Information Extraction (IE) tasks are commonly studied topics in various domains of research. Hence, the community continuously produces multiple techniques, solutions, and tools to perform such tasks. However, running those tools and integrating them within existing infrastructure requires time, expertise, and resources. One pertinent task here is triples extraction and linking, where structured triples are extracted from a text and aligned to an existing Knowledge Graph (KG). In this paper, we present PLUMBER, the first framework that allows users to manually and automatically create suitable IE pipelines from a community-created pool of tools to perform triple extraction and alignment on unstructured text. Our approach provides an interactive medium to alter the pipelines and perform IE tasks. A short video to show the working of the framework for different use-cases is available online under: https://www.youtube.com/watch?v=XC9rJNIUv8g
@article{arxiv.2206.01442,
title = {Plumber: A Modular Framework to Create Information Extraction Pipelines},
author = {Mohamad Yaser Jaradeh and Kuldeep Singh and Markus Stocker and Sören Auer},
journal= {arXiv preprint arXiv:2206.01442},
year = {2022}
}
Comments
pre-print for WWW'21 demo of ICWE PLUMBER publication