This repository serves as a comprehensive, curated collection of resources for Open Information Extraction (OIE), a field dedicated to extracting relational triples (subject, relation, object) from unstructured text without predefined schemas. It targets researchers, engineers, and practitioners in Natural Language Processing (NLP) and knowledge extraction, providing a centralized hub for seminal papers, code implementations, datasets, and surveys to accelerate advancements in the field.

How It Works

The repository organizes a vast landscape of OIE research and tools. It details the core OIE paradigm of unsupervised relation extraction and catalogs diverse methodologies, including rule-based, statistical, and neural network approaches. The resources highlight the evolution of OIE systems, their application in various downstream tasks (e.g., question answering, knowledge base population), and their adaptation for multilingual and cross-lingual scenarios.

Quick Start & Requirements

This repository is a curated list of resources and does not represent a single installable project. Therefore, direct installation or setup commands are not applicable. Users are directed to individual project links for specific setup instructions and dependencies.

Highlighted Details

• Extensive bibliography of OIE research papers, chronologically and thematically organized, spanning from 2006 to 2022.
• Links to numerous OIE systems and their codebases, including prominent examples like MinIE, ClausIE, Stanford OpenIE, OLLIE, OpenIE6, and IMoJIE.
• Access pointers to significant OIE corpora such as OPIEC (over 341 million triples) and ReVerb extractions, along with resources derived from OIE output like entailment rules and PhD theses.
• Dedicated sections for OIE in various languages (German, Chinese, Portuguese, etc.) and multilingual OIE systems, alongside resources for OIE canonicalization and supervised OIE approaches.

Maintenance & Community

No specific information regarding maintenance, contributors, or community channels (like Discord or Slack) is provided within the README.

Licensing & Compatibility

The README does not specify a license for the curated list itself, nor does it detail the licenses of the linked OIE systems or datasets. Users must consult the individual resources for licensing information and compatibility.

Limitations & Caveats

As a curated list, this repository reflects the state of OIE research up to its last update and does not offer a unified, single-click OIE solution. The quality, maintenance status, and specific technical requirements of the linked systems and datasets will vary. Users need to evaluate each resource individually for their specific needs.