# Better Call the Plumber: Orchestrating Dynamic Information Extraction Pipelines

arXiv: https://arxiv.org/abs/2102.10966 · HTML: https://mlsanity.com/paper/2102.10966/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 43 references that are in the map.
- First posted on arXiv 22 Feb 2021.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [KG-BERT: BERT for Knowledge Graph Completion](https://mlsanity.com/paper/1909.03193/) (2019)
- [Deep Reinforcement Learning for Mention-Ranking Coreference Models](https://mlsanity.com/paper/1609.08667/) (2016)
- [A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks](https://mlsanity.com/paper/1811.06031/) (2018)
- [A Survey on Open Information Extraction](https://mlsanity.com/paper/1806.05599/) (2018)
- [Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction](https://mlsanity.com/paper/1906.09317/) (2019)
- [RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network](https://mlsanity.com/paper/2009.08694/) (2020)
- [Falcon 2.0: An Entity and Relation Linking Tool over Wikidata](https://mlsanity.com/paper/1912.11270/) (2019)
- [Analysis of Named Entity Recognition and Linking for Tweets](https://mlsanity.com/paper/1410.7182/) (2014)
- [EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs](https://mlsanity.com/paper/1801.03825/) (2018)
- [Learning Dynamic Context Augmentation for Global Entity Linking](https://mlsanity.com/paper/1909.02117/) (2019)
- [OpenTapioca: Lightweight Entity Linking for Wikidata](https://mlsanity.com/paper/1904.09131/) (2019)
- [Graphene: Semantically-Linked Propositions in Open Information Extraction](https://mlsanity.com/paper/1807.11276/) (2018)
- [Seq2RDF: An end-to-end application for deriving Triples from Natural Language Text](https://mlsanity.com/paper/1807.01763/) (2018)
- [Leveraging Semantic Parsing for Relation Linking over Knowledge Bases](https://mlsanity.com/paper/2009.07726/) (2020)

## Built on by (most cited first, 4 of 4)

- [CoPERLex: Content Planning with Event-based Representations for Legal Case Summarization](https://mlsanity.com/paper/2501.14112/) (2025)
- [Procedural Text Mining with Large Language Models](https://mlsanity.com/paper/2310.03376/) (2023)
- [KGpipe: Generation and Evaluation of Pipelines for Data Integration into Knowledge Graphs](https://mlsanity.com/paper/2511.18364/) (2025)
- [Plumber: A Modular Framework to Create Information Extraction Pipelines](https://mlsanity.com/paper/2206.01442/) (2022)

## Abstract (arXiv)

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.