Papers with Open Information Extraction
PENELOPIE: Enabling Open Information Extraction for the Greek Language through Machine Translation (2021.eacl-srw)
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| Challenge: | a new method for information extraction from Greek corpora is being developed for low-resource languages. |
| Approach: | They propose a methodology that aims at bridging the gap between high and low-resource languages in the context of Open Information Extraction. |
| Outcome: | The proposed method outperforms the current state-of-the-art for the Greek language on benchmark datasets. |
AnnIE: An Annotation Platform for Constructing Complete Open Information Extraction Benchmark (2022.acl-demo)
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Niklas Friedrich, Kiril Gashteovski, Mingying Yu, Bhushan Kotnis, Carolin Lawrence, Mathias Niepert, Goran Glavaš
| Challenge: | Open Information Extraction (OIE) is the task of extracting facts from sentences in the form of relations and their corresponding arguments in schema-free manner. |
| Approach: | They propose an interactive annotation platform that facilitates annotating complete facts from input sentences. |
| Outcome: | The proposed platform facilitates such challenging annotation tasks and supports creation of fact-oriented OIE evaluation benchmarks. |
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. |
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. |
Neural Open Information Extraction (P18-2)
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| Challenge: | Existing Open IE systems are built on hand-crafted patterns from syntactic parsing, yet they face errors in propagation and compounding at each stage. |
| Approach: | They propose a neural Open IE approach with an encoder-decoder framework . they propose to learn highly confident arguments and relation tuples bootstrapped from a state-of-the-art Open ie system. |
| Outcome: | The proposed approach outperforms baseline methods while maintaining comparable computational efficiency. |
Supervised Open Information Extraction (N18-1)
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| Challenge: | Existing methods for Open Information Extraction (Open IE) use semisupervised approaches or rule-based algorithms. |
| Approach: | They propose a supervised approach to Open Information Extraction (Open IE) they build on recent deep Semantic Role Labeling models to extract Open IE tuples . |
| Outcome: | The proposed model outperforms state-of-the-art Open IE systems on benchmark datasets. |
Open Information Extraction with Entity Focused Constraints (2023.findings-eacl)
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| Challenge: | Open Information Extraction (OIE) is the task of extracting tuples from unstructured corpora without any knowledge of the type and lexical form of the subject, the object, or the subject. |
| Approach: | They exploit domain knowledge to inject constraints into the extraction through constrained inference and constraint-aware training. |
| Outcome: | The proposed approach improves the CaRB and WIRe57 metric and achieves a 29.17% improvement in the CARB and 24.37% improvement on the WIRe56 metric. |
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. |
Thesis Proposal: A Normalization-First Framework for Sound, Complete, and Utility-Ready Open Information Extraction (2026.acl-srw)
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| Challenge: | Existing approaches to extract relational tuples from text are incomplete and ambiguous . Existing methods rely on predefined schemas to produce t-uples . |
| Approach: | They propose a normalization-first framework that reframes OIE as a structured semantic transformation pipeline . they formalize soundness, completeness, and usefulness as approximate yet verifiable guarantees over extraction quality . |
| Outcome: | The proposed framework aims to make OIE usable for downstream reasoning and machine interpretability. |
A Predicate-Function-Argument Annotation of Natural Language for Open-Domain Information eXpression (2020.emnlp-main)
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| Challenge: | Existing OIE (Open Information Extraction) algorithms are redundant and not reusable. |
| Approach: | They propose a pipeline where an Open-domain Information eXpression task provides a platform for all OIE strategies. |
| Outcome: | The proposed pipeline provides a platform for all OIE strategies. |
Graphene: Semantically-Linked Propositions in Open Information Extraction (C18-1)
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| Challenge: | Existing Open IE systems focus on identifying and extracting relations of interest, but this manual labor scales linearly with the number of target relations. |
| Approach: | They propose an Open Information Extraction approach that uses a two-layered transformation stage and rhetorical relation identification to transform sentences into syntactically sound sentences. |
| Outcome: | The proposed approach outperforms state-of-the-art Open IE systems in the construction of correct n-ary predicate-argument structures. |
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. |
Event-Driven Learning of Systematic Behaviours in Stock Markets (2020.findings-emnlp)
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| Challenge: | Using financial news, we can predict stock market behaviours by extracting financial events from the news and ranking the importance of the events. |
| Approach: | They propose to combine open information extraction and neural co-reference resolution to extract financial events from news streams and extend hierarchical attention networks that include attentions on event, news and temporal levels. |
| Outcome: | The proposed method achieves significantly better accuracies and higher simulated annualized returns than state-of-the-art models when being applied to predicting Standard&Poor 500, Dow Jones, Nasdaq indices and 10 individual stocks. |
LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction (2021.eacl-main)
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| Challenge: | Open Information Extraction (OIE) systems extract factual propositions into n-ary tuples . current datasets are limited in size and diversity . |
| Approach: | They propose to convert QA-SRL 2.0 dataset to large-scale OIE dataset LSOIE. |
| Outcome: | The proposed dataset is 20 times larger than the next largest human-annotated OIE 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. |
Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain (D18-1)
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| Challenge: | Experimental results reveal dual structure between OIE and OIN tasks helps to build better OIE agents and OINE agents. |
| Approach: | They propose an Open-Domain Information Narration task as the reverse task of Open Information Extraction (OIE) they then propose an OIN task as an OIE agent and an OIR agent to implement the dual structure . |
| Outcome: | The proposed task is the reverse task of Open Information Extraction (OIE) The proposed system is able to implement the dual structure with a reinforcement learning paradigm. |
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 . |
Guide the Many-to-One Assignment: Open Information Extraction via IoU-aware Optimal Transport (2023.acl-long)
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Kaiwen Wei, Yiran Yang, Li Jin, Xian Sun, Zequn Zhang, Jingyuan Zhang, Xiao Li, Linhao Zhang, Jintao Liu, Guo Zhi
| Challenge: | Open Information Extraction (OIE) aims to extract structured information from text without the limitations of close ontology. |
| Approach: | They propose a method to assign ground truth labels to parallelly generated tuple proposals . they leverage intersection-over-union (IoU) as assignment quality measurement . |
| Outcome: | The proposed method outperforms the state-of-the-art models on three benchmarks. |
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. |
Open Information Extraction on Scientific Text: An Evaluation (C18-1)
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| Challenge: | Open Information Extraction (OIE) is the unsupervised creation of structured information from text. |
| Approach: | They propose to use two state-of-the-art OIE systems to evaluate the performance of OIE on scientific texts originating from 10 different disciplines. |
| Outcome: | The proposed methods perform significantly worse on scientific text than encyclopedic text. |
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. |
A Survey on Open Information Extraction (C18-1)
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| Challenge: | Existing approaches to open information extraction (Open IE) focus on narrow, well-defined requests over a predefined set of target relations on small, homogeneous corpora. |
| Approach: | They propose to use unsupervised methods to extract all types of relations found in text . they propose to implement a system that can be automated to detect possible relations . |
| Outcome: | The proposed approaches have been compared with existing methods and are based on the results of a literature review. |
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. |
OIE@OIA: an Adaptable and Efficient Open Information Extraction Framework (2022.acl-long)
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| Challenge: | Different Open Information Extraction (OIE) tasks require different types of information. |
| Approach: | They propose to adapt an OIE Graph to different OIE tasks with simple rules . they implement an end-to-end OIA generator and make it open-accessible . |
| Outcome: | The proposed system achieves new SOTA performance on three popular OIE tasks. |
Linking Surface Facts to Large-Scale Knowledge Graphs (2023.emnlp-main)
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| Challenge: | Open Information Extraction (OIE) methods extract facts in the form of triples . ambiguity of these triples hinders their downstream usage . |
| Approach: | They propose a benchmark that measures fact linking performance on a granular triple slot level . they propose to use a system that can detect out-of-KG entities and predicates . |
| Outcome: | The proposed benchmark can measure fact linking performance on a granular triple slot level while also measuring if a system can recognize that a surface form has no match in the existing KG. |
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. |
BenchIE^FL: A Manually Re-Annotated Fact-Based Open Information Extraction Benchmark (2024.findings-acl)
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| Challenge: | Numerous OIE systems claim ever-increasing performance, limiting their effectiveness. |
| Approach: | They propose a benchmark which fully enforces the principles of BenchIE while containing fewer errors, omissions and shortcomings when candidate facts are matched towards reference ones. |
| Outcome: | The proposed benchmark aims to improve the performance of OIE extractors while avoiding errors and omissions. |
IMoJIE: Iterative Memory-Based Joint Open Information Extraction (2020.acl-main)
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| Challenge: | Recent neural OpenIE systems are statistical or rule-based for Open Information Extraction. |
| Approach: | They propose an extension to CopyAttention that produces the next extraction conditioned on all previously extracted tuples. |
| Outcome: | The proposed model outperforms CopyAttention by 18 pts and a BERT-based strong baseline by 2 ptes. |
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. |
CaRB: A Crowdsourced Benchmark for Open IE (D19-1)
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| Challenge: | Open Information Extraction (Open IE) systems have been evaluated traditionally via manual annotation. |
| Approach: | They propose to use a dataset to score Open IE systems by matching system predictions with benchmark datasets. |
| Outcome: | The proposed framework matches predictions with the benchmark dataset and is noisy and inconsistent. |
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. |
From Speculation Detection to Trustworthy Relational Tuples in Information Extraction (2023.findings-emnlp)
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| Challenge: | Existing studies on speculation detection are defined at sentence level, but not all factual tuples extracted from a sentence are speculative. |
| Approach: | They propose to study speculations in OIE tuples and determine whether a tample is speculative. |
| Outcome: | The proposed model is based on the LSOIE dataset and provides labels for speculative tuples. |
Leveraging Linguistically Enhanced Embeddings for Open Information Extraction (2024.lrec-main)
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| Challenge: | Open Information Extraction (OIE) is a structure prediction task in NLP that aims to extract structured n-ary tuples from free text. |
| Approach: | They propose to leverage linguistic features with a Seq2Seq PLM for OIE to improve performance. |
| Outcome: | The proposed methods give any neural OIE architecture the key performance boost from both PLMs and linguistic features in one go. |
Open Information Extraction via Chunks (2023.emnlp-main)
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| Challenge: | Existing OIE systems split a sentence into tokens and recognize token spans as tuple relations and arguments. |
| Approach: | They propose to split a sentence into tokens and recognize token spans as tuple relations and arguments. |
| Outcome: | The proposed model achieves state-of-the-art on multiple OIE datasets showing that SaC has better properties than sentence as token sequence. |