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The Arabic Ontology -- An Arabic Wordnet with Ontologically Clean Content

Summary

The paper describes the Arabic Ontology, a formal Arabic wordnet. It sets itself apart from WordNet-style resources, which classify concepts by what native speakers would accept as "B is a kind of A." Jarrar argues that this speaker-intuition criterion produces errors. Examples from WordNet 3.1 include redundant implied hypernym links, a synonymy claimed between imaginary and complex numbers, a flat hierarchy of number types, the Morning Star and Evening Star treated as distinct stars, and Islamic months filed under Gregorian months. The Arabic Ontology instead grounds its definitions and taxonomy in ontological analysis, scientific knowledge and expert consensus, and it follows a strict method for writing glosses. Its top levels are aligned with BFO, DOLCE and DOLCE+DnS. In a coverage experiment, roughly 90% of the tested Arabic concepts could be placed under existing nodes. The resource is mapped to Princeton WordNet, Wikidata and many Arabic lexicons, and it is served through a lexicographic search portal. It matters because it is a precision-oriented lexical-semantic backbone for Arabic NLP and linked data. The author notes it is still closer to a taxonomy than to a richly axiomatized ontology.

Key points

  • Size: about 1,300 thoroughly investigated concepts, plus about 11,000 partially validated ones drawn from specialized glossaries.
  • Coverage: about 90% of the concepts in the evaluation could be classified under the ontology's top-level nodes.
  • Access: the portal at ontology.birzeit.edu also hosts about 150 Arabic-multilingual lexicons, which were digitized, cleaned, and stored in a normalized schema.
  • Mapping: 13,445 manual mappings in total.
    • 11,400 SameAs
    • 1,050 SubClassOf/SuperClassOf
    • 770 InstanceOf/Type
    • 125 Similar
    • 100 PartOf/HasPart
  • Linked data: the mappings are published in the W3C Lemon model as RDF. The concepts rely on LDC SAMA lemmas, which should make future links to Arabic corpora straightforward.

Interesting ideas

  • I1. Benchmark against experts, not speakers. A taxonomy edge is accepted only if it agrees with scientific or expert classification, which avoids wrong is-a links such as "Islamic month is-a Gregorian month." — For Mova (world): When compiling the ontology layer from WordNet/Wikidata, flag hypernym edges that clash with a domain authority, such as a units table or a number hierarchy, so the world model does not inherit naive errors.
  • I2. Prune links that can be inferred. A direct is-a link that already follows from a transitive chain adds no information and should be removed. — For Mova (world): A pruning pass over imported hypernym graphs would keep the compiled knowledge minimal and make the reasoning traces behind explanations shorter.
  • I3. One instance, several names. Different senses that describe the same individual on different occasions, like Morning Star and Evening Star for Venus, should be linked to one instance rather than split into two concepts. — For Mova (frames): Coreference could consult an "alternative designation of the same instance" relation when resolving descriptive names in text to a single entity.
  • I4. Events, not verbs, as concepts. A verb and its verbal noun (learn/learning, drink/drinking) are treated as expressions of one event concept, because "verb" is only a linguistic category. — For Mova (frames): Map verb frames and their nominalizations to a single event node, so "the drinking of water" and "drank water" yield the same who-did-what.
  • I5. Mappings carry precision and confidence. Each mapping is a tuple <e1, e2, relation, precision, confidence>, so weak or doubtful links can be documented and later excluded or fixed. — For Mova (learn): Silver data and cross-resource links could store both scores, letting training filter out low-confidence alignments and letting evaluation use them as negative controls.

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: Linguistics & languages

Cited by

26 papers of our library cite it (of the 140884 whose bibliographies we read); 5 of them have a page here, 21 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.3233/ao-200241 · arXiv 2205.09664

@misc{jarrar2022arabic,
  title = {{The Arabic Ontology -- An Arabic Wordnet with Ontologically Clean Content}},
  author = {Jarrar, Mustafa},
  year = {2022},
  doi = {10.3233/ao-200241},
  eprint = {2205.09664},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.3233/ao-200241}
}

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