Better Call the Plumber: Orchestrating Dynamic Information Extraction Pipelines

Abstract

We propose Plumber, the first framework that brings together the research community’s disjoint information extraction (IE) efforts. The Plumber architecture comprises 33 reusable components for various Knowledge Graphs (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.