---
title: "Using Linguistic Analysis to Translate Arabic Natural Language"
description: "AlAgha proposes a domain-independent pipeline that turns Arabic questions into SPARQL using syntactic analysis instead of… 35 references."
authors: ["Iyad AlAgha"]
year: 2015
venue: "arXiv:1508.01447"
source: "https://arxiv.org/abs/1508.01447"
doi: "10.48550/arxiv.1508.01447"
arxiv: "1508.01447"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/alagha-2015-using-linguistic-analysis-translate-arabic/"
---
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# Using Linguistic Analysis to Translate Arabic Natural Language Queries to SPARQL

**Authors:** Iyad AlAgha · **Year:** 2015 · **Venue:** arXiv:1508.01447 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1508.01447) · [PDF](https://arxiv.org/pdf/1508.01447) · [DOI 10.48550/arxiv.1508.01447](https://doi.org/10.48550/arxiv.1508.01447) · [arXiv 1508.01447](https://arxiv.org/abs/1508.01447) · [OpenAlex](https://openalex.org/W2212742528)

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

## Summary
Querying ontology-based knowledge bases normally requires knowing SPARQL. Most natural-language interfaces that remove this barrier were built for English. AlAgha proposes a domain-independent pipeline that turns Arabic questions into SPARQL using syntactic analysis instead of keyword matching.

The pipeline works in four steps:
1. Microsoft's ATKS statistical parser produces a constituency tree for the Arabic question.
2. Noun phrases are pulled from that tree.
3. Neighbouring NPs are paired, together with the words that link them, into "Intermediate Triples". These are then grounded against the ontology, whose terms carry Arabic names through rdfs:label. Missing pieces are filled using ontology knowledge.
4. Query targets and modifiers are identified and rendered as SPARQL, including negation and conjunctive or disjunctive constraints.

The design borrows from PANTO and was tested on two OWL datasets, Geography and Diseases. The author reports results comparable to English systems. The error analysis shows that parser mistakes cause the largest share of failures. The work matters as an early attempt to open Semantic Web content to Arabic speakers without building a hand-crafted lexicon.

## Key points
- Parsing is handled by Microsoft's Arabic Toolkit Service (ATKS), whose statistical parser is available to academics as a web service.
- NP extraction works in two steps:
  - It first takes single tokens whose tag contains NN (NN, NNS, NNP, DTNN).
  - It then adds multi-word NPs whose leaves are all nouns, discarding nouns already covered by a larger NP.
- NPs are ordered by pre-order traversal and chained into overlapping pairs (⟨NP1,NP2⟩, ⟨NP2,NP3⟩…). Each pair is a candidate triple.
- The evaluation used two datasets, Geography and Diseases. A sample Diseases ontology has classes such as Cure, Disease, Symptom, Organ and Diagnosis.
- Error shares for Geography and Diseases respectively:
  - parsing errors: 44% / 43%
  - entity-identification errors: 18% / 36%
  - missing semantic analysis (calculations, superlatives, context): 39% / 21%

## Interesting ideas
- An NP counts as a complete unit only if every leaf under it is a noun, and smaller nouns inside it are dropped, which gives a cheap rule for multiword entity spans.
- The query is represented as a chain of overlapping NP pairs plus their linking words, producing "intermediate triples" before any ontology grounding.
- Gaps in a triple, such as a bare preposition like "in", are filled by asking which ontology properties can connect the two grounded classes. The paper notes this can yield several candidates, e.g. isCityOf vs borders.
- Failures are sorted into parser, entity-matching, and reasoning buckets, each reported per dataset.
- The paper flags superlatives like "largest state" as underspecified: the dimension, area or population, has to come from context.

## For Mova

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

- **I1** (tree): The same "all-noun subtree" test could run over compound/flat relations in UD trees to propose multiword entity candidates before frame building.
- **I2** (frames): A pre-grounding triple layer could serve as an explainable halfway point between the UD tree and VerbNet-role frames, where unresolved predicates stay visible.
- **I3** (world): Domain/range constraints from the knowledge graph could propose candidate relations for implicit links like "cities in Texas", with ambiguity reported rather than hidden.
- **I4** (learn): Evaluation reports could attribute every wrong answer to the pipeline stage that broke, so regressions point to the tokens, tree, frames or world stage directly.
- **I5** (answer): When answering, Mova could detect comparatives lacking an explicit measure and either look up a default dimension in compiled measures or ask, instead of guessing silently.

*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

- [Islamic University of Gaza](https://md.research.elamur.ai/free/en/universities/islamic-university-of-gaza/) — University, Gaza, Palestine · 3 works · 109 citations

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## Cite

Cite as: Iyad AlAgha. 2015. Using Linguistic Analysis to Translate Arabic Natural Language Queries to SPARQL. arXiv:1508.01447.

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

```bibtex
@misc{alagha2015using,
  title = {{Using Linguistic Analysis to Translate Arabic Natural Language Queries to SPARQL}},
  author = {Iyad AlAgha},
  year = {2015},
  doi = {10.48550/arxiv.1508.01447},
  eprint = {1508.01447},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1508.01447}
}
```

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## References

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*Updated 1 November 2026*