Plumber: A Modular Framework to Create Information Extraction Pipelines
This work addresses the problem of resource-intensive tool integration for researchers and practitioners in information extraction, though it is incremental as it builds on existing community tools.
The authors tackled the challenge of integrating diverse information extraction tools for triple extraction and linking by introducing PLUMBER, a modular framework that enables manual and automatic pipeline creation, resulting in a system that simplifies tool integration and pipeline customization for users.
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