---
title: "Open Information Extraction Using Wikipedia — Wu 2010"
description: "Most relation extractors are trained on labeled examples for each target relation, so they cannot cover the huge, open-ended variety of relations…"
authors: ["Fei Wu", "Daniel S. Weld"]
year: 2010
venue: "Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics"
source: "https://aclanthology.org/P10-1013/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wu-2010-open-information-extraction-wikipedia/"
---
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# Open Information Extraction Using Wikipedia

**Authors:** [Fei Wu](https://md.research.elamur.ai/free/en/authors/fei-wu/), [Daniel S. Weld](https://md.research.elamur.ai/free/en/authors/daniel-s-weld/) · **Year:** 2010 · **Venue:** Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/P10-1013/) · [PDF](https://aclanthology.org/P10-1013.pdf) · [OpenAlex](https://openalex.org/W2161494021)

**Citations:** 533 citations (OpenAlex, 2026-10-14) · [115](https://md.research.elamur.ai/free/en/papers/wu-2010-open-information-extraction-wikipedia/cited-by/) in our library, of the 140884 papers whose bibliographies we read

## Summary
Most relation extractors are trained on labeled examples for each target relation, so they cannot cover the huge, open-ended variety of relations expressed on the Web. Open IE systems such as TextRunner avoid fixed relation inventories but are trained on heuristically labeled data of limited quality. This paper introduces WOE (Wikipedia-based Open Extractor), which builds its training data by automatically aligning Wikipedia infobox attribute values (taken from DBpedia) with sentences in the same article that mention both the article subject and the value. These relation-specific matches are then abstracted into relation-independent, unlexicalized training examples. WOE has two variants. WOE pos is a CRF over shallow features such as POS tags and NP chunks. WOE parse is a classifier over shortest dependency paths from the Stanford Parser. On three test corpora, both variants outperform TextRunner, and the parser-based variant gains the most. The work shows that noisy collaborative resources can supervise a general-purpose extractor that works beyond Wikipedia. It also shows that unlexicalized syntactic paths are valuable, contrary to earlier findings.

## Key points
- DBpedia provided cleaned infoboxes for 1,027,744 articles. Matching these to article sentences produced 301,962 labeled training sentences.
- Compared with TextRunner, WOE pos improves F-measure by 18–34% while running at the same speed. WOE parse improves it by 72–91% but is roughly 30 times slower because of parsing.
- The authors trained TextRunner's learner and features on several alternative training-data sources. This experiment showed that most of WOE's accuracy gain comes from the Wikipedia matching heuristic.
- WOE parse learns 15,333 general dependency-path patterns. Its accuracy on WSJ with automatic parses is almost identical to its accuracy with gold-standard trees, so parser errors have negligible impact.
- Synonym sets for matching are compiled from Wikipedia redirect pages and the anchor texts of backward links.

## Interesting ideas
- **Lift distant supervision to the relation-agnostic level.** Use relation-specific matches between a knowledge base and text only to generate examples, then strip away the relation identity and lexical content to train one general extractor. This could transfer to any language that has a Wikipedia with infoboxes, including Ukrainian.
- **Prefer precision when matching.** The matcher discards an attribute if its value appears in more than one sentence. It also rejects sentences where the subject and value are not noun-phrase heads or sit in unrelated clauses. Throwing away ambiguous cases is a cheap way to get cleaner automatic training sets.
- **Use a cascade of entity-mention heuristics.** The matcher tries, in order: the full name, synonyms, a prefix or suffix of the name, "the \<type>" phrases built from the article's first sentence, and the article's most frequent pronoun (except "it"). This cascade is a simple, reusable approach to coreference-light subject matching in encyclopedic text.
- **Join hyperlinked anchor texts before parsing.** Merging the words of an anchor text into a single token with underscores helped the parser. It is an easy preprocessing trick for any pipeline that parses wiki-style markup.
- **Ablate the supervision source separately from the model.** The authors kept TextRunner's learner and features fixed and varied only how training data was generated. This cleanly attributes gains to the data rather than the model, an evaluation design worth copying.

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

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Information extraction](https://md.research.elamur.ai/free/en/topics/information-extraction/)

## Cite

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

```bibtex
@inproceedings{wu2010open,
  title = {{Open Information Extraction Using Wikipedia}},
  author = {Wu, Fei and Weld, Daniel S.},
  booktitle = {Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics},
  year = {2010},
  url = {https://aclanthology.org/P10-1013/}
}
```

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## References

31 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. E. Agichtein and L. Gravano. 2000. Snowball: Extracting relations from large plain-text collections. In ICDL. [find](https://scholar.google.com/scholar?q=E%2E+Agichtein+and+L%2E+Gravano%2E+2000%2E+Snowball%3A+Extracting+relations+from+large+plain%2Dtext+collections%2E+In+ICDL%2E)
2. Alan Akbik and Jügen Broß. 2009. Wanderlust: Extracting semantic relations from natural language text using dependency grammar patterns. In WWW Workshop. [find](https://scholar.google.com/scholar?q=Alan+Akbik+and+Ju%CC%88gen+Bro%C3%9F%2E+2009%2E+Wanderlust%3A+Extracting+semantic+relations+from+natural+language+text+using+dependency+grammar+patterns%2E+In+WWW+Workshop%2E)
3. Sören Auer and Jens Lehmann. 2007. What have innsbruck and leipzig in common? extracting semantics from wiki content. In ESWC. [find](https://scholar.google.com/scholar?q=So%CC%88ren+Auer+and+Jens+Lehmann%2E+2007%2E+What+have+innsbruck+and+leipzig+in+common%3F+extracting+semantics+from+wiki+content%2E+In+ESWC%2E)
4. M. Banko, M. Cafarella, S. Soderland, M. Broadhead, and O. Etzioni. 2007. Open information extraction from the Web. In Procs. of IJCAI. [find](https://scholar.google.com/scholar?q=M%2E+Banko%2C+M%2E+Cafarella%2C+S%2E+Soderland%2C+M%2E+Broadhead%2C+and+O%2E+Etzioni%2E+2007%2E+Open+information+extraction+from+the+Web%2E+In+Procs%2E+of+IJCAI%2E) [our card](https://md.research.elamur.ai/free/en/papers/banko-2007-open-information-extraction-web/)
5. Razvan C. Bunescu and Raymond J. Mooney. 2005. Subsequence kernels for relation extraction. In NIPS. [find](https://scholar.google.com/scholar?q=Razvan+C%2E+Bunescu+and+Raymond+J%2E+Mooney%2E+2005%2E+Subsequence+kernels+for+relation+extraction%2E+In+NIPS%2E)
6. R. Bunescu and R.Mooney. 2005. A shortest path dependency kernel for relation extraction. In HLT/EMNLP. [find](https://scholar.google.com/scholar?q=R%2E+Bunescu+and+R%2EMooney%2E+2005%2E+A+shortest+path+dependency+kernel+for+relation+extraction%2E+In+HLT%2FEMNLP%2E) [our card](https://md.research.elamur.ai/free/en/papers/bunescu-2005-shortest-path-dependency-kernel-relation/)
7. Eugene Charniak and Mark Johnson. 2005. Coarse-to-fine n-best parsing and maxent discriminative reranking. In ACL. [find](https://scholar.google.com/scholar?q=Eugene+Charniak+and+Mark+Johnson%2E+2005%2E+Coarse%2Dto%2Dfine+n%2Dbest+parsing+and+maxent+discriminative+reranking%2E+In+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/narrative-2005-charniak-coarse-fine-n-best-parsing/)
8. M. Craven, D. DiPasquo, D. Freitag, A. McCallum, T. Mitchell, K. Nigam, and S. Slattery. 1998. Learning to extract symbolic knowledge from the world wide web. In AAAI. [find](https://scholar.google.com/scholar?q=M%2E+Craven%2C+D%2E+DiPasquo%2C+D%2E+Freitag%2C+A%2E+McCallum%2C+T%2E+Mitchell%2C+K%2E+Nigam%2C+and+S%2E+Slattery%2E+1998%2E+Learning+to+extract+symbolic+knowledge+from+the+world+wide+web%2E+In+AAAI%2E)
9. Dmitry Davidov and Ari Rappoport. 2008. Unsupervised discovery of generic relationships using pattern clusters and its evaluation by automatically generated sat analogy questions. In ACL. [find](https://scholar.google.com/scholar?q=Dmitry+Davidov+and+Ari+Rappoport%2E+2008%2E+Unsupervised+discovery+of+generic+relationships+using+pattern+clusters+and+its+evaluation+by+automatically+generated+sat+analogy+questions%2E+In+ACL%2E)
10. Dmitry Davidov, Ari Rappoport, and Moshe Koppel. 2007. Fully unsupervised discovery of concept-specific relationships by web mining. In ACL. [find](https://scholar.google.com/scholar?q=Dmitry+Davidov%2C+Ari+Rappoport%2C+and+Moshe+Koppel%2E+2007%2E+Fully+unsupervised+discovery+of+concept%2Dspecific+relationships+by+web+mining%2E+In+ACL%2E)
11. Marie-Catherine de Marneffe and Christopher D. Manning. 2008. Stanford typed dependencies manual. [http://nlp.stanford.edu/downloads/lex-parser.shtml](http://nlp.stanford.edu/downloads/lex-parser.shtml). [↗](http://nlp.stanford.edu/downloads/lex-parser.shtml)
12. Benjamin Van Durme and Lenhart K. Schubert. 2008. Open knowledge extraction using compositional language processing. In STEP. [find](https://scholar.google.com/scholar?q=Benjamin+Van+Durme+and+Lenhart+K%2E+Schubert%2E+2008%2E+Open+knowledge+extraction+using+compositional+language+processing%2E+In+STEP%2E)
13. R. Hoffmann, C. Zhang, and D. Weld. 2010. Learning 5000 relational extractors. In ACL. [find](https://scholar.google.com/scholar?q=R%2E+Hoffmann%2C+C%2E+Zhang%2C+and+D%2E+Weld%2E+2010%2E+Learning+5000+relational+extractors%2E+In+ACL%2E)
14. Jing Jiang and ChengXiang Zhai. 2007. A systematic exploration of the feature space for relation extraction. In HLT/NAACL. [find](https://scholar.google.com/scholar?q=Jing+Jiang+and+ChengXiang+Zhai%2E+2007%2E+A+systematic+exploration+of+the+feature+space+for+relation+extraction%2E+In+HLT%2FNAACL%2E)
15. A. Gangemi M. Ciaramita. 2005. Unsupervised learning of semantic relations between concepts of a molecular biology ontology. In IJCAI. [find](https://scholar.google.com/scholar?q=A%2E+Gangemi+M%2E+Ciaramita%2E+2005%2E+Unsupervised+learning+of+semantic+relations+between+concepts+of+a+molecular+biology+ontology%2E+In+IJCAI%2E)
16. Andrew Kachites McCallum. 2002. Mallet: A machine learning for language toolkit. In [http://mallet.cs.umass.edu](http://mallet.cs.umass.edu). [↗](http://mallet.cs.umass.edu)
17. Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. 2009. Distant supervision for relation extraction without labeled data. In ACL-IJCNLP. [find](https://scholar.google.com/scholar?q=Mike+Mintz%2C+Steven+Bills%2C+Rion+Snow%2C+and+Dan+Jurafsky%2E+2009%2E+Distant+supervision+for+relation+extraction+without+labeled+data%2E+In+ACL%2DIJCNLP%2E)
18. T. H. Kotaro Nakayama and S. Nishio. 2008. Wikipedia link structure and text mining for semantic relation extraction. In CEUR Workshop. [find](https://scholar.google.com/scholar?q=T%2E+H%2E+Kotaro+Nakayama+and+S%2E+Nishio%2E+2008%2E+Wikipedia+link+structure+and+text+mining+for+semantic+relation+extraction%2E+In+CEUR+Workshop%2E)
19. Dat P.T Nguyen, Yutaka Matsuo, and Mitsuru Ishizuka. 2007. Exploiting syntactic and semantic information for relation extraction from wikipedia. In IJCAI07-TextLinkWS. [find](https://scholar.google.com/scholar?q=Dat+P%2ET+Nguyen%2C+Yutaka+Matsuo%2C+and+Mitsuru+Ishizuka%2E+2007%2E+Exploiting+syntactic+and+semantic+information+for+relation+extraction+from+wikipedia%2E+In+IJCAI07%2DTextLinkWS%2E)
20. Marius Pasca. 2008. Turning web text and search queries into factual knowledge: Hierarchical class attribute extraction. In AAAI. [find](https://scholar.google.com/scholar?q=Marius+Pasca%2E+2008%2E+Turning+web+text+and+search+queries+into+factual+knowledge%3A+Hierarchical+class+attribute+extraction%2E+In+AAAI%2E)
21. Fuchun Peng and Andrew McCallum. 2004. Accurate Information Extraction from Research Papers using Conditional Random Fields. In HLT-NAACL. [find](https://scholar.google.com/scholar?q=Fuchun+Peng+and+Andrew+McCallum%2E+2004%2E+Accurate+Information+Extraction+from+Research+Papers+using+Conditional+Random+Fields%2E+In+HLT%2DNAACL%2E)
22. Hoifung Poon and Pedro Domingos. 2008. Joint Inference in Information Extraction. In AAAI. [find](https://scholar.google.com/scholar?q=Hoifung+Poon+and+Pedro+Domingos%2E+2008%2E+Joint+Inference+in+Information+Extraction%2E+In+AAAI%2E)
23. Y. Shinyama and S. Sekine. 2006. Preemptive information extraction using unristricted relation discovery. In HLT-NAACL. [find](https://scholar.google.com/scholar?q=Y%2E+Shinyama+and+S%2E+Sekine%2E+2006%2E+Preemptive+information+extraction+using+unristricted+relation+discovery%2E+In+HLT%2DNAACL%2E)
24. Rion Snow, Daniel Jurafsky, and Andrew Y. Ng. 2005. Learning syntactic patterns for automatic hypernym discovery. In NIPS. [find](https://scholar.google.com/scholar?q=Rion+Snow%2C+Daniel+Jurafsky%2C+and+Andrew+Y%2E+Ng%2E+2005%2E+Learning+syntactic+patterns+for+automatic+hypernym+discovery%2E+In+NIPS%2E)
25. Fabian M. Suchanek, Gjergji Kasneci, and Gerhard Weikum. 2007. Yago: A core of semantic knowledge - unifying WordNet and Wikipedia. In WWW. [find](https://scholar.google.com/scholar?q=Fabian+M%2E+Suchanek%2C+Gjergji+Kasneci%2C+and+Gerhard+Weikum%2E+2007%2E+Yago%3A+A+core+of+semantic+knowledge+%2D+unifying+WordNet+and+Wikipedia%2E+In+WWW%2E)
26. Mengqiu Wang. 2008. A re-examination of dependency path kernels for relation extraction. In IJC-NLP. [find](https://scholar.google.com/scholar?q=Mengqiu+Wang%2E+2008%2E+A+re%2Dexamination+of+dependency+path+kernels+for+relation+extraction%2E+In+IJC%2DNLP%2E)
27. Fei Wu and Daniel Weld. 2007. Autonomouslly Semantifying Wikipedia. In CIKM. [find](https://scholar.google.com/scholar?q=Fei+Wu+and+Daniel+Weld%2E+2007%2E+Autonomouslly+Semantifying+Wikipedia%2E+In+CIKM%2E)
28. Fei Wu, Raphael Hoffmann, and Danel S. Weld. 2008. Information extraction from Wikipedia: Moving down the long tail. In KDD. [find](https://scholar.google.com/scholar?q=Fei+Wu%2C+Raphael+Hoffmann%2C+and+Danel+S%2E+Weld%2E+2008%2E+Information+extraction+from+Wikipedia%3A+Moving+down+the+long+tail%2E+In+KDD%2E)
29. Min Zhang, Jie Zhang, Jian Su, and Guodong Zhou. 2006. A composite kernel to extract relations between entities with both flat and structured features. In ACL. [find](https://scholar.google.com/scholar?q=Min+Zhang%2C+Jie+Zhang%2C+Jian+Su%2C+and+Guodong+Zhou%2E+2006%2E+A+composite+kernel+to+extract+relations+between+entities+with+both+flat+and+structured+features%2E+In+ACL%2E)
30. Shubin Zhao and Ralph Grishman. 2005. Extracting relations with integrated information using kernel methods. In ACL. [find](https://scholar.google.com/scholar?q=Shubin+Zhao+and+Ralph+Grishman%2E+2005%2E+Extracting+relations+with+integrated+information+using+kernel+methods%2E+In+ACL%2E)
31. Jun Zhu, Zaiqing Nie, Xiaojiang Liu, Bo Zhang, and Ji-Rong Wen. 2009. Statsnowball: a statistical approach to extracting entity relationships. In WWW. [find](https://scholar.google.com/scholar?q=Jun+Zhu%2C+Zaiqing+Nie%2C+Xiaojiang+Liu%2C+Bo+Zhang%2C+and+Ji%2DRong+Wen%2E+2009%2E+Statsnowball%3A+a+statistical+approach+to+extracting+entity+relationships%2E+In+WWW%2E)