---
title: "A Study on Linking Wikipedia Categories to Wordnet Synsets"
description: "The paper tackles a disambiguation step in building a Named Entity Repository from WordNet and… 19 references, cited by 6 papers here."
authors: ["Antonio Toral", "Óscar Ferrández", "Eneko Agirre", "Rafael Muñoz"]
year: 2009
venue: "Proceedings of the International Conference RANLP-2009"
source: "https://aclanthology.org/R09-1080/"
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/toral-2009-study-linking-wikipedia-categories-wordnet/"
---
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# A Study on Linking Wikipedia Categories to Wordnet Synsets using Text Similarity

**Authors:** [Antonio Toral](https://md.research.elamur.ai/free/en/authors/antonio-toral/), [Óscar Ferrández](https://md.research.elamur.ai/free/en/authors/oscar-ferrandez/), [Eneko Agirre](https://md.research.elamur.ai/free/en/authors/eneko-agirre/) and [Rafael Muñoz](https://md.research.elamur.ai/free/en/authors/rafael-munoz/) · **Year:** 2009 · **Venue:** [Proceedings of the International Conference RANLP-2009](https://md.research.elamur.ai/free/en/venues/ranlp/) ([RANLP 2009](https://md.research.elamur.ai/free/en/venues/ranlp/2009/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/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/R09-1080/) · [PDF](https://aclanthology.org/R09-1080.pdf) · [OpenAlex](https://openalex.org/W2250862994)

**Citations:** 16 citations (OpenAlex, 2026-10-14) · 6 in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper tackles a disambiguation step in building a Named Entity Repository from WordNet and Wikipedia. When polysemous WordNet nouns are matched to Wikipedia categories, it is unclear which sense a category refers to. An earlier approach used instances shared by both resources. It was perfectly precise but covered only 39% of cases. The authors therefore compare each WordNet gloss with the abstract of the matched category and pick the most similar sense.

They test several methods on a manually built, publicly released gold standard:
- word overlap,
- Semantic Vectors (random projection),
- Personalised PageRank over the WordNet graph,
- an in-house textual entailment (TE) system, applied in both directions and averaged.

They also try voting, unsupervised and supervised combinations of these methods.

The most-frequent-sense baseline is hard to beat at 64.7%. The best unsupervised result is 68%, from voting. A supervised TE model reaches 77.74%. The work shows that text-similarity techniques can resolve most ambiguous WordNet–Wikipedia links automatically, which matters for resource-linking projects such as YAGO.

## Key points
- Baselines: First Sense scores 64.7%. Word overlap scores 56.3%, rising to 62.7% once stop words are removed. Removing stop words also lifts Personalised PageRank from 61.8% to 64.3%.
- The Semantic Vectors model was built from 117,598 WordNet 2.1 glosses and 2,179,275 Wikipedia abstracts (January 2008 dump). It scored lowest at 54.1%.
- The TE system's accuracy depends heavily on training data:
  - trained on AVE 2007–08 and RTE-3 corpora: 52.8%;
  - without training: 64.7%;
  - supervised BayesNet with 10-fold cross-validation on the task data: 77.74%.
- Combinations:
  - The oracle upper bound is 84.5%.
  - The best voting combination (PPR + TE + WO) reaches 68%.
  - No supervised combination beats TE alone; the best gets 77.24%. The authors attribute this to TE's internal features overlapping with the other methods.
- Personalised PageRank used all WordNet 3.0 relations, including disambiguated glosses. It ran with damping 0.85 for 30 iterations, and these settings were not tuned for this task.

*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

- [University of the Basque Country](https://md.research.elamur.ai/free/en/universities/university-of-the-basque-country/) — University, Leioa, Spain · 62 works · 16,683 citations
- [Dublin City University](https://md.research.elamur.ai/free/en/universities/dublin-city-university/) — University, Dublin, Ireland · 73 works · 9,768 citations
- [University of Alicante](https://md.research.elamur.ai/free/en/universities/university-of-alicante/) — University, Alicante, Spain · 24 works · 1,251 citations

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## Cite

Cite as: Antonio Toral, Óscar Ferrández, Eneko Agirre and Rafael Muñoz. 2009. A Study on Linking Wikipedia Categories to Wordnet Synsets using Text Similarity. Proceedings of the International Conference RANLP-2009.

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

```bibtex
@inproceedings{toral2009study,
  title = {{A Study on Linking Wikipedia Categories to Wordnet Synsets using Text Similarity}},
  author = {Antonio Toral, Óscar Ferrández, Eneko Agirre and Rafael Muñoz},
  booktitle = {Proceedings of the International Conference RANLP-2009},
  year = {2009},
  url = {https://aclanthology.org/R09-1080/}
}
```

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## References

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

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*Updated 3 November 2026*