Designing and Implementing Arabic WordNet Semantic-Based
Hassanin M. Al-Barhamtoshy and Wajdi H. Al-Jideebi
Faculty of Computing and Information Technology, King Abdulaziz University, SA
Abstract
The major aim of this research is to propose, design and implement linguistic foundations for an
English-Arabic dictionary and to explore the demands of this dictionary, to be used in the
international languages, taken into consideration both computation and computerization. The
major focus of analysis, design and implementation, is on the English-Arabic pair. However, the
theoretical model within which this analysis has been made, we believe, can be extended to other
language pairs.
This proposal presents designing and implementing an Arabic WordNet based on semantic. A
relational database is employed to store the lexical and conceptual relations, giving the database
extensibility in Arabic language. The proposed model is extended beyond an Arabic replication
of the word/sense relation to include the morphological and lexical roots and patterns of Arabic.
Consequently, the model investigates the meaning, synonym, antonym, meronym, hypernym,
hyponym, principle, attribute and pertains structures of the Arabic words.
1. Introduction
Accounts of earlier versions of the design are given in [1-3], they include: embedded grammar
tags, natural language interaction on the web [1], facilitating semantic web search with embedded
grammar tags is presented in [2], and dynamic context generation for natural language understanding is
described as a multifaceted knowledge approach.
The EuroWordNet [4-5] approach to multilingual resource development has emphasized the
separate integrity of the dictionaries in different languages and provided an additional bilingual
index to support the search for translations. The effort reported here is on an altogether more
limited scale, and stores the data for different languages in the tables of a single database. Before
we talk about languages, let us remember that the word is the primary component in any text or
language. The best known way to find out a word is to use a dictionary. We can use it to find out
word spell, meaning, synonyms-prefixes and suffixes...etc.
1.1 Introduction to WordNet
WordNet is a lexical database, it provides a large repository of English lexical items, which is
available online. The WordNet was designed to establish relations between the main four types
of Parts of Speech (POS): noun, verb, adjective and adverb [4-6]. The synset represents the
smallest unit in WordNet, which describes a specific meaning of a word. It includes the word
itself, explanation and the synonyms of its meaning.
A specific meaning of one word under one type of POS is called a sense [6]. Each sense of a
word is in a different synset. Synsets are structures containing sets of terms with synonymous
meanings. Each synset has a gloss that defines the concept it represents. As an example, the
words night, nighttime and dark constitute a single synset that has the following gloss [6]: the
time after sunset and before sunrise, while it is dark outside. Synsets are connected to one
another through explicit semantic relations. Some of these relations (hypernymy, hyponymy for
nouns and hypernymy and troponymy for verbs) constitute kind-of and part-of (holonymy and
meronymy for nouns) hierarchies [5, 6]. For one word and one type of POS, if there are more
than one sense, WordNet organizes them in the order of the most frequently used to the least
frequently used [4-6].
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Synonym-substitution algorithms have been developed for the purpose of matching source
vocabulary terms with existing Unified Medical Language System (UMLS) terms during the
integration process in [7].
1.2. Introduction to Arabic WordNet
Arabic WordNet is a lexical database, which is structured along the same structures as the Euro
WordNet [4] and Princeton WordNet [5, 8]. As mentioned in researches, WordNet contains
information about nouns, verbs and adverbs in English. Such WordNet is organized in synset
structure. The synset is a set of words with the same Part-Of-Speech(POS), that can be
interchanged in a manner context. As an example, { ‫ عربة‬, ‫ سيارة‬, ‫ تاكسي‬, ‫ سيارة نقل‬, ... etc} form
Arabic synset because they can be used to share the same meaning. Consequently, synsets can be
related to each other by semantic relations, such as synonymy, antonymy, hyponopmy,
meronymy, … etc., as illustrated in Figure 1.
{ Transport }
{ Vehicle }
{ ‫ سيارة‬Car }
{ ‫} سيارة شرطة‬
{ ‫ عربة‬, ‫ سيارة‬, ‫ تاكسي‬, ‫} سيارة نقل‬
{ ‫} سيارة نقل الموتى‬
Figure 1: Synsets related to {Car ‫} سيارة‬
Each of these synsets is related and linked to other synsets as is illustrated for {Car ‫} سيارة‬,
{Vehicle } and { Transport }. Therefore, all word meanings in a language can be interlinked,
interconnected and constitute a relation network (language-internal relations) or WordNet.
1.3. Arabic WordNet Groups
In Arabic WordNet, we initially worked in five groups: KAU (Saudi Arabia: faculty of
Computing and Information Technology), Cairo University (Faculty of Computer and
Information), Ain Shams University (Faculty of Engineering), Azhar University (Systems and
Computers Engineering Dept.) and RDI groups. The Arabic WordNet lexical database will be
integrated with such groups.
We expect that Arabic WordNet will open up a whole range of new tools, applications and
services in Arabic countries in national translation and cultural translation levels. Also, Arabic
WordNet will give non-native users the possibility to navigate through the vocabulary of
language with new ways. Finally, it will be used in information retrieval, question/ answer
systems, language understanding, expert systems, language modeling, document computing and
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summarizer to automatic translation tools and resources. In this paper, we will use a general
description of the standard database of the WordNet and create a suitable database of Arabic
WordNet.
The following sections design the proposed Arabic WordNet database (section 2). The general
methodology, including database structure of the proposed WordNet is illustrated in section 3.
The WordNet internal relations are described in section 4. The design and implementation of the
proposed model are introduced in section 5, as well as the overall structure. Whereas, section 6
presents conclusion remarks.
2. The Proposed Model Architecture
The proposed model is based on morphological Arabic template grammar [13, 14]. Such
grammar can be applied on the word level (morphological engine).
The morphological engine employs the Arabic template grammar and allows affixes processing
to find out different alternatives of word input [14]. Lexical disruption engine is used to map
between lexical meanings (words). It is important in case of natural language processing and
machine translation [14].
Also, the proposed model is based on interlingua between the used distinction languages.
Equivalence relations between the synsets are made explicit in the so called Inter-Lingual-Index
(ILI) or semantic deep structure.
The model structure consists of languages WordNet which uses the internal semantic deep
structure (inter lingua). The semantic inter-lingua represents inter-relations between languages
WordNet. Figure 2 describes the different modules and their inter-lingua relations.
1. English Word Net, 2. Language Translation Module 3. Arabic Word Net
English Word Net
Dictionary
1 Language
Dependent
Lexicon
2 Lexical
Descriptions
3. Relation Links
Arabic Word Net
Language Translation
Module
ILI Semantic Relation
Relation Rules
1 Language
Dependent
Arabic
Dictionary
2 Lexical
Descriptions
Arabic
Lexicon
3. Relation Links
Figure 2: The Proposed Model Architecture
Both the translation module and the semantic relation can be transferred via the equivalence
relations of the ILI-records to the language-specific meanings, as shown in Figure2. The
semantic relation uses group of language specific lexicons related to the ILI-record concepts.
Such lexicons employ their specific concepts.
Therefore, the main purpose of the semantic relation module is to provide a common framework
for the most important concepts between all the WordNets. It consists of x basic semantic
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distinctions that classify a set of ILI-records representing the most important concepts in the
related WordNets. The next sections will describe the semantic relation and its motivation.
2.1 Experimental Relations
The goal of the proposed model is to integrate with the existing WordNet in the other languages;
like English and Euro WordNet. Therefore, the methodology is based on using existing
WordNets, and integrating such WordNets with the Arabic WordNet.
The proposed model experiments this process on sequences of string matching and string
manipulation stages as in Figure (3).
Stage 1:
Search and/or match for
Arabic word in the standard
translation dictionary
Translate the Arabic
word to translated
equivalent word in
other languages
Stage 2:
Apply morphological
analysis to find Arabic root
and associated features
Find the equivalent
Synsets for the other
languages
Stage 3:
Search and/or match for
Arabic root in the standard
translation dictionary
Stage 4:
Generate Arabic Synset
Languages
Dictionary
English and
Euro
WordNets
Group of Synsets
in different
languages
Match and link
between equivalent
Synsets
Figure 3: Overall Flow for Processing Arabic WordNet relative to other Languages
The first two stages serve to filter out language terms that can be found in other WordNets. Stage
1 uses conventional matching techniques to find an exact string in other languages (using
languages dictionary).
The second stage uses morphological rules and Arabic rules to find root(s) and associated
features. However, such stages can only be made by a domain expert, and our concern here is
primarily on the results of the synsets stages: stages 3 and 4.
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2.2 Semantic Relations
The proposed model will describe the previous modules in more details, but we summarize here
in order to provide clear picture of the events.
The ILI is a list of meaning, taken from WordNet. Each ILI record consists of a synset,
specifying the meaning and the reference to its source.
Two separate language independent links are linked to ILI records:
1. Top Ontology, which contains concepts, semantic distinctions, such as Object, Substance,
Location, Dynamic and Static.
2. Domain Ontology, which includes group of meanings in terms of topics or scripts, such as
Traffic, Road Traffic, Air-Traffic, Sports, Hospital and Restaurant.
Any word can belong to multiple synsets, as the following format:
Index Word:
[Word] [POS] : meaning + ; “example”+ ;
Where:
POS:
[Verb] | [Noun] | [Adjective] | [Adverb]
+: more than one time
WordNet is focused on relationships between synsets, and verbs can be related to verbs, nouns to
nouns, … etc. The semantic relationships available in WordNet are as the following:
1. All parts of speech
a. Synonymy ‫الترادف‬: the words that have similar meanings.
b. Antonymy ‫التضاد‬/‫التنافر‬: the words that have opposite meanings.
c. Glossary: is used to store a gloss for every synset.
d. Similar : connects synsets that have similar meaning.
2. Verb only
a. Troponymy ‫المجاز‬: is a semantic relation of doing something in the manner of something else.
b. Entailment ‫االستتزاا‬: is a relationship between verbs doing something that requires doing
something else.
c. Principle: defines and arranges the relation between verbs and adjectives.
3. Nouns only
a. Hypernymy ‫االشتتما‬: refers to a hierarchical relationship between words. (e.g. furniture >
chair)
b. Hyponnymy ‫التضمين‬: is the opposite of hypernamy.
c. Meronymy ‫االشتقاق‬: is part/whole relationship. ( e.g. paper > book)
d. Attribute: describes the relation between noun and adjective synsets.
4. Adjectives only
a. Participle: defines and arranges the relation between verbs and adjectives.
b. Pertain‫تعزّق‬: describes a lexical relation between two words.
c. Attribute: describes the relation between noun and adjective synsets.
5. Adverbs only
a. Pertain: describes a lexical relation between two words.
As stated in MT translation, the interlingua (ILI) has well known advantages:
1. New language can be integrated without equivalent considerations and relations for other
languages.
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2. The ILI can be adapted as a central resource to make matching more efficient and precise.
The equivalence relations of the ILI-records can be used by the Top and Domain Ontology to the
language specific meanings, as shown in Figure 2. The Top and Domain ontology can be further
inherited by all other related language-specific concepts. Consequently, the main purpose of the
Top ontology “is to provide a common framework for the most important concepts in all
WordNets” [9].
The Top Ontology consists of “63 base semantic distinctions that classify a set of 1300 ILIrecords representing the most important concepts in the different WordNets [9].
2.3 Arabic WordNet Synsets
Most synsets are connected to other synsets via a number of semantic relations (based on word
types), and include [5, 8, 9]:
 Nouns
o hypernyms: Y is a hypernym of X if every X is a (kind of) Y
o hyponyms: Y is a hyponym of X if every Y is a (kind of) X
o coordinate terms: Y is a coordinate term of X if X and Y share a hypernym
o holonym: Y is a holonym of X if X is a part of Y
o meronym: Y is a meronym of X if Y is a part of X
 Verbs
o hypernym: the verb Y is a hypernym of the verb X if the activity X is a (kind of) Y
(travel to movement)
o troponym: the verb Y is a troponym of the verb X if the activity Y is doing X in
some manner (lisp to talk)
o entailment: the verb Y is entailed by X if by doing X you must be doing Y
(sleeping by snoring)
o coordinate terms: those verbs sharing a common hypernym
 Adjectives
o related nouns
o participle of verb
 Adverbs
o Pertain: root adjectives
The morphology functions of the software distributed with the database try to deduce the lemma
or root form of a word from the user's input; only the root form is stored in the database unless it
has irregular inflected forms.
3. Database Design of Arabic WordNet
The structure of the Arabic WordNet database is based on the layout structure of the Princeton
WordNet. Therefore, the main semantic relation of the WordNet (Synset) will be taken into our
consideration. However, some specific changes will be made to the database design, which are
devoted to the following objectives:




to reuse the existing resources of WordNet;
to create multilingual databases [9];
to be used in MT (language specific relations in WordNet);
to add an equivalent relation for each synset with respect to Euro WordNet.
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The database relations can be made across part-of-speech. WordNet maintains a strict division
between different part-of-speeches.
3.2.1 Synonym Structure
The first main file is synonym, which stores the synset information of the WordNet corpus. The
first column represents SynSetID with 7 digit length, indicating to which synset the word
belongs. As discussed in many researches [5, 8, 9, 10], words belonging to the same synset are
synonyms. For example, synset starting with 1225000 identifies an Arabic synset ( ‫القبتتو‬:
acceptance). The words in a synset are numbered serially, starting with one. Therefore, the
second column represents Arabic synset (may be code or word). The third column is the Arabic
word itself (morphological pattern). The fourth column stores the synset category. The synset
categories – in Arabic WorldNet- are limited to verbs, nouns, adjectives, and characters. There
are no any pronouns, prepositions, conjunctions or interjections. Figure 4 shows the proposed
Arabic synonym file structure.
SynSetID
1225001
1225002
Synset
‫ القبو‬/ acceptance
‫ القبو‬/ acceptance
Arabic Word
‫أجاب‬
‫أقر‬
Category (Type)
verb / ‫فعل‬
verb / ‫فعل‬
Figure 4: The proposed Arabic synonym file structure
It may be needed to add last column, which indicates how common a word is in relation to a text
in a test corpus. Consequently, the higher is the number, the more common the word.
WordNet also provides the polysemy count of a word: the number of synsets that contain the
word. If a word participates in several synsets (i.e., has several senses) then, typically some
senses are much more common than others. WordNet quantifies this by the frequency score: in
which several sample texts have all words semantically tagged with the corresponding synset,
and then a count provided indicates how often a word appears in a specific sense.
3.2.2 Glossary Structure
The second file is used to store a gloss for every synset. Therefore, this file may contain an
explanation, definition and example of Arabic sentences. The structure of this file contains
SynSetID, the second column allocates a gloss in an array structure, as shown in figure 5.
SynSetID
Glosses (Gloss [ ] )
1225001
“‫”أجاب الدعوة‬
1225002
"‫"أقر المعاهدة‬
1225012
"‫"سزم باألمر الواقع‬
Figure 5: Arabic Glossary File Structure
2.2.3 Antonym Structure
While semantic relations apply to all members of a synset, because they share a meaning but are
all mutually synonyms, words can also be connected to other words through lexical relations,
including antonyms (opposites of each other in meaning) and derivationally related, as well.
Consequently, Antonym file is used to store all relations between words that are antonyms. So
antonym is a lexical relation, it relates two words no two synsets. The antonym file structure is
shown in Figure 6.
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SynSetID1
SynSetID2
1225001
1225112
Figure 6: Arabic Antonym File Structure
3.2.4 Hypernym and Hyponym Structure
Hypernym is a relation between synsets, and it is a semantic relation. The hypernym of a
hypernym of a word is also a hypernym of the word (hypernym chain). The hypernym file
consists of two columns SynSetID1 and SynSetID2, see Figure 7.
SynSetID1
SynSetID2
Figure 7: Arabic Hypernym File Structure
3.2.5 Hyponym Structure
The hyponym is the reversing structure of the hypernym structure (i.e., SynSetID1 becomes
SynSetID2 like Figure 7.
3.2.6 Similar Structure
The structure of this file connects synsets that have similar meaning. The structure consists of
two synsets IDs as arguments, it is like hypernym structure.
3.2.7 Meronym Structure
Sometimes a meronym relation is called part/whole relation. This relation is only applied in
nouns. An Arabic word A is a meronym of another Arabic word B if A is-a-part of B. The
structure of meronym consists of two synsets IDs as arguments, it is like hypernym structure.
This structure is explained in the original Prolog documentation of WordNet [8,10].
3.2.8 Attribute Structure
This structure describes the relation between noun and adjective synsets. An attribute is a noun
that has values and is described by adjectives. For example, the noun (‫ حجتم‬size) is an attribute
with the values (‫ قزيتل‬little), (‫ صتيير‬small), (‫ ضت م‬big( and (‫ كبيتر‬large). Therefore, the relation
between nouns and adjectives are defined by attribute operation, and it is similar to antonym,
hypernym, hyponym and similar structures. Consequently, the synset 2225009- as an examplecontaining the noun (‫ وزن‬weight) is an attribute relation to the adjectives of the synset 311008,
containing the word (‫ خفيف‬light). Therefore, the attribute relation between nouns and adjectives is
semantic.
3.2.9 Principle Structure
This structure defines and arranges the relation between verbs and adjectives. The operation of
this structure arranges the participle relation and therefore describes the verb-adjective semantic
relation. The past participle adds “‫ ”و‬to the root of the verb, e.g., the verb (‫ فعتل‬did) becomes the
adjective ( ‫)فعتو‬. The structure of the participle is composed of four arguments to specify two
words, as shown in the following Figure 8.
SynSetID1
Word1
SynSetID2
Word2
Figure 8: Principle Structure
3.2.10 Pertain Structure
This structure describes a lexical relation between two words. It indicates where a word pertains
to another word. The structure defines two words by their synset ID and word number.
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The lexical indicator for the first word uses the first two columns; it can be an adjective or an
adverb. If the first word is an adjective- as a POS, then the second word must be noun or another
adjective. As an example, the first two columns could refer to the adjective lexical word (‫)يوميتا‬,
which pertains to noun ( ‫)يتو‬, defined by the third and fourth columns. If the first two columns
describe an adverb, therefore the assigned second two columns are the adjective.
Lexical Word1
Lexical Word2
SynSetID1
Word1
SynSetID2
Word2
3222010
‫ يوميا‬/ adjective
2111015
‫ يو‬/ noun
3222011
‫ أسبوعيا‬/ adjective
2213009
‫أسبوع‬/ noun
4123009
‫مرتفعأ‬/ adjective
3123011
‫مرتفع‬/ adjective
Figure 9: Pertain Structure
3.3 Database Entity Types
Euro WordNet makes a functional difference between 3 types of entities [9,10,11,12]:
1. First Order Entity: Any concrete entity perceivable by senses can be represented in 3-D space. (e.g.,
object, animal, plant, man, woman, instrument).
2. Second Order Entity: Any static or dynamic situation, which cannot be seen, felt, grasped, heard as an
independent physical thing. They can take place in time rather than exist (e.g., be, cause, move,
continue, occur, apply).
3. Third Order Entity:Any unobservable proposition independently of time and space, and it can be true
or false or real. It can be remembered, forgotten, asserted or denied (e.g., idea ‫فكرة‬, thought ‫تفكير‬, theory
‫نظرية‬, plan ‫ت طيط‬, information ‫معزومات‬, intention ‫مقصد‬, goal ‫)هدف‬.
4. The WordNet Internal Relations
In WordNet and Euro WordNet, the most important relation is synonymy, which is already
implicit in the notion of a synset. As an example, Figure 10 gives the relations encoded in Win
1.5 together with examples for various POS.
Relation
ANTONYM
HYPONYM
MERONYM
ENTAILMENT
TROPONYM
CAUSE
DERIVED FROM
ATTRIBUTE
SIMILAR TO
PARTICIPLE
POS
noun / noun; verb / verb; adjective / adjective
noun/noun
noun/noun
verb/verb
verb/verb
verb/verb
adjective/adverb
noun/adjective
adjective/adjective
adjective/verb
Example
man/woman; enter/exit
slicer/knife
head/nose
buy/pay
walk/move
kill/die
beautiful/beautifully
size/small
ponderous/heavy
elapsed/ elapse
Figure 10: The Relations Encoded in Win 1.5 together with Examples for various POS
The POS contains :N = noun, V = verb, Adj = Adjective, Adv= Adverb, and PN = pronoun or name.
5. Designing and Implementing Arabic-English WordNet
One way to construct a bilingual WordNet is to have new tables constructed to manipulate both
WordNet synsets and Arabic words, roots and patterns, as well as via synonym, hyponymy and
WordNet relations. Therefore, the morphological analyzer takes place to analyze each Arabic
word, according to Arabic Template Grammar [13, 14, 15, 16-18].
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Figures 11 and 12 illustrate the overall flow processing of the Arabic word "‫ "قارئ‬and therefore,
the translated meaning in English is "reader".
Figure 11: Overall Flow for Translating Arabic Words "‫ "قارئ‬and "‫ "م طط‬to other Languages
Figure 12: Overall Flow for the Translated Word "reader" to the related synsets
6. Conclusion
Arabic WordNet is designed to be especially easy to use; it has the simple structure of WordNet
and its underlying representation is based on natural language fragments. Motivated by the range
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of concepts available in Arabic dictionaries commonsense knowledge base, the content of Arabic
WorNet reflects a far richer set of concepts and semantic relations than those available in
standard dictionaries.
This proposal has designed and implemented an Arabic WordNet based on multilingual
dictionary and semantic relation of standard WordNet. A relational database was employed to
store the lexical and conceptual relations, giving the database extensibility in Arabic language.
The proposed model is extended beyond an Arabic replication of the word/sense relation to
include the morphological and lexical roots and patterns of Arabic.
We hope that this paper has encouraged the reader to consider using Arabic WordNet within
their own projects, and to discover the benefits afforded by such large scale semantic resources
like in [19-25].
Acknowledgements
I would like to thank IT deanship for support, guidance and insights that have contributed
tremendously to this paper. Also we would like to acknowledge Basil A. Ba-Aziz and Seef AlHarthi for their programming efforts. Many thanks to: WordNet Princeton, for using some results of
their research papers. This work has been supported, in part, by KAU Award # MT- 01-01-14230.
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