Papers with OpenIE
LED: A Dataset for Life Event Extraction from Dialogs (2023.findings-eacl)
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| Challenge: | Existing methods for extracting life events from conversations are limited. |
| Approach: | They propose a dataset containing fine-grained life event annotations on conversational data. |
| Outcome: | The proposed dataset combines three information extraction frameworks to extract life events from conversations. |
CaRe: Open Knowledge Graph Embeddings (D19-1)
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| Challenge: | Existing methods for generating Open Knowledge Graphs have been criticized for not achieving canonicalization of OpenKGs. |
| Approach: | They propose to use Open Information Extraction methods to extract triples from text . they propose to learn embeddings of NPs and RPs present in the graph . |
| Outcome: | The proposed methods improve OpenKG embeddings and bootstrap OpenKGs from text corpus. |
OpenFact: Factuality Enhanced Open Knowledge Extraction (2023.tacl-1)
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Linfeng Song, Ante Wang, Xiaoman Pan, Hongming Zhang, Dian Yu, Lifeng Jin, Haitao Mi, Jinsong Su, Yue Zhang, Dong Yu
| Challenge: | Existing OIE systems organize knowledge into subject-relation-object (SRO) triplets, and they use templates to extract such knowledge triplet. |
| Approach: | They propose a framework to handle expressiveness and groundedness in OpenFact . they propose to use templates, extra constraints, and adopt human efforts to ensure that most triplets contain enough details. |
| Outcome: | The proposed framework improves expressiveness and groundedness of OpenFact . it is more accurate and denser than OPIEC-Linked, which is grounded to Wikidata . |
When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications (2023.acl-long)
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| Challenge: | Existing consensus on which OpenIE model is best for each application is lacking . different assumptions made by different models and datasets have a statistically significant effect on performance, making it important to choose the most appropriate OpenIE system for one’s applications. |
| Approach: | They propose to use OpenIE to extract relation tuples from plain text to compare different models and training sets to find the best model for their applications. |
| Outcome: | The proposed models perform well on a Complex QA application. |
CompactIE: Compact Facts in Open Information Extraction (2022.naacl-main)
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| Challenge: | Despite advances in open information extraction, many systems focus on covering more information over compactness of constituents. |
| Approach: | They propose a neural OpenIE system that produces compact extractions with overlapping constituents by using a pipelined approach. |
| Outcome: | The proposed system produces 1.5x-2x more compact extractions than previous systems, with high precision, establishing a new state-of-the-art in OpenIE. |
OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference (N19-1)
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| Challenge: | Existing methods for knowledge extraction and alignment are limited in quality and performance. |
| Approach: | They propose to integrate OpenIE extractions in the form of (subject, predicate, object) triples with Knowledge Bases (KB) |
| Outcome: | The proposed method improves state-of-the-art for OpenIE extractions and boosts performance on OpenIE from semi-structured data. |
Towards Generalized Open Information Extraction (2022.findings-emnlp)
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| Challenge: | Open Information Extraction (OpenIE) models are evaluated on in-domain test sets aside from the training corpus, which violates the initial task principle of domain-independence. |
| Approach: | They propose to generalize OpenIE over unseen target domains with different data distributions from source training domains. |
| Outcome: | The proposed method beats the previous methods in both in- and out-of-domain settings by 6.0% in F1 score absolutely. |
Alignment-Augmented Consistent Translation for Multilingual Open Information Extraction (2022.acl-long)
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| Challenge: | Existing training data in English is limited to supervised Open Information Extraction (OpenIE) . |
| Approach: | They propose a model that automatically converts English sentences into other languages by using Alignment-Augmented Constrained Translation (AACTrans) they train a generative OpenIE model that outputs for each sentence relations in the first stage and all extractions containing the relation in the second stage. |
| Outcome: | The proposed model outperforms existing models on Spanish, Portuguese, Chinese, Hindi and Telugu on 5 languages. |
DocOIE: A Document-level Context-Aware Dataset for OpenIE (2021.findings-acl)
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| Challenge: | Existing solutions focus on extracting tuples at sentence level, but sentences exist as part of a document rather than standalone. |
| Approach: | They propose to annotate 800 sentences from 80 documents to form a DocOIE dataset . they propose to use document-level context to improve OpenIE performance . |
| Outcome: | The proposed OpenIE model improves performance by incorporating documentlevel context into the dataset. |
CycleOIE: A Low-Resource Training Framework For Open Information Extraction (2025.coling-main)
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Zhihong Jin, Chunhong Zhang, Zheng Hu, Jibin Yu, Ruiqi Ma, Qingyun Chen, Xiaohao Liao, Yanxing Zhang
| Challenge: | Open Information Extraction (OpenIE) models rely heavily on large amounts of annotated data. |
| Approach: | They propose a training framework that maximizes data efficiency through a cycle-consistency mechanism. |
| Outcome: | The proposed approach improves the quality of training data by curating low-quality datasets annotated by a large language model. |
Open Information Extraction from Question-Answer Pairs (N19-1)
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| Challenge: | Existing work on OpenIE extracts structured data from sentences . a system for extracting tuples from question-answer pairs solves this problem . |
| Approach: | They propose a system for extracting tuples from question-answer pairs . they use distributed representations of a question and an answer to generate knowledge facts . |
| Outcome: | The proposed system extracts meaningful structured tuples from question-answer pairs . it can find new and interesting facts to extend knowledge bases, the authors show . |
Integrating Lexical Information into Entity Neighbourhood Representations for Relation Prediction (2021.naacl-main)
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| Challenge: | Existing methods to predict knowledge base relations are limited by maintenance costs and text-based formats. |
| Approach: | They propose a system that can extend relational database tables with information extracted from a document corpus. |
| Outcome: | The proposed system outperforms existing methods by incorporating embeddings of text-based representations of the entities and relations. |
Syntactic Multi-view Learning for Open Information Extraction (2022.emnlp-main)
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| Challenge: | Open Information Extraction (OpenIE) aims to generate structured tuples from unstructured open-domain text. |
| Approach: | They propose to model constituency and dependency trees into word-level graphs and combine them with sentential semantic representations to extract relational tuples. |
| Outcome: | The proposed model integrates constituency and dependency trees into word-level graphs and enables neural OpenIE to learn from syntactic structures. |
Towards Data-driven Ontologies: a Filtering Approach using Keywords and Natural Language Constructs (2020.lrec-1)
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| Challenge: | Creating ontologies is an expensive task. |
| Approach: | They evaluate two commonly used methods, OpenIE and co-occurrences, and use them to generate ontologies from documents. |
| Outcome: | The proposed methods perform better on pizza and agriculture document sets than OpenIE and co-occurrences. |
OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extraction (2020.emnlp-main)
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| Challenge: | OpenIE generates extractions iteratively, requiring repeated encoding of partial outputs. |
| Approach: | They propose an iterative open information extraction system that generates extractions iterativly, requiring repeated encoding of partial outputs. |
| Outcome: | The proposed system beats the previous systems by as much as 4 pts in F1 while being much faster. |
OpenCeres: When Open Information Extraction Meets the Semi-Structured Web (N19-1)
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| Challenge: | Open Information Extraction (OpenIE) is a problem of extracting triples from natural language text whose predicate relations are not aligned to any pre-defined ontology. |
| Approach: | They propose an open-source method to extract triples from semi-structured websites . they use a semi-supervised label propagation technique to create training data for relations . |
| Outcome: | The proposed method extracts over 2 million triples from 31 websites in the movie vertical. |
Abstractive Open Information Extraction (2023.emnlp-main)
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| Challenge: | Existing OpenIE datasets and metrics are ill-suited for this task. |
| Approach: | They propose a new open-domain task that extends OpenIE to include inferred relations . they propose metric to evaluate the effectiveness of open-source OpenIE . |
| Outcome: | The proposed model can extract inferred relations from the extracted relation tuples. |
MILIE: Modular & Iterative Multilingual Open Information Extraction (2022.acl-long)
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Bhushan Kotnis, Kiril Gashteovski, Daniel Rubio, Ammar Shaker, Vanesa Rodriguez-Tembras, Makoto Takamoto, Mathias Niepert, Carolin Lawrence
| Challenge: | Current OpenIE systems extract all triple slots independently. |
| Approach: | They propose a neural OpenIE system that extracts triple slots iteratively . they propose to use the system to extract easy slots and difficult ones . |
| Outcome: | The proposed system outperforms SOTA systems on multiple languages ranging from Chinese to Arabic. |
A Survey on Open Information Extraction from Rule-based Model to Large Language Model (2024.findings-emnlp)
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Liu Pai, Wenyang Gao, Wenjie Dong, Lin Ai, Ziwei Gong, Songfang Huang, Li Zongsheng, Ehsan Hoque, Julia Hirschberg, Yue Zhang
| Challenge: | Open Information Extraction (OpenIE) is a key NLP task aimed at extracting structured information from unstructured text sources. |
| Approach: | They propose to categorize OpenIE into rule-based, neural, and pre-trained large language models and discuss each within a chronological framework. |
| Outcome: | The paper categorizes OpenIE approaches into rule-based, neural, and pre-trained large language models, discussing each within a chronological framework. |
The Search for Conflicts of Interest: Open Information Extraction in Scientific Publications (2025.findings-emnlp)
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| Challenge: | public repositories of relations of interest are increasingly recommended or mandated in various domains to avoid COIs. |
| Approach: | They propose an LLM-based open information extraction framework for extracting financial or other types of interesting relations from scientific text. |
| Outcome: | The proposed framework extracts financial or other types of interesting relations from scientific text. |
Maximal Clique Based Non-Autoregressive Open Information Extraction (2021.emnlp-main)
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| Challenge: | Open Information Extraction (OpenIE) aims to discover textual facts from a given sentence. |
| Approach: | They propose a non-autoregressive framework that generates a fact graph and a graph with an edge linking two nodes that belong to the same fact. |
| Outcome: | The proposed framework outperforms current state-of-the-art methods on two benchmark datasets and significantly outperformed the existing ones. |
Efficient Data Learning for Open Information Extraction with Pre-trained Language Models (2023.findings-emnlp)
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| Challenge: | Experimental results indicate that, compared to previous SOTA methods, OK-IE requires only 1/100 of the training data (900 instances) and 1/120 of the time (3 minutes) to achieve comparable results. |
| Approach: | They propose a framework that transforms OpenIE into the pre-training task form of the T5 model, thereby reducing the need for extensive training data. |
| Outcome: | The proposed framework transforms OpenIE into the pre-training task form of the T5 model, reducing the need for extensive training data and significantly reducing training time. |
Lingua-Graph: A Unified Representation of Cross-Task Common Substructures for Analytic Language Processing (2026.acl-long)
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| Challenge: | Existing structural-analytic tasks are fragmented by inconsistent task requirements . we propose a solution for the representation layer, called Lingua-Graph . |
| Approach: | They propose a representation-then-decision paradigm for structural-analytic tasks . they propose Graph-based representations that capture entities, facts, and relations . |
| Outcome: | The proposed model improves interpretability and higher hostability of entities, facts, and relations . the proposed model is available on github.com/rudaoshi/Lingua . |