# Graphene: Semantically-Linked Propositions in Open Information Extraction

arXiv: https://arxiv.org/abs/1807.11276 · HTML: https://mlsanity.com/paper/1807.11276/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2018.

## Built on by (most cited first, 8 of 8)

- [Refined Commonsense Knowledge from Large-Scale Web Contents](https://mlsanity.com/paper/2112.04596/) (2021)
- [Automated Construction of Theme-specific Knowledge Graphs](https://mlsanity.com/paper/2404.19146/) (2024)
- [DocOIE: A Document-level Context-Aware Dataset for OpenIE](https://mlsanity.com/paper/2105.04271/) (2021)
- [AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension](https://mlsanity.com/paper/2203.08992/) (2022)
- [Better Call the Plumber: Orchestrating Dynamic Information Extraction Pipelines](https://mlsanity.com/paper/2102.10966/) (2021)
- [Discourse-Aware Text Simplification: From Complex Sentences to Linked Propositions](https://mlsanity.com/paper/2308.00425/) (2023)
- [Rules still work for Open Information Extraction](https://mlsanity.com/paper/2403.10758/) (2024)
- [Enoki: Efficient Multi-Level Hallucination Detection](https://mlsanity.com/paper/2609.00581/) (2026)

## Abstract (arXiv)

We present an Open Information Extraction (IE) approach that uses a two-layered transformation stage consisting of a clausal disembedding layer and a phrasal disembedding layer, together with rhetorical relation identification. In that way, we convert sentences that present a complex linguistic structure into simplified, syntactically sound sentences, from which we can extract propositions that are represented in a two-layered hierarchy in the form of core relational tuples and accompanying contextual information which are semantically linked via rhetorical relations. In a comparative evaluation, we demonstrate that our reference implementation Graphene outperforms state-of-the-art Open IE systems in the construction of correct n-ary predicate-argument structures. Moreover, we show that existing Open IE approaches can benefit from the transformation process of our framework.