Better Call the Plumber: Orchestrating Dynamic Information Extraction Pipelines
Mohamad Yaser Jaradeh, Kuldeep Singh, Markus Stocker, Andreas Both,, S\"oren Auer

TL;DR
This paper introduces Plumber, a comprehensive framework that dynamically constructs and optimizes information extraction pipelines for Knowledge Graphs, integrating multiple subtasks and outperforming existing methods on standard datasets.
Contribution
Plumber is the first framework to unify disjoint IE approaches into a dynamic, component-based pipeline system with an optimization model for pipeline selection.
Findings
Outperforms baseline methods on KG triple extraction tasks
Demonstrates effective pipeline generation for DBpedia and ORKG datasets
Provides insights into component interactions and failure cases
Abstract
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…
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