---
title: "Thesis Proposal — paper by Prakash et al. (2026)"
description: "This thesis proposal argues that the root problem is a mis-specified task rather than model architecture, and it recasts OIE as… 52 references."
authors: ["Chandan Prakash", "Pavan Kumar Chittimalli", "Arnab Bhattacharya"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)"
source: "https://aclanthology.org/2026.acl-srw.116/"
doi: "10.18653/v1/2026.acl-srw.116"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/prakash-2026-thesis-proposal-normalization-first-framework/"
---
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# Thesis Proposal: A Normalization-First Framework for Sound, Complete, and Utility-Ready Open Information Extraction

**Authors:** Chandan Prakash, Pavan Kumar Chittimalli and [Arnab Bhattacharya](https://md.research.elamur.ai/free/en/authors/arnab-bhattacharya/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-srw.116/) · [PDF](https://aclanthology.org/2026.acl-srw.116.pdf) · [DOI 10.18653/v1/2026.acl-srw.116](https://doi.org/10.18653/v1/2026.acl-srw.116) · [OpenAlex](https://openalex.org/W7166874904)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Open Information Extraction (OIE) systems, from TextRunner through rule-based, neural tagging, generative and LLM-based models, produce relational tuples that look plausible but lose logic-bearing content such as modality, negation, conditions, quantification and attribution. For example, a hedged, conditional loan-approval sentence gets reduced to a bare (system, approve, loan) fact. This thesis proposal argues that the root problem is a mis-specified task rather than model architecture, and it recasts OIE as a staged semantic transformation. First, text is rewritten without meaning loss into irreducible, declarative sentence units in active voice. Next, extraction is limited to atomic relations, unary or binary, that carry explicit semantic annotations. Finally, entities and relations are canonically aligned. Because full semantic interpretation is undecidable, soundness, completeness and usefulness are treated as approximate but checkable guarantees under Probably Approximately Correct (PAC) learning. The proposal reports no experimental results. Its contribution is a research agenda: formal foundations, an annotation schema, benchmarks, metrics and a multilingual extension. The goal is OIE output that downstream reasoning systems can rely on.

## Key points
- The pipeline has four stages aimed at knowledge base population:
  - (I) Linguistic Structuring & Context Resolution;
  - (II) Lossless Semantic Decomposition;
  - (III) Validated Relational Signal Extraction;
  - (IV) Canonical Alignment and Knowledge Consolidation.
- A standardized annotation layer covers polarity/negation, modality, scope, quantification and attribution. It extends the idea first introduced in MinIE, which current benchmarks largely ignore.
- The evaluation scheme uses four scores: Argument Identification, Relation Phrase, Complete Relation and Semantic Annotation.
  - These are combined into a weighted aggregate, with per-domain scores reported separately.
  - Completeness is approximated through semantic equivalence, sentence reconstruction and entailment consistency.
- Planned work includes:
  - new benchmark datasets;
  - a system that pairs probabilistic models with deterministic validation;
  - canonicalization tested on downstream tasks;
  - multilingual generalization.
- The authors acknowledge several limitations:
  - dependence on upstream coreference resolution and parsing;
  - added computational cost;
  - difficulty representing n-ary or event-centric facts with only unary and binary relations;
  - expensive, potentially subjective annotation.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Indian Institute of Technology Kanpur](https://md.research.elamur.ai/free/en/universities/indian-institute-of-technology-kanpur/) — University, Kanpur, India · 14 works · 619 citations
- [Tata Consultancy Services](https://md.research.elamur.ai/free/en/companies/tata-consultancy-services/) — Company, Pune, India · 8 works · 35 citations

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🧭 Topics (1): [🔎 Information extraction](https://md.research.elamur.ai/free/en/topics/information-extraction/) · 167 works

## Cite

Cite as: Chandan Prakash, Pavan Kumar Chittimalli and Arnab Bhattacharya. 2026. Thesis Proposal: A Normalization-First Framework for Sound, Complete, and Utility-Ready Open Information Extraction. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{prakash2026thesis,
  title = {{Thesis Proposal: A Normalization-First Framework for Sound, Complete, and Utility-Ready Open Information Extraction}},
  author = {Chandan Prakash, Pavan Kumar Chittimalli and Arnab Bhattacharya},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-srw.116},
  url = {https://doi.org/10.18653/v1/2026.acl-srw.116}
}
```

- [← previous Thesis Proposal: A Multi-Agent System for Ontology-Based Perspective-Aware Knowledge Extraction](https://md.research.elamur.ai/free/en/papers/miranda-2026-thesis-proposal-multi-agent-system/)
- [next → Thesis Proposal: Intentional Inference for Insight Generation](https://md.research.elamur.ai/free/en/papers/onderkova-2026-thesis-proposal-intentional-inference-insight/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/45/)

## References

52 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Alan Akbik and Alexander Löser. 2012. KrakeN: N-ary facts in open information extraction. In Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX), pages 52–56, Montréal, Canada. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Alan+Akbik+and+Alexander+L%C3%B6ser%2E+2012%2E+KrakeN%3A+N%2Dary+facts+in+open+information+extraction%2E+In+Proceedings+of+the+Joint+Workshop+on+Automatic+Knowledge+Base+Construction+and+Web%2Dscale+Knowledge+Extracti)
2. Luciano Del Corro and Rainer Gemulla. 2013. Clausie: clause-based open information extraction. In Proceedings of the 22nd International Conference on World Wide Web, WWW ’13, page 355–366, New York, NY, USA. Association for Computing Machinery. [find](https://scholar.google.com/scholar?q=Luciano+Del+Corro+and+Rainer+Gemulla%2E+2013%2E+Clausie%3A+clause%2Dbased+open+information+extraction%2E+In+Proceedings+of+the+22nd+International+Conference+on+World+Wide+Web%2C+WWW+%E2%80%9913%2C+page+355%E2%80%93366%2C+New+York%2C+N)
3. Alibaba Cloud. Alibaba cloud model studio. [find](https://scholar.google.com/scholar?q=Alibaba+Cloud%2E+Alibaba+cloud+model+studio%2E)
4. Gabor Angeli, Melvin Jose Johnson Premkumar, and Christopher D. Manning. 2015. [Leveraging linguistic structure for open domain information extraction](https://md.research.elamur.ai/free/en/papers/angeli-2015-leveraging-linguistic-structure-open-domain/). In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Lang…
5. Sangnie Bhardwaj, Samarth Aggarwal, and Mausam. 2019. CaRB: A crowdsourced benchmark for open IE. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6262– 6267, Hong… [find](https://scholar.google.com/scholar?q=Sangnie+Bhardwaj%2C+Samarth+Aggarwal%2C+and+Mausam%2E+2019%2E+CaRB%3A+A+crowdsourced+benchmark+for+open+IE%2E+In+Proceedings+of+the+2019+Conference+on+Empirical+Methods+in+Natural+Language+Processing+and+the+9th+)
6. Nikita Bhutani, H. V. Jagadish, and Dragomir Radev. 2016. Nested propositions in open information extraction. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 55–64, Austin, Texas. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Nikita+Bhutani%2C+H%2E+V%2E+Jagadish%2C+and+Dragomir+Radev%2E+2016%2E+Nested+propositions+in+open+information+extraction%2E+In+Proceedings+of+the+2016+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C)
7. Zhen Chen, Jingping Liu, Deqing Yang, Yanghua Xiao, Huimin Xu, Zongyu Wang, Rui Xie, and Yunsen Xian. 2024. Exploiting duality in open information extraction with predicate prompt. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining, WSDM ’24, page 125–133, New York… [find](https://scholar.google.com/scholar?q=Zhen+Chen%2C+Jingping+Liu%2C+Deqing+Yang%2C+Yanghua+Xiao%2C+Huimin+Xu%2C+Zongyu+Wang%2C+Rui+Xie%2C+and+Yunsen+Xian%2E+2024%2E+Exploiting+duality+in+open+information+extraction+with+predicate+prompt%2E+In+Proceedings+of+t)
8. Janara Christensen, Mausam, Stephen Soderland, and Oren Etzioni. 2011. An analysis of open information extraction based on semantic role labeling. In Proceedings of the Sixth International Conference on Knowledge Capture, K-CAP ’11, page 113–120, Kuicai Dong, Aixin Sun, Jung-Jae Kim, and Xiaoli Li.… [find](https://scholar.google.com/scholar?q=Janara+Christensen%2C+Mausam%2C+Stephen+Soderland%2C+and+Oren+Etzioni%2E+2011%2E+An+analysis+of+open+information+extraction+based+on+semantic+role+labeling%2E+In+Proceedings+of+the+Sixth+International+Conference+)
9. Kuicai Dong, Aixin Sun, Jung-jae Kim, and Xiaoli Li. 2023. Open information extraction via chunks. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15390–15404, Singapore. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Kuicai+Dong%2C+Aixin+Sun%2C+Jung%2Djae+Kim%2C+and+Xiaoli+Li%2E+2023%2E+Open+information+extraction+via+chunks%2E+In+Proceedings+of+the+2023+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pages+1539)
10. Oren Etzioni, Michele Banko, Stephen Soderland, and Daniel S. Weld. 2008. [Open information extraction from the web](https://md.research.elamur.ai/free/en/papers/banko-2007-open-information-extraction-web/). Commun. ACM, 51(12):68–74.
11. Anthony Fader, Stephen Soderland, and Oren Etzioni. 2011. Identifying relations for open information extraction. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pages 1535–1545, Edinburgh, Scotland, UK. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Anthony+Fader%2C+Stephen+Soderland%2C+and+Oren+Etzioni%2E+2011%2E+Identifying+relations+for+open+information+extraction%2E+In+Proceedings+of+the+2011+Conference+on+Empirical+Methods+in+Natural+Language+Processi)
12. Zhiyuan Fan and Shizhu He. 2023. Efficient data learning for open information extraction with pre-trained language models. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 13056–13063, Singapore. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Zhiyuan+Fan+and+Shizhu+He%2E+2023%2E+Efficient+data+learning+for+open+information+extraction+with+pre%2Dtrained+language+models%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+2023%2C+pag)
13. Farima Fatahi Bayat, Nikita Bhutani, and H. Jagadish. 2022. CompactIE: Compact facts in open information extraction. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 900–910, Seattle, United State… [find](https://scholar.google.com/scholar?q=Farima+Fatahi+Bayat%2C+Nikita+Bhutani%2C+and+H%2E+Jagadish%2E+2022%2E+CompactIE%3A+Compact+facts+in+open+information+extraction%2E+In+Proceedings+of+the+2022+Conference+of+the+North+American+Chapter+of+the+Associat)
14. Pablo Gamallo and Marcos Garcia. 2015. Multilingual open information extraction. In Portuguese conference on artificial intelligence, pages 711–722. Springer. [find](https://scholar.google.com/scholar?q=Pablo+Gamallo+and+Marcos+Garcia%2E+2015%2E+Multilingual+open+information+extraction%2E+In+Portuguese+conference+on+artificial+intelligence%2C+pages+711%E2%80%93722%2E+Springer%2E)
15. Kiril Gashteovski, Rainer Gemulla, and Luciano del Corro. 2017. MinIE: Minimizing facts in open information extraction. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2630–2640, Copenhagen, Denmark. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Kiril+Gashteovski%2C+Rainer+Gemulla%2C+and+Luciano+del+Corro%2E+2017%2E+MinIE%3A+Minimizing+facts+in+open+information+extraction%2E+In+Proceedings+of+the+2017+Conference+on+Empirical+Methods+in+Natural+Language+P)
16. Kiril Gashteovski, Mingying Yu, Bhushan Kotnis, Carolin Lawrence, Mathias Niepert, and Goran Glavaš. 2022. BenchIE: A framework for multi-faceted fact-based open information extraction evaluation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: L… [find](https://scholar.google.com/scholar?q=Kiril+Gashteovski%2C+Mingying+Yu%2C+Bhushan+Kotnis%2C+Carolin+Lawrence%2C+Mathias+Niepert%2C+and+Goran+Glava%C5%A1%2E+2022%2E+BenchIE%3A+A+framework+for+multi%2Dfaceted+fact%2Dbased+open+information+extraction+evaluation%2E+In+)
17. Ralph Grishman and Beth Sundheim. 1996. [Message Understanding Conference- 6: A brief history](https://md.research.elamur.ai/free/en/papers/grishman-1996-message-understanding-conference-6-brief/). In COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics.
18. David Haussler and Manfred Warmuth. 2018. The probably approximately correct (pac) and other learning models. The Mathematics of Generalization, pages 17–36. [find](https://scholar.google.com/scholar?q=David+Haussler+and+Manfred+Warmuth%2E+2018%2E+The+probably+approximately+correct+%28pac%29+and+other+learning+models%2E+The+Mathematics+of+Generalization%2C+pages+17%E2%80%9336%2E)
19. Keshav Kolluru, Vaibhav Adlakha, Samarth Aggarwal, Mausam, and Soumen Chakrabarti. 2020a. OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extraction. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3748–3761, On… [find](https://scholar.google.com/scholar?q=Keshav+Kolluru%2C+Vaibhav+Adlakha%2C+Samarth+Aggarwal%2C+Mausam%2C+and+Soumen+Chakrabarti%2E+2020a%2E+OpenIE6%3A+Iterative+Grid+Labeling+and+Coordination+Analysis+for+Open+Information+Extraction%2E+In+Proceedings+of+)
20. Keshav Kolluru, Samarth Aggarwal, Vipul Rathore, Mausam, and Soumen Chakrabarti. 2020b. IMo-JIE: Iterative memory-based joint open information extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5871–5886, Online. Association for Computationa… [find](https://scholar.google.com/scholar?q=Keshav+Kolluru%2C+Samarth+Aggarwal%2C+Vipul+Rathore%2C+Mausam%2C+and+Soumen+Chakrabarti%2E+2020b%2E+IMo%2DJIE%3A+Iterative+memory%2Dbased+joint+open+information+extraction%2E+In+Proceedings+of+the+58th+Annual+Meeting+of+)
21. Keshav Kolluru, Muqeeth Mohammed, Shubham Mittal, Soumen Chakrabarti, and Mausam. 2022. Alignment-augmented consistent translation for multilingual open information extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2… [find](https://scholar.google.com/scholar?q=Keshav+Kolluru%2C+Muqeeth+Mohammed%2C+Shubham+Mittal%2C+Soumen+Chakrabarti%2C+and+Mausam%2E+2022%2E+Alignment%2Daugmented+consistent+translation+for+multilingual+open+information+extraction%2E+In+Proceedings+of+the+6)
22. Bhushan Kotnis, Kiril Gashteovski, Daniel Rubio, Ammar Shaker, Vanesa Rodriguez-Tembras, Makoto Takamoto, Mathias Niepert, and Carolin Lawrence. 2022. MILIE: Modular & iterative multilingual open information extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational L… [find](https://scholar.google.com/scholar?q=Bhushan+Kotnis%2C+Kiril+Gashteovski%2C+Daniel+Rubio%2C+Ammar+Shaker%2C+Vanesa+Rodriguez%2DTembras%2C+Makoto+Takamoto%2C+Mathias+Niepert%2C+and+Carolin+Lawrence%2E+2022%2E+MILIE%3A+Modular+%26+iterative+multilingual+open+info)
23. Fabrice Lamarche and Philippe Langlais. 2024. BenchIE^FL: A manually re-annotated fact-based open information extraction benchmark. In Findings of the Association for Computational Linguistics: ACL 2024, pages 8372–8394, Bangkok, Thailand. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Fabrice+Lamarche+and+Philippe+Langlais%2E+2024%2E+BenchIE%5EFL%3A+A+manually+re%2Dannotated+fact%2Dbased+open+information+extraction+benchmark%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL+20)
24. William Lechelle, Fabrizio Gotti, and Phillippe Langlais. 2019. WiRe57 : A fine-grained benchmark for open information extraction. In Proceedings of the 13th Linguistic Annotation Workshop, pages 6–15, Florence, Italy. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=William+Lechelle%2C+Fabrizio+Gotti%2C+and+Phillippe+Langlais%2E+2019%2E+WiRe57+%3A+A+fine%2Dgrained+benchmark+for+open+information+extraction%2E+In+Proceedings+of+the+13th+Linguistic+Annotation+Workshop%2C+pages+6%E2%80%9315)
25. Mausam, Michael Schmitz, Stephen Soderland, Robert Bart, and Oren Etzioni. 2012. Open language learning for information extraction. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 523–534, Jeju Island,… [find](https://scholar.google.com/scholar?q=Mausam%2C+Michael+Schmitz%2C+Stephen+Soderland%2C+Robert+Bart%2C+and+Oren+Etzioni%2E+2012%2E+Open+language+learning+for+information+extraction%2E+In+Proceedings+of+the+2012+Joint+Conference+on+Empirical+Methods+in+)
26. Mausam Mausam. 2016. Open information extraction systems and downstream applications. In Proceedings of the twenty-fifth international joint conference on artificial intelligence, pages 4074–4077. [find](https://scholar.google.com/scholar?q=Mausam+Mausam%2E+2016%2E+Open+information+extraction+systems+and+downstream+applications%2E+In+Proceedings+of+the+twenty%2Dfifth+international+joint+conference+on+artificial+intelligence%2C+pages+4074%E2%80%934077%2E)
27. Filipe Mesquita, Jordan Schmidek, and Denilson Barbosa. 2013. Effectiveness and efficiency of open relation extraction. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 447–457, Seattle, Washington, USA. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Filipe+Mesquita%2C+Jordan+Schmidek%2C+and+Denilson+Barbosa%2E+2013%2E+Effectiveness+and+efficiency+of+open+relation+extraction%2E+In+Proceedings+of+the+2013+Conference+on+Empirical+Methods+in+Natural+Language+P)
28. Ritwik Mishra, Simranjeet Singh, Rajiv Ratn Shah, Ponnurangam Kumaraguru, and Pushpak Bhattacharyya. 2023. IndIE: A multilingual open information extraction tool for Indic languages. In Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings), pages 312–326, Nusa Dua, B… [find](https://scholar.google.com/scholar?q=Ritwik+Mishra%2C+Simranjeet+Singh%2C+Rajiv+Ratn+Shah%2C+Ponnurangam+Kumaraguru%2C+and+Pushpak+Bhattacharyya%2E+2023%2E+IndIE%3A+A+multilingual+open+information+extraction+tool+for+Indic+languages%2E+In+Findings+of+th)
29. Shashi Narayan, Claire Gardent, Shay B. Cohen, and Anastasia Shimorina. 2017. Split and rephrase. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 606– 616, Copenhagen, Denmark. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Shashi+Narayan%2C+Claire+Gardent%2C+Shay+B%2E+Cohen%2C+and+Anastasia+Shimorina%2E+2017%2E+Split+and+rephrase%2E+In+Proceedings+of+the+2017+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pages+606%E2%80%93+)
30. Christina Niklaus, Matthias Cetto, André Freitas, and Siegfried Handschuh. 2018. A survey on open information extraction. arXiv preprint arXiv:1806.05599. [find](https://scholar.google.com/scholar?q=Christina+Niklaus%2C+Matthias+Cetto%2C+Andr%C3%A9+Freitas%2C+and+Siegfried+Handschuh%2E+2018%2E+A+survey+on+open+information+extraction%2E+arXiv+preprint+arXiv%3A1806%2E05599%2E)
31. Liu Pai, Wenyang Gao, Wenjie Dong, Lin Ai, Ziwei Gong, Songfang Huang, Li Zongsheng, Ehsan Hoque, Julia Hirschberg, and Yue Zhang. 2024. A survey on open information extraction from rule-based model to large language model. In Findings of the Association for Computational Linguistics: EMNLP 2024, pa… [find](https://scholar.google.com/scholar?q=Liu+Pai%2C+Wenyang+Gao%2C+Wenjie+Dong%2C+Lin+Ai%2C+Ziwei+Gong%2C+Songfang+Huang%2C+Li+Zongsheng%2C+Ehsan+Hoque%2C+Julia+Hirschberg%2C+and+Yue+Zhang%2E+2024%2E+A+survey+on+open+information+extraction+from+rule%2Dbased+model+t)
32. Harinder Pal and Mausam. 2016. Demonyms and compound relational nouns in nominal open IE. In Proceedings of the 5th Workshop on Automated Knowledge Base Construction, pages 35–39, San Diego, CA. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Harinder+Pal+and+Mausam%2E+2016%2E+Demonyms+and+compound+relational+nouns+in+nominal+open+IE%2E+In+Proceedings+of+the+5th+Workshop+on+Automated+Knowledge+Base+Construction%2C+pages+35%E2%80%9339%2C+San+Diego%2C+CA%2E+Assoc)
33. Kevin Pei, Ishan Jindal, and Kevin Chang. 2023a. Abstractive open information extraction. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6146–6158, Singapore. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Kevin+Pei%2C+Ishan+Jindal%2C+and+Kevin+Chang%2E+2023a%2E+Abstractive+open+information+extraction%2E+In+Proceedings+of+the+2023+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pages+6146%E2%80%936158%2C+Si)
34. Kevin Pei, Ishan Jindal, Kevin Chen-Chuan Chang, ChengXiang Zhai, and Yunyao Li. 2023b. When to use what: An in-depth comparative empirical analysis of OpenIE systems for downstream applications. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Lo… [find](https://scholar.google.com/scholar?q=Kevin+Pei%2C+Ishan+Jindal%2C+Kevin+Chen%2DChuan+Chang%2C+ChengXiang+Zhai%2C+and+Yunyao+Li%2E+2023b%2E+When+to+use+what%3A+An+in%2Ddepth+comparative+empirical+analysis+of+OpenIE+systems+for+downstream+applications%2E+In+P)
35. David Ponce, Thierry Etchegoyhen, Jesús Calleja, and Harritxu Gete. 2024. Split and rephrase with large language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11588– 11607, Bangkok, Thailand. Association for Computat… [find](https://scholar.google.com/scholar?q=David+Ponce%2C+Thierry+Etchegoyhen%2C+Jes%C3%BAs+Calleja%2C+and+Harritxu+Gete%2E+2024%2E+Split+and+rephrase+with+large+language+models%2E+In+Proceedings+of+the+62nd+Annual+Meeting+of+the+Association+for+Computational+)
36. Ji Qi, Chuchun Zhang, Xiaozhi Wang, Kaisheng Zeng, Jifan Yu, Jinxin Liu, Lei Hou, Juanzi Li, and Xu Bin. 2023. Preserving knowledge invariance: Rethinking robustness evaluation of open information extraction. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing,… [find](https://scholar.google.com/scholar?q=Ji+Qi%2C+Chuchun+Zhang%2C+Xiaozhi+Wang%2C+Kaisheng+Zeng%2C+Jifan+Yu%2C+Jinxin+Liu%2C+Lei+Hou%2C+Juanzi+Li%2C+and+Xu+Bin%2E+2023%2E+Preserving+knowledge+invariance%3A+Rethinking+robustness+evaluation+of+open+information+ext)
37. Youngbin Ro, Yukyung Lee, and Pilsung Kang. 2020. Multi^2OIE: Multilingual open information extraction based on multi-head attention with BERT. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1107–1117, Online. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Youngbin+Ro%2C+Yukyung+Lee%2C+and+Pilsung+Kang%2E+2020%2E+Multi%5E2OIE%3A+Multilingual+open+information+extraction+based+on+multi%2Dhead+attention+with+BERT%2E+In+Findings+of+the+Association+for+Computational+Linguis)
38. Jacob Solawetz and Stefan Larson. 2021. LSOIE: A large-scale dataset for supervised open information extraction. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2595–2600, Online. Association for Computational Linguis… [find](https://scholar.google.com/scholar?q=Jacob+Solawetz+and+Stefan+Larson%2E+2021%2E+LSOIE%3A+A+large%2Dscale+dataset+for+supervised+open+information+extraction%2E+In+Proceedings+of+the+16th+Conference+of+the+European+Chapter+of+the+Association+for+Co)
39. Gabriel Stanovsky and Ido Dagan. 2016. Creating a large benchmark for open information extraction. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2300–2305, Austin, Texas. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Gabriel+Stanovsky+and+Ido+Dagan%2E+2016%2E+Creating+a+large+benchmark+for+open+information+extraction%2E+In+Proceedings+of+the+2016+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pages+2300)
40. Gabriel Stanovsky, Julian Michael, Luke Zettlemoyer, and Ido Dagan. 2018. Supervised open information extraction. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 885–895,… [find](https://scholar.google.com/scholar?q=Gabriel+Stanovsky%2C+Julian+Michael%2C+Luke+Zettlemoyer%2C+and+Ido+Dagan%2E+2018%2E+Supervised+open+information+extraction%2E+In+Proceedings+of+the+2018+Conference+of+the+North+American+Chapter+of+the+Association)
41. Mingming Sun, Xu Li, Xin Wang, Miao Fan, Yue Feng, and Ping Li. 2018. Logician: A unified end-to-end neural approach for open-domain information extraction. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM ’18, page 556–564, New York, NY, USA. Associati… [find](https://scholar.google.com/scholar?q=Mingming+Sun%2C+Xu+Li%2C+Xin+Wang%2C+Miao+Fan%2C+Yue+Feng%2C+and+Ping+Li%2E+2018%2E+Logician%3A+A+unified+end%2Dto%2Dend+neural+approach+for+open%2Ddomain+information+extraction%2E+In+Proceedings+of+the+Eleventh+ACM+Internat)
42. Beth M. Sundheim and Nancy A. Chinchor. 1993. Survey of the Message Understanding Conferences. In Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993. [find](https://scholar.google.com/scholar?q=Beth+M%2E+Sundheim+and+Nancy+A%2E+Chinchor%2E+1993%2E+Survey+of+the+Message+Understanding+Conferences%2E+In+Human+Language+Technology%3A+Proceedings+of+a+Workshop+Held+at+Plainsboro%2C+New+Jersey%2C+March+21%2D24%2C+1993)
43. Shikhar Vashishth, Prince Jain, and Partha Talukdar. 2018. Cesi: Canonicalizing open knowledge bases using embeddings and side information. In Proceedings of the 2018 World Wide Web Conference, pages 1317–1327. [find](https://scholar.google.com/scholar?q=Shikhar+Vashishth%2C+Prince+Jain%2C+and+Partha+Talukdar%2E+2018%2E+Cesi%3A+Canonicalizing+open+knowledge+bases+using+embeddings+and+side+information%2E+In+Proceedings+of+the+2018+World+Wide+Web+Conference%2C+pages+)
44. Michael Vasilkovsky, Anton Alekseev, Valentin Malykh, Ilya Shenbin, Elena Tutubalina, Dmitriy Sa-likhov, Mikhail Stepnov, Andrey Chertok, and Sergey Nikolenko. 2022. Detie: Multilingual open information extraction inspired by object detection. In Proceedings of the AAAI Conference on Artificial Inte… [find](https://scholar.google.com/scholar?q=Michael+Vasilkovsky%2C+Anton+Alekseev%2C+Valentin+Malykh%2C+Ilya+Shenbin%2C+Elena+Tutubalina%2C+Dmitriy+Sa%2Dlikhov%2C+Mikhail+Stepnov%2C+Andrey+Chertok%2C+and+Sergey+Nikolenko%2E+2022%2E+Detie%3A+Multilingual+open+informati)
45. Chenguang Wang, Xiao Liu, and Dawn Song. 2022a. IELM: An open information extraction benchmark for pre-trained language models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8417–8437, Abu Dhabi, United Arab Emirates. Association for Computational L… [find](https://scholar.google.com/scholar?q=Chenguang+Wang%2C+Xiao+Liu%2C+and+Dawn+Song%2E+2022a%2E+IELM%3A+An+open+information+extraction+benchmark+for+pre%2Dtrained+language+models%2E+In+Proceedings+of+the+2022+Conference+on+Empirical+Methods+in+Natural+La)
46. Xin Wang, Minlong Peng, Mingming Sun, and Ping Li. 2022b. OIE@OIA: an adaptable and efficient open information extraction framework. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6213–6226, Dublin, Ireland. Association for C… [find](https://scholar.google.com/scholar?q=Xin+Wang%2C+Minlong+Peng%2C+Mingming+Sun%2C+and+Ping+Li%2E+2022b%2E+OIE%40OIA%3A+an+adaptable+and+efficient+open+information+extraction+framework%2E+In+Proceedings+of+the+60th+Annual+Meeting+of+the+Association+for+Co)
47. Fei Wu and Daniel S. Weld. 2010. [Open information extraction using Wikipedia](https://md.research.elamur.ai/free/en/papers/wu-2010-open-information-extraction-wikipedia/). In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 118–127, Uppsala, Sweden. Association for Computational Linguistics. xAI. xai grok models.
48. Ying Xu, Mi-Young Kim, Kevin Quinn, Randy Goebel, and Denilson Barbosa. 2013. Open information extraction with tree kernels. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 868–877, Atlanta, Geor… [find](https://scholar.google.com/scholar?q=Ying+Xu%2C+Mi%2DYoung+Kim%2C+Kevin+Quinn%2C+Randy+Goebel%2C+and+Denilson+Barbosa%2E+2013%2E+Open+information+extraction+with+tree+kernels%2E+In+Proceedings+of+the+2013+Conference+of+the+North+American+Chapter+of+the+)
49. Mohamed Yahya, Steven Whang, Rahul Gupta, and Alon Halevy. 2014. ReNoun: Fact extraction for nominal attributes. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 325–335, Doha, Qatar. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Mohamed+Yahya%2C+Steven+Whang%2C+Rahul+Gupta%2C+and+Alon+Halevy%2E+2014%2E+ReNoun%3A+Fact+extraction+for+nominal+attributes%2E+In+Proceedings+of+the+2014+Conference+on+Empirical+Methods+in+Natural+Language+Processi)
50. Peiran Yao, Kostyantyn Guzhva, and Denilson Barbosa. 2024. Semantic graphs for syntactic simplification: A revisit from the age of LLM. In Proceedings of TextGraphs-17: Graph-based Methods for Natural Language Processing, pages 105–115, Bangkok, Thailand. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Peiran+Yao%2C+Kostyantyn+Guzhva%2C+and+Denilson+Barbosa%2E+2024%2E+Semantic+graphs+for+syntactic+simplification%3A+A+revisit+from+the+age+of+LLM%2E+In+Proceedings+of+TextGraphs%2D17%3A+Graph%2Dbased+Methods+for+Natural)
51. Bowen Yu, Yucheng Wang, Tingwen Liu, Hongsong Zhu, Limin Sun, and Bin Wang. 2021. Maximal clique based non-autoregressive open information extraction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9696–9706, Online and Punta Cana, Dominican Republic… [find](https://scholar.google.com/scholar?q=Bowen+Yu%2C+Yucheng+Wang%2C+Tingwen+Liu%2C+Hongsong+Zhu%2C+Limin+Sun%2C+and+Bin+Wang%2E+2021%2E+Maximal+clique+based+non%2Dautoregressive+open+information+extraction%2E+In+Proceedings+of+the+2021+Conference+on+Empirica)
52. Bowen Yu, Zhenyu Zhang, Jingyang Li, Haiyang Yu, Tingwen Liu, Jian Sun, Yongbin Li, and Bin Wang. 2022. Towards generalized open information extraction. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1439–1453, Abu Dhabi, United Arab Emirates. Association for Computa… [find](https://scholar.google.com/scholar?q=Bowen+Yu%2C+Zhenyu+Zhang%2C+Jingyang+Li%2C+Haiyang+Yu%2C+Tingwen+Liu%2C+Jian+Sun%2C+Yongbin+Li%2C+and+Bin+Wang%2E+2022%2E+Towards+generalized+open+information+extraction%2E+In+Findings+of+the+Association+for+Computationa)

*Updated 1 November 2026*