and discuss their
limitations.
