---
title: "Advancing the Arabic WordNet: Elevating Content Quality (2024)"
description: "The existing Arabic WordNet releases (AWN V1, AWN V2) were built mostly by translating Princeton WordNet (PWN) synsets… 40 references."
authors: ["Abed Alhakim Freihat", "Hadi Mahmoud Khalilia", "Gábor Bella", "Fausto Giunchiglia"]
year: 2024
venue: "Proceedings of the 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT) with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation @ LREC-COLING 2024"
source: "https://aclanthology.org/2024.osact-1.9/"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/freihat-2024-advancing-arabic-wordnet-elevating-content/"
---
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# Advancing the Arabic WordNet: Elevating Content Quality

**Authors:** [Abed Alhakim Freihat](https://md.research.elamur.ai/free/en/authors/abed-alhakim-freihat/), Hadi Mahmoud Khalilia, [Gábor Bella](https://md.research.elamur.ai/free/en/authors/gabor-bella/) and [Fausto Giunchiglia](https://md.research.elamur.ai/free/en/authors/fausto-giunchiglia/) · **Year:** 2024 · **Venue:** [Proceedings of the 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT) with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation @ LREC-COLING 2024](https://md.research.elamur.ai/free/en/venues/osact/) ([Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT) with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation @ LREC-COLING 2024](https://md.research.elamur.ai/free/en/venues/osact/2024/)) · **Publisher:** [European Language Resources Association (ELRA)](https://md.research.elamur.ai/free/en/publishers/european-language-resources-association-elra/) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.osact-1.9/) · [PDF](https://aclanthology.org/2024.osact-1.9.pdf) · [OpenAlex](https://openalex.org/W7131776934)

**Citations:** 1 citations (OpenAlex, 2026-10-14)

## Summary
The existing Arabic WordNet releases (AWN V1, AWN V2) were built mostly by translating Princeton WordNet (PWN) synsets, partly with semi-automatic tools. As a result they contain wrong or duplicated lemmas, leave out valid synonyms, and lack glosses and usage examples, so their errors are hard to spot. Earlier work tried to add more entries rather than fix the ones already there. The authors produce AWN V3 by revising AWN V1 within the expand model. Two translators worked synset by synset, comparing each PWN synset with its AWN V1 counterpart in a spreadsheet and consulting bilingual dictionaries. They added missing lemmas, glosses and examples and removed incorrect lemmas. They also chose not to carry over certain kinds of redundant polysemy from English. Two new structural elements were introduced: lexical gaps, which mark concepts Arabic does not lexicalize, and phrasets, which are free phrases that convey such concepts. A PhD-level Arabic linguist checked every contribution and judged about 98% of them correct. The revised resource is released openly. It shows that a wordnet can be improved by careful quality work and not only by adding coverage.

## Key points
- AWN V3 contains 9,576 synsets covering nouns, verbs, adjectives and adverbs. 5,554 synsets from AWN V1 were updated, which is more than 58% of the original.
- Edits made:
  - 2,726 lemmas added and 8,751 incorrect lemmas deleted;
  - 9,322 glosses and 12,204 example sentences added;
  - 236 lexical gaps identified and 701 phrasets inserted.
- For comparison, AWN V1 had 9,618 synsets (its core alone had 9,228), and AWN V2 extended it to 11,269. Both versions had no glosses and no examples.
- The workflow has three stages. Translators first translate a synset or mark it as a gap, using the Al-Mawrid Al-Qareeb dictionary. They then cross-validate each other's work. Finally an expert reviews the gap, gloss and lemma decisions. Overall correctness was 98.08%, ranging from 96.82% for gaps to 99.13% for examples.
- The data is published at github.com/HadiPTUK/AWN3.0. For each POS there are two spreadsheets: one with the final synsets and one listing the added and deleted components.

## Interesting ideas
- Lexical gaps are recorded explicitly, which separates two cases: a concept the language really has no word for, and a word the resource simply has not added yet.
- Phrasets give a compositional phrase for a concept that has no single-word equivalent.
- Senses that only reflect redundant polysemy in the English source, such as specialization or compound-noun polysemy, are deliberately not inherited.
- Correctness is reported separately for each type of edit (additions, deletions, glosses, gaps). Each rejected edit gets a comment, and errors are traced to their cause, here translators misreading English glosses.
- The paper argues that entries without glosses and examples cannot really be checked, so it adds both everywhere.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (world): Our WordNet-derived ontology could mark concepts as "unlexicalized in language X" rather than leaving them empty. This would keep a missing entry from being read as non-existence.
- **I2** (back): When generating or translating through the meta-world, the system could use a stored phrase whenever the target vocabulary has no lemma for a concept node.
- **I3** (world): Pruning or merging such near-duplicate WordNet senses before compiling the ontology would reduce the sense ambiguity that frames and the text world must otherwise resolve.
- **I4** (learn): Self-induced repair rules and silver-data edits could be scored by edit type with logged rejection reasons. This would show which kinds of changes are unreliable.
- **I5** (world): Requiring every compiled knowledge entry (verb class, ontology node) to carry a gloss and an example sentence would make it auditable. The examples could then serve as regression tests for the parser and frames.

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

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## Cite

Cite as: Abed Alhakim Freihat, Hadi Mahmoud Khalilia, Gábor Bella and Fausto Giunchiglia. 2024. Advancing the Arabic WordNet: Elevating Content Quality. Proceedings of the 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT) with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation @ LREC-COLING 2024.

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

```bibtex
@inproceedings{freihat2024advancing,
  title = {{Advancing the Arabic WordNet: Elevating Content Quality}},
  author = {Abed Alhakim Freihat, Hadi Mahmoud Khalilia, Gábor Bella and Fausto Giunchiglia},
  booktitle = {Proceedings of the 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT) with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation @ LREC-COLING 2024},
  year = {2024},
  doi = {10.63317/3sodtwuwrss4},
  url = {https://doi.org/10.63317/3sodtwuwrss4}
}
```

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## References

40 references, 3 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 24 October 2026*