# Logician: A Unified End-to-End Neural Approach for Open-Domain Information Extraction

arXiv: https://arxiv.org/abs/1904.12535 · HTML: https://mlsanity.com/paper/1904.12535/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2019.

## Built on by (most cited first, 15 of 15)

- [A Survey on Neural Open Information Extraction: Current Status and Future Directions](https://mlsanity.com/paper/2205.11725/) (2022)
- [Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation](https://mlsanity.com/paper/2109.01048/) (2021)
- [A Survey on Open Information Extraction from Rule-based Model to Large Language Model](https://mlsanity.com/paper/2208.08690/) (2022)
- [Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction](https://mlsanity.com/paper/2305.13981/) (2023)
- [milIE: Modular & Iterative Multilingual Open Information Extraction](https://mlsanity.com/paper/2110.08144/) (2021)
- [Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration](https://mlsanity.com/paper/2207.06717/) (2022)
- [Syntactically Robust Training on Partially-Observed Data for Open Information Extraction](https://mlsanity.com/paper/2301.06841/) (2023)
- [SpaceE: Knowledge Graph Embedding by Relational Linear Transformation in the Entity Space](https://mlsanity.com/paper/2204.10245/) (2022)
- [Denoising Enhanced Distantly Supervised Ultrafine Entity Typing](https://mlsanity.com/paper/2210.09599/) (2022)
- [Mastering the Task of Open Information Extraction with Large Language Models and Consistent Reasoning Environment](https://mlsanity.com/paper/2310.10590/) (2023)
- [Exploiting Duality in Open Information Extraction with Predicate Prompt](https://mlsanity.com/paper/2401.11107/) (2024)
- [NL2GDPR: Automatically Develop GDPR Compliant Android Application Features from Natural Language](https://mlsanity.com/paper/2208.13361/) (2022)
- [Towards Generalized Open Information Extraction](https://mlsanity.com/paper/2211.15987/) (2022)
- [Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features](https://mlsanity.com/paper/2409.04009/) (2024)
- [Challenges in Expanding Portuguese Resources: A View from Open Information Extraction](https://mlsanity.com/paper/2501.11851/) (2025)

## Abstract (arXiv)

In this paper, we consider the problem of open information extraction (OIE) for extracting entity and relation level intermediate structures from sentences in open-domain. We focus on four types of valuable intermediate structures (Relation, Attribute, Description, and Concept), and propose a unified knowledge expression form, SAOKE, to express them. We publicly release a data set which contains more than forty thousand sentences and the corresponding facts in the SAOKE format labeled by crowd-sourcing. To our knowledge, this is the largest publicly available human labeled data set for open information extraction tasks. Using this labeled SAOKE data set, we train an end-to-end neural model using the sequenceto-sequence paradigm, called Logician, to transform sentences into facts. For each sentence, different to existing algorithms which generally focus on extracting each single fact without concerning other possible facts, Logician performs a global optimization over all possible involved facts, in which facts not only compete with each other to attract the attention of words, but also cooperate to share words. An experimental study on various types of open domain relation extraction tasks reveals the consistent superiority of Logician to other states-of-the-art algorithms. The experiments verify the reasonableness of SAOKE format, the valuableness of SAOKE data set, the effectiveness of the proposed Logician model, and the feasibility of the methodology to apply end-to-end learning paradigm on supervised data sets for the challenging tasks of open information extraction.