# Lexical Diversity in Kinship Across Languages and Dialects

Hadi Khalilia
DISI
University of Trento
Trento, Italy
<EMAIL_ADDRESS>
Gábor Bella
Lab-STICC CNRS UMR 628
IMT Atlantique
Brest, France
<EMAIL_ADDRESS>
Abed Alhakim Freihat
DISI
University of Trento
Trento, Italy
<EMAIL_ADDRESS>
Shandy Darma
DISI
University of Trento
Trento, Italy
<EMAIL_ADDRESS>
Fausto Giunchiglia
DISI
University of Trento
Trento, Italy
<EMAIL_ADDRESS>

###### Abstract

Languages are known to describe the world in diverse ways. Across lexicons,
diversity is pervasive, appearing through phenomena such as lexical gaps and
untranslatability. However, in computational resources, such as multilingual
lexical databases, diversity is hardly ever represented. In this paper, we
introduce a method to enrich computational lexicons with content relating to
linguistic diversity. The method is verified through two large-scale case
studies on kinship terminology, a domain known to be diverse across languages
and cultures: one case study deals with seven Arabic dialects, while the other
one with three Indonesian languages. Our results, made available as browseable
and downloadable computational resources, extend prior linguistics research on
kinship terminology, and provide insight into the extent of diversity even
within linguistically and culturally close communities.

_Keywords_ multilingual lexicon $\cdot$ dialect $\cdot$ language diversity
$\cdot$ lexical gap $\cdot$ kinship $\cdot$ lexical typology

##  1 Introduction

The culture and the social structure of a community are reflected in the
language spoken by its members. One of the most salient examples of this
phenomenon is the worldwide diversity of terms used to describe family
structures and relationships. While, thanks to studies such as Murdock,
(1970), kin terms around the globe are generally well documented, many local
variations—across dialects of a single language or across languages of a
single country—have not yet been fully described or understood. For example,
the term مَعزوزي maazoozi in the Algerian Arabic dialect, meaning younger
brother, does not have any equivalent term in the Gulf Arabic dialect. In
contrast, the Gulf word ابن العُود ibn alood meaning elder brother does not
exist in Algerian, which instead uses the word سِيدي siedi.

Beyond a linguistic or anthropologic interest, the availability of digital
resources on language diversity is also desirable from a computational
perspective. Language processing applications need to be aware of such
phenomena of diversity in order to provide high-quality results. For example,
a machine translation system needs to tackle cases of lexical
untranslatability where a word or expression in a source language has no
equivalent in a given target language, and the choice of an approximate
translation can change the meaning of an utterance. For example, for the
English sentence his cousin gave birth to a twin, Google Translate provides
the Arabic translation أنجب ابن عمه توأما a’njaba ibna a’mihi tawaman that
means His father’s brother’s son gave birth to a twin. This syntactically
correct yet unintended meaning of a male giving birth output is due to a
_lexical gap_ , i.e. a nonexistent equivalent Arabic term for _cousin_. Such
cases of _techno-linguistic bias_ —where language technology provides better
results _by design_ in certain languages than in others—tend to remain hidden
in monolingual resources but are revealed in multilingual settings (Bella et
al.,, 2023; Bella et al., 2022a, ).

In recent years, there has been an increasing number of linguistic databases
covering a large number of languages. These resources are usually aimed at
quantitative studies for comparative linguistics, such as the classification
of pain predicates (Reznikova et al.,, 2012), a semantic map of motion verbs
(Wälchli and Cysouw,, 2012), the modeling of color terminology (McCarthy et
al.,, 2019), the CLICS database of cross-linguistic colexifications (Rzymski
et al.,, 2020), DiACL (Diachronic Atlas of Comparative Linguistics), a
database for ancient Indo-European languages spoken in Eurasia typology
(Carling et al.,, 2018), or the Cross-Linguistic Database of Phonetic
Transcription Systems (Anderson et al.,, 2018). Often, such databases use
phonetic representations of lexical units or are limited to a few hundred or a
few thousand core concepts, limiting their usability for the processing of
contemporary written language. In our experience, most of the existing
typology-informed NLP research is restricted to exploring language-specific
morphosyntactic features and has ignored diversity within lexical resources
(Batsuren et al.,, 2022). A notable exception is the Universal Knowledge Core,
a massively multilingual lexical database that explicitly represents
linguistic diversity and that we reuse in our work.

Our research is part of the _LiveLanguage_ initiative, the overarching
objective of which is to create, publish, and manage language resources that
are “diversity-aware”—i.e. that reflect the viewpoints of multiple speaker
communities—and that can be reused by multiple communities: linguists,
cognitive scientists, AI engineers, language teachers and students (Bella et
al.,, 2023). Contrary to mainstream exploitative practices, LiveLanguage aims
to carry out its goals while empowering local speaker communities, giving them
control over resources they help to produce (Helm et al.,, 2023). Involving
human contributors and deciders from speaker communities is therefore a
crucial part of our methodology.

In particular, the present paper focuses on diversity where it is less
expected to appear: within dialects of the same language and within languages
of the same country. Therefore, we describe a multidisciplinary study on the
diversity of kin terms across seven Arabic dialects (Algerian, Egyptian,
Tunisian, Gulf, Moroccan, Palestinian, and Syrian) and three languages from
Indonesia (Indonesian, Javanese, and Banjarese). We consider kin terms as a
domain particularly well-suited both for research on the methodology of
collecting and producing diversity-aware linguistic data, and for comparative
studies on diversity across languages.

Our paper aims to provide four contributions: (1) a general method for
collecting multilingual lexical data from native speakers for a given domain
(in our case the domain of kin terms), in a diversity-aware manner; (2) 223
kin terms and 1,619 lexical gaps collected in seven Arabic dialects and three
Indonesian languages; (3) a qualitative and quantitative discussion of our
results regarding the diversity observed across the dialects and languages
covered; and (4) the publication of our results as an open, computer-
processable dataset, as well as its integration into the Universal Knowledge
Core multilingual database. Our starting point is state-of-the-art datasets on
worldwide kinship terminology from ethnography (Murdock,, 1970) and
computational linguistics (Khishigsuren et al.,, 2022). Our data collection
method is based on collaborative input from native speakers and language
experts. Our results extend the state-of-the-art resources above with kin
terms in languages and dialects not yet covered, as well as with 22 new
kinship concepts not yet associated with other languages within those
resources.

The structure of the paper is organized as follows. In Section 2 we give an
overview of lexical typology and the phenomena of lexical untranslatability
and lexical gaps with respect to the domain of kinship in particular. The
Universal Knowledge Core resource is presented in Section 3. In Section 4 we
describe our data collection method. Sections 5 and 6 introduce two case
studies on Arabic dialects and Indonesian languages, respectively. Section 7
discusses previous studies related to our work. Finally, we provide
conclusions in Section 8.

##  2 Untranslatability and Lexical Typology

Linguists understand translation from one language to another as a complex and
multidimensional problem, ranging from multiple coexisting forms of meaning
equivalence to untranslatability (Catford,, 1965; Bella et al., 2022a, ). The
diversity between cultures is a major cause for this problem appearing on
several lexical-semantic levels. Some examples of the linguistic diversity are
the richness of Toaripi vocabulary on the various forms of motion verbs
describing walking around the beach like (isai) meaning “go beachward” and
(kavai) meaning “go inland with respect to the beach”, the language of the
coastal Papua New Guinea country, the lack of vocabulary for the word meaning
“sailing” in Mongolian, which is the language of a landlocked country, or the
Arabic word تَسنَّم meaning “to ascend a camel’s hump”.

The domain of kinship terms, which is the subject of our paper, is known to be
extremely varied across languages, due to the different ways family structures
are organised around the world. Matriarchal societies may describe certain
female relatives with more detail, while strongly patriarchal ones are more
descriptive with respect to male relatives. Arabic dialects, for instance,
distinguish paternal and maternal brothers but also blood brothers, full
brothers, and breastfeeding brothers. Thus, not only are kinship-related
vocabularies ‘richer’ or ‘poorer’ across languages, they are also structured
in different manners.

In this research, we focus on lexical untranslatability, which manifests most
clearly through the lexical gap phenomenon when a word in a source language
does not have a concise and precise translation in a given target language.
Lexical gaps are often the linguistic manifestation of culturally or spatially
defined specificities of a community of language speakers that cannot entirely
be predicted or explained through systematic principles or recurrent patterns
(Lehrer,, 1970). Table 1 below presents this phenomenon for nine concepts
representing sibling relationships from the kinship domain in eight
languages111These nine concepts do not cover sibling terms exhaustively in all
languages: for example, many Austronesian languages use different terms based
on the gender of the speaker.. One can observe that none of the eight
languages has concise lexicalizations for all nine concepts, yet each concept
is lexicalized in at least one language. Such variations in lexicalization
pose a problem for both machine and human translation: for instance,
substituting a specific term instead of a broader one may result in injecting
unintended meaning. In Javanese, at least four specific
terms—(sedulur/sibling), (adhi/younger sibling), (kangmas/elder brother), and
(Mbakyu/elder sister)—are used for expressing the sibling relationship, and
accordingly, translating this sentence through Google Translate (my sister is
ten years older than me) to Javanese gives this nonsensical sentence (adhiku
luwih tuwa sepuluh taun tinimbang aku) meaning (my younger sibling is ten
years older than me). This result is due to the lack of Javanese vocabulary
for the word meaning (sister), and also lacks the term meaning “younger
sister”, so the machine translator uses (adhi) meaning “younger sibling,”
which finally produces the semantically absurd output.

Table 1: Lexicalizations of nine meanings around the concept of (sibling) in
eight languages.
Meaning | English | Japanese | Arabic |  Italian |  Indonesian | Hindi | Hungarian | Javanese
---|---|---|---|---|---|---|---|---
sibling | sibling | GAP | GAP |  GAP |  saudara |  shodr | testvér | sedulur
elder sibling | GAP | GAP | GAP |  GAP |  kakak | GAP | nagytestvér | GAP
younger sibling | GAP | GAP | GAP |  GAP |  adik | GAP | kistestvér | adhi
brother | brother | GAP |  أخ |  fratello |  GAP |  ByA | GAP | GAP
sister | sister | GAP |  أُخْت |  sorella |  GAP |  bhn | GAP | GAP
elder brother | GAP | あに | GAP |  fratellone |  abang |  ByA | báty | kangmas
elder sister | GAP | あね | GAP |  sorellona |  GAP |  dFdF | nővér | mbakyu
younger brother | GAP | おとうと | GAP |  fratellino |  GAP |  BAI | öcs | GAP
younger sister | GAP | いもうと | GAP |  sorellina |  GAP |  bhn | húg | GAP

Lexical typology is a field of linguistics that studies the diversity across
languages according to the structural features of languages with respect to
specific semantic fields (Plungyan,, 2011). Different classical studies are
conducted in this field on grammar and phonology, such as VoxClamantis V1.0— a
large-scale corpus for phonetic typology (Salesky et al.,, 2020) and the
structure of the space semantic field by identifying a set of semantic
parameters and notions depending on the grammatical information of the field’s
constituents (Levinson and Wilkins,, 2006). Other examples of such studies
have been conducted on lexical-typological issues that appear across languages
during translation, like the presence or absence of lexicalizations in
languages. In these articles, authors focused on semantic fields that offer
the richness of cross-lingual diversity: family relationships (Kemp and
Regier,, 2012), colors (Roberson et al.,, 2005), food (Bella et al., 2022b, ),
body parts (Wierzbicka,, 2007), putting and taking events (Kopecka and
Narasimhan,, 2012), cutting and breaking events (Majid et al.,, 2007), or
cardinal direction terms (Arora et al.,, 2021). However, as mentioned in the
introduction, only a few open datasets have been published in the scientific
research area. These include the classification of kinship by Murdock, (1970),
which has been published in D-PLACE (Kirby et al.,, 2016). Part of Kay and
Cook, (2016)’s work on colors is published under the lexicon chapter of the
World Atlas of Language Structures (WALS) (Dryer and Haspelmath,, 2013).
Additionally, a color categorization dataset by McCarthy et al., (2019) is
available on GitHub222https://github.com/aryamccarthy/basic-color-terms.

Digital lexicons have been increasingly used in lexical typology, enabling
typologists to explore a broader range of languages and semantic domains. One
noteworthy example is the KinDiv333http://ukc.disi.unitn.it/index.php/kinship
lexicon (Khishigsuren et al.,, 2022), which encompasses 1,911 words and
identifies 37,370 gaps within the domain of kinship, spanning 699 languages.
In our current research, we extend our investigation into the kinship domain,
specifically focusing on exploring linguistic diversity among Arabic dialects
and Indonesian languages. Other examples include Viberg, (1983)’s seminal
study, which was conducted on perceptual terminology in 50 languages and has
been expanded upon by Georgakopoulos et al., (2022) to cover 1,220 languages.
Furthermore, the Kinbank database, recently introduced by Passmore et al.,
(2023), serves as a comprehensive repository of kinship terminology,
encompassing more than 1,173 languages and offering a broad coverage of
various kinship subdomains.

##  3 Universal Knowledge Core

This section describes the Universal Knowledge Core
(UKC)444http://ukc.datascientia.eu, a large multilingual lexical database that
we adopt for the production of diversity-aware datasets in this research
(Giunchiglia et al.,, 2017). The use of the UKC is motivated by its ability to
represent linguistic unity and diversity explicitly: conceptualisations shared
across languages, word senses appearing only in certain languages, shared
lexicalisations (e.g. cognates), as well as lexical gaps. The theoretical
underpinnings of the lexical model of the UKC have been described in
Giunchiglia et al., (2018) and in Bella et al., 2022b , and are illustrated in
Figure 1.

Figure 1: Structural elements in the UKC lexical database

The UKC is divided into a supra-lingual concept layer (as shown at the top of
Figure 1) and the layer of individual lexicons (at the bottom of Figure 1).
The concept layer includes hierarchies of concepts that represent lexical
meaning shared across languages. Concepts are language-independent units and
act as bridges across languages, and each one should be lexicalized by at
least one language to be present in the concept layer. Supra-lingual concepts
and their relations (e.g. hypernymy, meronymy) are in part derived from third-
party resources such as Princeton WordNet (PWN) (Miller,, 1995), and are in
part proper to the UKC. In particular, the UKC contains an extensive formal
conceptualisation of kinship domain terms computed from the KinDiv database,
spanning about 200 distinct concepts.555https://github.com/kbatsuren/KinDiv
KinDiv itself is based on ethnographic evidence from 699 languages
(Khishigsuren et al.,, 2022). While this existing hierarchy of kinship
concepts does not fully cover all terms that appear in our study, it is the
most complete one we are aware of, motivating our choice of the UKC as a
platform for our research.

The lexicon layer consists of language-specific lexicons that provide
lexicalizations for the concepts from the supra-lingual concept layer, while
also asserting _lexical gaps_ whenever lexicalizations are known not to exist.
Lexicons also provide term definitions as well as lexical relationships
specific to the language, such as derivations, metonymy, or antonymy
relations. Lexicons can also contain _language-specific concepts_ that do not
appear in the supra-lingual concept layer. For example, in Figure 1, the
Arabic شَقِيقة, meaning “a female person who has the same father, mother, or
both parents as another person”, is represented as a language-specific
concept. The dual mechanism of defining lexical concepts either on the supra-
lingual or on the language-specific level allows for the representation of
differing worldviews that would be hard or impossible to reconcile into a
single global concept graph. The richness of its lexicon-level linguistic
knowledge makes the UKC unique among multilingual lexical databases and
particularly suitable for our study.

As mentioned in Section 2, a lexical gap for a specific concept is present in
a language if there is no concise equivalent word meaning for the concept in
that language. For example, neither English nor Arabic has a word meaning
_elder sibling_ ; for such cases, the UKC provides evidence of meaning non-
existence and untranslatability by representing lexical gaps inside lexicons,
as shown in Figure 1. This information can be used by the NLP community to
indicate the absence of equivalent words to downstream cross-lingual
applications.

Beyond providing lexical relations between shared word meanings as other
multilingual lexical databases do, the UKC also represents a richer set of
lexical-semantic connections between language units in a lexicon. For example,
the antonym lexical relation expresses that two senses are opposite in
meaning. While the lexical-semantic relation, similar-to, is used to connect
two concepts with similar meanings, and the hypernym-of connects parent
meaning with its child. For instance, in Figure 1, the English (little
brother) and (brother) are connected through a hypernym-of relationship. Such
information can be used by the NLP community to indicate the concise
equivalent language-specific word meaning to downstream cross-lingual
applications, e.g., as the position of a language-specific meaning in a
language hierarchy in a lexicon.

The UKC currently does not explicitly distinguish between languages and
dialects: each vocabulary is a separate entity labeled with a standard three-
letter ISO 639-3 code. When such a code is not available, the UKC uses a
standard extension mechanism where three additional (not standardised) letters
are added to the ISO code: e.g., for Syrian Arabic, the code arb-syr is used.

##  4 A Methodology for Building Diversity-Aware Lexicons

This section presents the general method by which we collected and produced
lexicalizations and gaps from native speakers and language experts. The same
method presented below was employed in an independent manner for each Arabic
dialect and Indonesian language covered by our study. The contents of this
section aim to serve as a tried and tested recipe for gathering lexical data
in a diversity-aware manner, that we intend to reuse in future lexicon
development projects.

We exploit the UKC to import language-independent concepts (e.g., kinship
concepts) to be used as an input dataset to our method and use its data
representation model to formalize our data. We reuse an already broad and
well-formalised hierarchy of 184 kinship concepts from the KinDiv database,
which includes kinship terms and gaps in 699 languages. Data in KinDiv is
based on the well-known results of Murdock, (1970), as well as on
lexicalizations retrieved from Wiktionary that we consider as an overall good-
quality resource. In Khishigsuren et al., (2022), the accuracy of KinDiv was
evaluated to be above 96%. One language expert per language provided this
percentage, which represents the proportion of the number of words (or gaps)
validated as correct to the total number of collected words (or gaps).

Our work extends KinDiv data by new concepts, lexicalizations, and lexical
gaps in languages and dialects that are either not present in KinDiv or are
incompletely covered. A lexical-semantic expert generates a contribution
(kinship terms and gaps) task, then a group of native speakers collects
contributions from a dialect (and a local language). After that, two steps for
validating collected contributions: language experts evaluate collected
lexical units and gaps of a dialect, and a lexical-semantic expert evaluates
explored kinship concepts (not existing in UKC). Additionally, resulting data
(including gaps, words, and new concepts) is used to update and enrich UKC.
So, gaps and words are merged into the lexicons of the UKC while new concepts
are integrated with the (top) concept layer. A general view of the method is
depicted in Figure 2.

Figure 2: Methodology macro-steps and data sources

Accordingly, the macro-steps of our methodology are as follows:

  1. 1.

Contribution task generation: First, prepare the materials: the dataset of
inputs to be examined and the architecture of the supra-lingual concept layer
of each subdomain.

  2. 2.

Contribution collection: The actual contribution effort is carried out by a
native speaker in a local language or dialect.

  3. 3.

Lexicon-level validation: Provided words and gaps are evaluated and corrected
by a language expert.

  4. 4.

Concept-level validation: New concepts and unclear contributions (i.e., words
on the borderline) are verified by a lexico-semantic expert.

###  4.1 Contribution task generation

This section describes the material needed during the execution of the next
steps of the methodology. Hence, two constituents must be prepared in this
step as described below:

  1. 1.

Dataset of inputs: Constructing the dataset of general word meanings is the
first step of studying diversity across dialects and represents the inputs of
the contribution collection phase. In this context, the UKC lexicon is
employed to build a dataset, which contains several facilities that support
retrieving categorized data from its interlingual shared meaning layer as
introduced in Section 3. Moreover, typology datasets or other approaches can
be used for that, such as the kinship dataset from Murdock, (1970); or
gathering data from online dictionaries using automatic methods, i.e. KinDiv
retrieves some of its kinship terms from Wiktionary. The constructed dataset
is a spreadsheet containing language-independent meanings from one semantic
field. At the same time, its content is distributed into subdomains (sheets)
for usability and simplicity in designing a concept hierarchy for each
subdomain which is a helpful tool for lexical-gap exploration. One spreadsheet
row is generated for each concept, containing the concept ID, the source
concept definition in the standard language, another definition in English, as
well as empty slots for inserting a lexical gap or a word with equivalent
meaning, and the data provider’s comments in a dialect or local language.

  2. 2.

Interlingual concept hierarchy: Modeling the interlingual shared meaning space
is essential to explore lexical gaps systematically. In this task, the UKC
concept hierarchy is exploited. UKC is the only resource introducing a
hierarchy of shared meanings across languages for each semantic field, such as
kinship, colors, or food. Furthermore, UKC uses a hybrid linguistic-conceptual
approach in modeling each domain. This approach adopts actual domain ontology
and linguistic data from typological literature. For example, a fragment of
the brotherhood hierarchy in the top layer of the UKC is shown in Figure 1. A
native speaker can compare each examined concept from the spreadsheet with the
hierarchy of its domain to extract additional knowledge about its meaning
based on a concept’s position in the hierarchy, which helps to provide a
concrete answer in terms of a gap or a lexical unit.

###  4.2 Contribution collection

Contributions from a local language or a dialect are provided by one native
speaker who was born and educated (university level) within the speaker
community. The following are the most notable instructions they are given:

  1. 1.

They are given the authority to skip concepts, stop contributions, or leave a
comment when they deem the terms are becoming too culture-specific and
consequently need an exact answer.

  2. 2.

They are asked to provide a lexicalization in a local language (or dialect)
that gives meaning equal to the concept’s meaning.

  3. 3.

They are asked explicitly to identify lexical gaps where no local (or dialect)
lexicalization exists.

  4. 4.

Within a local language (or dialect) and a subdomain (e.g., cousins), they are
asked to provide new concepts that did not exist in the list of inputs which
is imported from the UKC by providing a word (lemma) and a clear description
of its meaning.

The process of providing such contributions is depicted in two flowcharts; for
instance, Figure 3 shows the flowchart of the candidate gap (on the left-hand
side of the figure) and candidate equivalent word meaning (on the right-hand
side of the figure) exploration; it starts identifying a standard language and
a local language (or dialect) and providing a native speaker with a
spreadsheet including a list of subdomain concepts (inputs). Then, a native
speaker is asked to find a linguistic resource in the local language and use
it to search for concepts (concept-by-concept) to confirm lexicalizations and
gaps. He/she can use a linguistic resource in the search process as the
following steps: searching in a well-known dictionary, then in Wiktionary— a
large multilingual online lexicon after that in a typology dataset (if it is
available), and finally, using Google search (based on the count of search
hits). More details about these steps are described in Section 5. The native
speaker can rely on search results and the count of Google hits to give a more
concrete answer on whether the concept in the standard language has a
lexicalization or is a gap in the local language; such candidates are passed
to the next phase- lexicon-level validation.

Figure 3: Flowchart of gap and equivalent word meaning identification Figure
4: Flowchart of a new concept collection

A new concept collection is a third contribution in this phase, where the
steps of a candidate new concept exploration in a local language can be seen
in Figure 4. A native speaker can examine the list of subdomain concepts and
provide his/her (own) concepts with their definitions that he/she believes
have not existed in the list. The same search steps in gap identification can
be followed in this task. As shown in Figure 4, All candidate new concepts are
passed to the two subsequent validation phases: lexicon- and conceptual level.

###  4.3 Lexicon-Level Validation

Our lexicon-level validation method formally and explicitly addresses
individual gap identifications and their quality, as well as equivalent word
meanings and new concepts. It allows a qualitative evaluation of the entire
list of provided contributions through word-by-word and gap-by-gap in a loop
between a native speaker and a validator. A word, a gap, or a new concept does
not pass this validation until the native speaker provides the correct answer
for each of them, as shown in the flowcharts in Figure 3 and Figure 4.

A language expert who is also a native speaker of the determined language (or
dialect) will carry out this validation on a spreadsheet containing the data
and results gathered in the previous step with two additional empty columns:
the evaluation and lexicon-level validator’s comment, producing the following
information:

  1. 1.

Equivalent word meanings: validate the correctness of all provided words in
the local language (or dialect) by marking them up as correct, incorrect, or
unclear for borderline cases and by providing correct words or indicating them
as lexical gaps for incorrect ones.

  2. 2.

Lexical gaps: validate the word meanings marked as lexical gaps by the native
speaker in the local language, either as confirmed gaps or as non-gaps due to
an existing lexicalization in that language, which the validator needs to
indicate.

  3. 3.

New concepts: validate all proposed new word meanings in each subdomain by
marking them up as correct, correct but not new (in case the supposedly new
concepts already existed in the list), or not accepted (in case another
concept already existed in the list to express the meaning, or the validator
does not consider it as a desirable suggestion for other reasons).

Correct equivalent word meanings and gaps are integrated with the local
language lexicon on the fly. Also, correct new concepts are passed to the next
step to be validated at the concept level before merging them with the supra-
lingual shared meaning layer. While in case the evaluation is an incorrect
equivalent word or a gap, or not accepted new concept, the validator returns
each of them with a comment describing the reason to the native speaker to
review and address the problem; when the native speaker finishes revising
them, then he/she returns the new version of a contribution to the validator.
This cycle (native speaker’s contribution—lexicon level validation) is still
alive until the validator confirms the correctness of the contribution or
skips it.

###  4.4 Concept-Level Validation

In this step, a lexical-semantic expert who is the manager of the UKC system
verifies the new concepts and their quality as accept or reject to add them
into the supra-lingual concept layer as well as addresses unclear words and
non-confirmed gaps/non-gaps that are borderline cases. This validation is
based on a discussion session with the language expert responsible for
lexicon-level validation through concept-by-concept and case-by-case issue
validation. A spreadsheet containing all new concepts and determined (words
and gaps) to be examined is used. Columns of this sheet are the same columns
in the previous step and two additional empty ones: the evaluation and
concept-level validator’s comment. The following tasks are used:

  1. 1.

New concepts: Validate all proposed new concepts in each subdomain by marking
them up as correct, correct but not new (in case the supposedly new concepts
already existed in the UKC), or not accepted (in case another concept already
existed in the UKC to express the meaning, or the validator does not consider
the new concept as a desirable suggestion for any other reason).

  2. 2.

Unclear words: Validate the correctness of unclear word cases considered in
the border-area by the lexicon-level validator by marking them as correct or
incorrect and writing a comment.

  3. 3.

Non-confirmed gaps/non-gaps: Validate the word meanings that do not have
confirmation as lexical gaps or non-gaps by providing a judgment with a
comment

Correct new concepts are imported into UKC by merging them with the supra-
lingual conceptual layer. In contrast, not-accepted ones and those correct but
not new are returned to the validator at the lexicon level, who may also
return them with a comment describing the reason to the native speaker to
address an included problem. In a new cycle, modified new concepts by the
native speaker are transferred to this phase through the validator of lexicon-
level; then, the validator at this level reviews the updates and decides
whether to finish the revision cycle by accepting or rejecting the new
concepts or issue a new one for more review, as shown in Figure 4. In
addition, confirmed words and gaps output from this step are integrated with
the language lexicon in the UKC, as shown in Figure 3.

##  5 Case Study on Diversity Across Arabic Dialects

This section demonstrates the use of the methodology described in Section 4 on
kinship terminology from seven dialects of the Arabic language. Arabic is the
official language of more than four hundred million native speakers in twenty-
two countries in the Middle East and northern Africa. Classical Arabic or
Modern Standard Arabic (MSA) refers to the standard form of the language used
in academic writing, formal communication, classical poetry, and religious
sermons (Elkateb et al.,, 2006). Surprisingly lexical diversity is manifested
between Arabic dialects, evident in our study between seven of the twenty
dialects spoken worldwide. The selected dialects are Egyptian, Moroccan,
Tunisian, Algerian, Gulf, and South Levantine (two examples: Palestinian and
Syrian). Let us take the example of the Gulf word الخَال العودْ meaning
“mother’s elder brother,” which has no equivalent in South Levantine or
Moroccan; instead, they use the more general word الخَال meaning “mother’s
brother,” which can be used for both meanings “mother’s younger brother” or
“mother’s elder brother”. In this paper, we perform an experiment on the
Arabic dialects to capture their diversity in the kinship domain. The
resulting dataset with dialect-specific kinship terms will be integrated with
an instance of the Universal Knowledge Core for Arabic (Arabic
UKC)666http://arabic.ukc.datascientia.eu/concept ongoing project, which is the
first diversity-aware lexical resource for Arabic dialects so far.

###  5.1 Experiment Setup

As mentioned in Section 3, the UKC resource is our data source in building the
input dataset of kinship-independent language concepts and formalizing such
concepts and new word meanings (not existing in the inputs) explored in this
experiment. For example, the brotherhood hierarchy is shown in the top layer
of the UKC in Figure 1. In this study, contributions are provided by seven
native speakers (one per Arabic dialect). Regarding the contributors’ socio-
linguistic background, each has at least a master’s degree and was born and
educated, at least up to high school level, within the native speaker
community. The participants’ linguistic backgrounds are presented below:

  1. 1.

Participant 1: a native Algerian speaker with good command of English.

  2. 2.

Participant 2: a native Egyptian speaker with good command of English.

  3. 3.

Participant 3: a native Tunisian speaker with good command of English and
French.

  4. 4.

Participant 4: a native Gulf speaker with good command of English and Arabic-
Palestinian.

  5. 5.

Participant 5: a native Moroccan speaker with good command of English and
Italian.

  6. 6.

Participant 6: a native Palestinian speaker with good command of Arabic-Syrian
and English.

  7. 7.

Participant 7: a native Syrian speaker with good command of English.

Seven experiments (one for each dialect) are performed to explore lexical
units and gaps using our method. In each experiment, a spreadsheet of kinship
concepts is imported from the UKC (as the source, they were computed from the
KinDiv database), which serves as an input dataset to the contribution
(diversity-aspects) collection step. These kinship domain concepts are
language-independent units representing lexical meaning shared across 699
languages and spanning 184 distinct concepts. UKC categorizes kinship concepts
into six groups; each one contains a distinct subset of concepts sharing a
common kinship type meaning called a subdomain, for example, sibling and
cousin subdomains. The spreadsheet (the dataset) consists of six sheets, and
each one represents a kinship subdomain. See Table 2, which shows the
subdomain names and the count of containing concepts per subdomain of the
dataset.

Table 2: Statistics on the concepts in the input dataset.
Subdomains | Count of Concepts
---|---
Grandparents | 19
Grandchildren | 27
Siblings | 21
Uncle/Aunt | 27
Nephew/Niece | 33
Cousins | 57
Total | 184
Table 3: Count of Google Search Hits for Cousin Concepts in Arabic.
Concept | With/Without Diacritics | Count of Hits
---|---|---
العمومة Paternal cousin |  العُمومَةُ | 1.94 M | 3.04 M
العمومة | 1.1 M
الخؤولة Maternal cousin |  الخُؤولَةٌ | 111 k | 158 k
الخؤولة | 47 k
ابن العم Son of father’s brother |  اِبْن العَم | 84.8 M | 93.96 M
ابن العم | 9.16 M
بِنت العم Daughter of father’s brother |  بِنْت العَم | 8.43 M | 83.13 M
بِنت العم | 74.7 M
ابن العمة Son of father’s sister |  اِبْن العَمَّة | 12.5 M | 131.5 M
ابن العمة | 119 M
بِنت العمة Daughter of father’s sister |  بِنْت العَمَّة | 9 M | 30.4 M
بِنت العمة | 21.4 M
ابن الخال Son of mother’s brother |  اِبْن الخَال | 5.61 M | 33.01 M
ابن الخال | 27.4 M
بِنت الخال Daughter of mother’s brother |  بِنْت الخَال | 3.99 M | 30.69 M
بِنت الخال | 26.7 M
ابن الخالة Son of mother’s sister |  اِبْن الخَالَة | 12.5 M | 16.59 M
ابن الخالة | 4.09 M
بِنت الخالة Daughter of mother’s sister |  بِنْت الخَالَة | 11 M | 16.67 M
بِنت الخالة | 5.67 M

In the contribution collection, a native speaker answers by filling a lexical
unit or gap in a row empty slot specified for each concept. Linguistic
resources and Google Search are used to provide answers as precise as
possible. For example, the المعاني Almaany
dictionary777http://www.almaany.com/thesaurus.php,
Wiktionary888http://ar.wiktionary.org, and the _Fiqh AlArabiyya_ typology book
(Muttaqin,, 2009) are employed in sequential steps to give a judgment on
cousin words in Syrian. Additionally, counting the number of hits returned by
the Google search engine is another helpful indicator, where a high count of
hits indicates a searching word (i.e., ابن العمة meaning “son of father’s
sister,” has 131.5 million hits) is a lexical unit in Syrian. In contrast, a
low count indicates a lexical gap; for example, الخؤولة meaning “maternal
cousin,” has 158 thousand hits. Google hits of other cousin terms are shown in
Table 3. Since Arabic words can be written and read with or without diacritics
(i.e., “fatha” above a letter or “kassra” under it), thus, each word is typed
in two forms. Note that the content of this matrix cannot be considered the
only criterion for gap exploration because word hits may contain a count of
other hits resulting from searching in other Arabic dialects for the same
word.

###  5.2 Experiment Results

The overall contribution collection effort resulted in 180 words, 1,108
lexical gaps, and 19 new concepts identified, formalized, and collected.
Detailed statistics about the collected gaps and words are shown in Table 4.
New concepts were identified in three subdomains: siblings, cousins, and
grandchildren. The total number of new concepts, 19, is lower than the sum of
new concepts per language due to overlaps across languages: for example, أَخٌ
في الرضاعة meaning breastfeeding brother was found in all seven dialects, أخت
لأم meaning maternal sister was found both in Syrian and in Egyptian, while
أبْيِه meaning elder cousin, son of mother’s brother only exists in Egyptian.

Validation was carried out in two phases; in the first phase, words and gaps
were validated at the lexicon level by the first author, a Ph.D. student in
lexical semantics and a native speaker of Arabic, and the third author, an
Arabic native speaker with linguistic-semantic experience and good knowledge
in Arabic dialects. In the second phase, new concepts are verified and
approved to be added to the concept layer of the UKC by the second author, a
lexical-semantic expert, and the UKC system manager.

Using the lexicon-level validation method, the first author evaluated the
collected data in Palestinian and Syrian, while the third author validated the
remaining five dialects. Results can be seen in Table 5, whereby correctness,
we understand the number of words (or gaps) validated as correct divided by
the total number of words (or gaps). In the case of an incorrect word, the
validator either provides a correct word or indicates it as a lexical gap. For
example, for the Algerian dialect, the correctness of gathered words is 85.71%
and that of gaps is 98.08%. Four Algerian words were deemed incorrect: مانّي
for the meaning maternal grandmother, لالّة for the meaning paternal
grandmother, جَدّ for the meaning grandfather, and باب الشيخ for the meaning
grandparent. The validator indicated maternal grandmother, paternal
grandmother, and grandparent as gaps, while he replaced the mistaken word جَدّ
with the correct word باب الشيخ for grandfather. For gap evaluation, the
linguistic expert validates a lexical gap by confirming it as a gap or as a
non-gap due to an existing word in a dialect, for which he must provide the
correct word. For instance, Participant 1 identified the meanings elder
sister, father’s elder sister and mother’s elder sister as gaps in Algerian,
but the validator did not accept them and provided the polysemous word لالّة
for each of them. Evidence for validation was obtained from the dictionary
Dictionnaire arabe algérien999https://www.lexilogos.com/arabe_algerien.htm and
from usage attested in Algerian TV films. Upon discussion between the
validator and the participants, the mistakes made by the latter can be
explained by misunderstandings of the meanings of certain concepts provided in
MSA and English. The validator made sure to exclude or fix the mistakes,
bringing the correctness of the final dataset closer to 100%.

Table 4: The count of the diversity items collected and identified in the
Arabic dialects.
Dialects | Words | Gaps w/o new concepts | New concepts | Gaps considering new concepts
---|---|---|---|---
Algerian | 28 | 156 | 10 | 165
Egyptian | 32 | 152 | 19 | 152
Moroccan | 22 | 162 | 10 | 169
Palestinian | 23 | 161 | 14 | 166
Syrian | 24 | 160 | 10 | 169
Tunisian | 23 | 161 | 2 | 178
Gulf | 28 | 156 | 14 | 169
Total | 180 | 1108 | 19 | 1168
Table 5: Validator evaluation of words and lexical gaps by dialect.
Dialects | Correctness of Native Speaker Contribution
---|---
|  Words |  Gaps
Algerian | 85.71% |  98.08%
Egyptian | 96.90% |  97.37%
Moroccan | 95.83% |  97.53%
Palestinian | 100% |  98.76%
Syrian | 91.67% |  95.00%
Tunisian | 95.65% |  98.14%
Gulf | 100% |  96.79%
Average | 95.11% |  97.38%

In this study, we use the UKC for creating the input dataset and the domain
hierarchy and for storing and visualizing diversity data. Thus, the 19 new
concepts were merged with the UKC by reconstructing a domain hierarchy at the
supra-lingual concept layer. For example, the hierarchy of siblings was
redesigned to contain five new brotherhood concepts and five new sisterhood
concepts. For instance, in the Arabic-Egyptian lexicon, as shown in Figure 5,
أَخٌ في الرضاعة meaning “breastfeeding brother,” is set up as a sub-node for a
newly created concept of the brother, “a male person who has the same father,
mother, or both parents as another person or has the same breastfeeding
woman.”, also, from the figure, can be seen أخت لأب meaning “paternal brother”
and أخت لأم meaning “maternal brother” are inserted and connected the half-
brother concept. New concepts and lexicalization are marked with white nodes
and connected with blue lines.

Figure 5: Structural elements in the UKC database after merging new concepts

Additionally, resulting lexical units and gaps were added into UKC lexicons.
The website of the UKC provides several services for system users, such as
browsable online access to database contents, source materials, and data
visualization tools. The interactive exploration of linguistic diversity data
in lexicons is the central feature of the website. The user can browse: (1)
all meanings within a language of a word typed in by the user; and (2)
lexicalizations and gaps of a concept in all languages contained in the
database.

Figure 6 shows a screenshot of the concept exploration functionality
describing the concept جَدّ meaning “parent’s father”. On the left-hand side
of the screenshot, details are provided on the lexicalization of the concept
in Arabic, such as synonymous words, a definition, and a part of speech. The
middle part of the screenshot shows an interactive clickable map of all
lexicons that either contain the concept or, on the contrary, lack it due to
their languages being known not to lexicalize it. The color-coded dots
indicate the language family, while the black circled dot represents a lexical
gap. This map presents an instant global typological overview of the concept
selected; for instance, from Figure 6, one can see that most languages in
Europe lexicalize the concept جَدّ while several languages in the American
United States do not lexicalize it. Finally, the right-hand side shows the
concept جَدّ in the context of concept hierarchy, depicted as an interactive
graph: the concept, its parent and child concepts, and other lexical-semantic
relations (as metonymy and meronymy) are also presented when they exist. Note
that the graph only shows a part of the complete hierarchy for usability
reasons. Nevertheless, it is navigable and allows the exploration of the whole
concept graph in the selected language.

Figure 6: Exploring the concept of جَدّ as lexicalized in the Arabic language
(left), in the world (middle), and as part of the shared concept hierarchy
(right).

As mentioned at the beginning of this section, the resulting Arabic dataset
will be imported into the Arabic UKC, which is an instance of the UKC system;
the top layer contains independent language concepts, and the bottom layer
contains twenty lexicons as the number of Arabic dialects. A screenshot of the
homepage of the Arabic UKC is shown in Figure 7.

Figure 7: Homepage of the Arabic UKC ongoing project.

###  5.3 Discussion

The lexical diversity we observed across the seven dialects was higher than
our original expectations, with 19 new concepts identified. Ten of these
concepts are lexicalised in MSA, such as أخت في الرضاعة meaning “breastfeeding
sister” and أَخٌ لأب meaning “paternal brother”. The others (nine concepts)
are specific to the dialects, such as the Egyptian word أبْلَه meaning “elder
daughter of mother’s sister”, which returns to the Turkish word “kuzen”.
Mostly, the origin of these Egyptian-specific concepts is the Ottoman Turkish
language, when the Egyptian dialect was influenced by it during the Ottoman
occupation of Egypt in the period (1517 AD to 1867 AD).

Several shared meaning overlaps have been found between dialect pairs.
Likewise, intersections also existed between gaps. For a given domain $d$ and
languages $l_{a},...,l_{n}$, the formula below calculates the similarity of
the two languages in terms of the overlap of lexicalised concepts from that
domain, where $\textrm{LexConcepts}(d,l)$ stands for the set of domain
concepts that are lexicalized by the language $l$.

$\textrm{overlap}(d,l_{a},...,l_{n})=\frac{|\textrm{LexConcepts}(d,l_{a})\cap...\cap\textrm{LexConcepts}(d,l_{n})|}{\textrm{max}(|\textrm{LexConcepts}(d,l_{a})|,...,|\textrm{LexConcepts}(d,l_{n})|)}$
(1)

Figure 8 shows the overlaps between pairs of Arabic dialects over the kinship
domain. For example, the intersection of Egyptian and Gulf dialects gives a
shared coverage of 74.5%, while all dialects are 47.1% the same. In the former
case, the number of lexicalisations in Egyptian is 51, and in Gulf is 42.
Also, 38 of these lexical units are included in both dialects; see the dataset
uploaded to GitHub101010https://github.com/HadiPTUK/kinship_dialect. For
example, Formula 1 calculates the overlap between Egyptian and Gulf in the
Kinship domain ($K$) as follows:

$\mathrm{overlap}(K,\mathrm{Egyptian},\mathrm{Gulf})=\frac{|\mathrm{LexConcepts}(K,\mathrm{Egyptian})\cap\mathrm{LexConcepts}(K,\mathrm{Gulf})|}{\textrm{max}(|\mathrm{LexConcepts}(K,\mathrm{Egyptian})|,|\mathrm{LexConcepts}(K,\mathrm{Gulf})|)}$
$\mathrm{overlap}(K,\mathrm{Egyptian},\mathrm{Gulf})=\frac{38}{\mathrm{max}(51,42)}=\frac{38}{51}=74.5\%$

More detail about the analysis of shared coverage between the rest of the
Arabic dialects can be found in the same dataset uploaded to GitHub.

We find these overlaps—e.g. an overlap of 59.5% between Gulf and Tunisian, or
the overall overlap of 47.1% among all seven dialects—lower than our initial
expectations on dialectal variations. Arab dialectologists justify such
differences with two major factors: linguistic and religious influence (Zaidan
and Callison-Burch,, 2014). By linguistic influence, we refer to the
historical interaction of language-speaker communities, which affects the
lexicons. Examples are the Egyptian dialect influenced by the Coptic language
(historically spoken by the Copts, starting from the third century AD in Roman
Egypt) or the Levantine dialect influenced by the Western Aramaic, Canaanite,
Turkish, and Greek languages. The Gulf dialect is one of the Peninsular
groups, which was influenced by South Arabian Languages. Secondly, the
religion of the speaker community also affects the lexicon. Religion is a
sociolinguistic variable that shapes how Arabic is spoken. Religion in Arab
countries is a matter of group affiliation and is not usually considered an
individual choice: one is born a Muslim, Christian, Jew, or Druze, and this
becomes a bit like one’s ethnicity. So, for example, within the Egyptian
speech community, one can find language mixing between Islamic and Christian
terms, and the same in the Levantine community, which consists of a mixing of
Muslims, Christians, Jews, and Druze. The Gulf communities, instead, mostly
consist of Muslims (Al-Wer,, 2008).

Figure 8: The overlap (percentage of shared lexicalisations) for Arabic
dialects.

##  6 Case Study on Diversity Across Indonesian Languages

This section demonstrates the use of the methodology described in Section 4 on
kinship terminology from three Austronesian languages from Indonesia:
Indonesian, Javanese, and Banjarese. Contrary to the Arabic dialects in
Section 5, these three languages are not mutually intelligible.

Indonesia is the fourth most populous country in the world, and it has more
than 700 living languages (Eberhard et al.,, 2022). The national language
spoken in Indonesia is Bahasa Indonesia/Indonesian language, which was decided
in the historic moment of Youth’s Pledge, October 28th, 1928. However, many
Indonesians speak more than one language. For example, out of 198 million
people that speak Indonesian, 84 million of them speak Javanese (Aji et al.,,
2022).

Even with the high number of speakers, the count of natural language
processing research on Indonesian languages is very low compared to other
languages around the world. As of 2020, the count of published papers on the
Indonesian language is lower than other languages with less speaker count,
such as Polish and Dutch (Aji et al.,, 2022). Not surprisingly, the amount of
research on other languages (i.e., Banjarese and Javanese) in Indonesia is
much lower than that. It is therefore motivating to conduct this study that
discovers the richness of linguistic diversity across three Indonesian
languages: standard Indonesian, Banjarese, and Javanese. In one semantic
field, kinship, we have found that diversity is manifested in these languages;
for example, in Javanese, the word ponakan jaler meaning “nephew”, is a
lexical gap in Banjarese, and in the opposite direction, the Banjarese gulu
meaning “parent’s second eldest sibling” is also a gap in Javanese.

###  6.1 Experiment Setup

As in the Arabic experiment, we use the UKC lexicon to create the input
dataset of kinship terms, which are independent language and formalizing such
terms and also new concepts (not existing in the input dataset) identified in
this experiment, as shown in the top layer of the UKC in Figure 1 for the
brotherhood categorization.

In this study, three native speakers (one per language), born and educated
(high school level) within the speaker community, were recruited to
contribute. The participants’ linguistic backgrounds are listed below:

  1. 1.

Participant 1: a native Indonesian speaker with good command of English,
Javanese, and Banjarese.

  2. 2.

Participant 2: a native Banjarese speaker with good command of Indonesian and
English.

  3. 3.

Participant 3: a native Javanese speaker with good command of Indonesian and
English.

For each language, an experiment was carried out to identify words and gaps
associated with the same 184 kinship concepts as in the Arabic study (see
Table 2). For example, in Banjarese, the dictionary _Kamus Bahasa Banjar
Dialek Hulu-Indonesia_ (Balai Bahasa Banjarmasin,, 2008) and Google Search
hits were used in subsequent steps to provide a precise answer on each concept
from the given list of inputs. Such search steps were also followed by the
Banjarese native speaker for the task of judgment on new concepts identified
in the uncle/aunt subdomain. For instance, the Banjarese term _gulu_ ,
expressing an uncle/aunt relationship with the meaning of _a parent’s second
eldest sibling_ and attested by the dictionary above, did not previously exist
in the UKC or in the KinDiv dataset, nor in Murdock, (1970). Indonesian and
Javanese native speakers also follow the same steps and use the dictionaries
of Badan Pengembangan dan Pembinaan Bahasa, (2017) and Utomo, (2015) for the
task of judgment on terms and gaps identified in Indonesian and Javanese,
respectively.

###  6.2 Experiment Results

The overall contribution collection effort resulted in 41 words and 517
lexical gaps. Three new, yet unattested word meanings were also found and
formalised as new concepts. All three are used in Banjarese in the uncle/aunt
subdomain:

  * •

_julak_ , meaning _parent’s eldest sibling_ ;

  * •

_gulu_ , meaning _parent’s second eldest sibling_ ;

  * •

_angah_ or _tangah_ , meaning _parent’s middle elder sibling_ (when the number
of siblings is odd).

Statistics on the data collected for each language are shown in Table 6.

Table 6: The count of the diversity items collected and identified in the
Indonesian languages.
Languages |  Words |  Gaps w/o new concepts |  New concepts |  Gaps considering new concepts)
---|---|---|---|---
Indonesian |  11 |  173 |  0 |  176
Javanese |  17 |  167 |  0 |  170
Banjarese |  12 |  172 |  3 |  172
Total |  41 |  511 |  3 |  517

As in Arabic, a two-step validation was carried out in this study. The first
step validated words and gaps contributed by native speakers, carried out by
the fourth author, a native Indonesian speaker with a good command of all
three languages. The second validation step was done on the concept level,
performed by the second author, a lexical-semantic expert and UKC system
manager for new concept validation. In this step, the new concepts were
verified and approved to be added to the concept layer of the UKC.

Table 7 provides correctness results over native speaker contributions,
provided by the validator. Upon discussion between the validator and the
contributors, the mistakes made by the latter can be explained by
misunderstandings of the meanings of certain concepts, provided in English.
The validator made sure to exclude or fix the mistakes, bringing the
correctness of the final dataset closer to 100%.

Table 7: Validator evaluation of words and lexical gaps by language.
Languages | Correctness of Native Speaker Contribution
---|---
|  Words |  Gaps
Indonesian | 90.91% |  98.27%
Javanese | 94.44% |  95.78%
Banjarese | 91.7% |  97.67%
Average | 92.35% |  97.24%

The produced kinship datasets from this experiment will be merged with the
under-construction Indonesian UKC111111http://indonesia.ukc.datascientia.eu/,
a diversity-aware lexicon for languages spoken in Indonesia, also imported
into the main UKC database.

Figure 9 shows how UKC explores information about a specific Indonesian word.
However, the screenshot provides information about the Indonesian word
saudara, which means “sibling” in English. The left-hand side of the
screenshot explains synonymous words (lemmas) and the definition of the typed
word. The middle of the screenshot displays the map of a global typological
overview of the concept. Most languages do not lexicalize this concept, marked
by the black-circled dot. Only a few languages lexicalize it, such as
Indonesian, Swedish, Ainu, and Malayalam, marked by white-circled dots. The
right-hand side shows the lexico-semantic relations of the concept.

Figure 9: Exploring the concept of saudara as lexicalized in the Indonesian
language (left), in the world (middle), and as part of the shared concept
hierarchy (right).

The UKC lexicon is also equipped with several interactive visualization
services that can be used to browse lexical units and gaps by domain in all
supported languages. Figure 10 shows an example of using such services in
visualizing the content of the grandparent subdomain in Indonesian.

Figure 10: Interactive browser tool showing lexical units and gaps for the
grandparent subdomain in Indonesian.

###  6.3 Discussion

More than 90% of our 184 initial kinship concepts were found to be gaps in the
three Indonesian languages, as shown in Table 6. Using Formula 1, we
calculated the overlaps between the Indonesian languages in terms of kinship
lexicalisations, shown in Figure 11. For more details, see the dataset
uploaded to the GitHub
repository121212https://github.com/HadiPTUK/kinship_dialect. 35.3% of the
concepts are lexicalised by the three Indonesian languages studied. The
Javanese–Banjarese overlap is 52.9%, Javanese–Indonesian is 60%, and finally
Banjarese–Indonesian is 41.2%. Even though all three languages are included in
the Malayo-Polynesian branch of the Austronesian language family, Indonesian
and Banjarese are considered Malayic languages, while Javanese is not, which
is the first reason for this result. Furthermore, these languages exist on
different islands in Indonesia; Javanese exists on Java Island, Banjarese is
located on the southern part of Borneo Island, and the Indonesian language is
based on Malay, which is spoken on Sumatra Island (Sneddon,, 2003), so this
geographical barrier restricts interactions between speakers, and each
language has developed within its own speech community.

Finally, using Formula 1, we computed the overlaps between Arabic dialects and
Indonesian languages. Figure 12 shows that the ten languages together cover
only 3.9% of the concepts, and the most similar language pair, namely
Egyptian–Indonesian, is merely 5.9% similar. For researchers in ethnography or
comparative linguistics, the observation of such pronounced levels of cross-
lingual and cross-cultural diversity may not come as a surprise, as major
variations in kin patterns are well known in these domains. On the other hand,
we believe that beyond these narrow fields of research, there is a general
lack in understanding the depth of diversity in how, through languages, people
describe and interpret the world. Most computational linguists and engineers
who build language processing systems, as well as the users who trust such
systems for their daily activities, do not suspect the breadth of the mental
divide across languages that language applications, such as machine
translation systems, are meant to bridge. We think that through quantified
measures, as we are attempting to do with our simple measure of overlap
introduced on p. 1, can be useful to improve our qualitative grasp on
diversity, which we consider a promising direction for future research.

Table 8 includes statistics of collected words and gaps by domain across
Arabic and Indonesian languages. The results show that only three words in the
domain of cousins are identified in the Indonesian languages, while in
Egyptian, 16 words are used around the concept of the cousin.

Figure 11: The number of words in the intersection of Indonesian languages
according to shared meaning. Figure 12: The number of words in the
intersection of Indonesian and Arabic languages according to shared meaning.
Table 8: Statistics of the collection of diversity data by domain.
Domains |  Words |  Gaps
---|---|---
Grandparents |  21 |  169
Grandchildren |  19 |  251
Siblings |  37 |  173
Uncle/Aunt |  44 |  226
Nephew/Niece |  33 |  297
Cousins |  67 |  503
Total |  221 |  1619

##  7 Related Work

Ethnologists and linguists have for a long time studied how family structures
map to kinship terminology across languages and social groups. The most famous
and comprehensive ethnographic study on kin term patterns is that of Murdock,
(1970), upon which our work also indirectly relied: our cross-lingual
formalisation of kin terms is based on the one provided by the KinDiv
resource, itself in part derived from Murdock’s data. KinDiv covers 699
languages and is a computer-processable database that can also be exploited
for applications in computational linguistics. Our results provide linguistic
evidence in seven Arabic dialects and three Indonesian languages that do not
figure in these resources.

The exploration of kin terminology and the building of large-scale databases
on the topic has also been the subject of more recent efforts—we only cite two
examples here. The AustKin project131313http://austkin.net has produced a
large-scale database on kin terms in hundreds of indigenous Australian
languages. The recent Kinbank database (Passmore et al.,, 2023) is a
comprehensive resource on kinship terminology, covering over 1,173 languages,
with a broad coverage of kinship subdomains. As Kinbank was released after the
initial submission of our paper, we did not rely on it for our work. We
consider our research as complementary to Kinbank: concentrating on a
relatively low number of dialects and languages, our results could, in
principle, be integrated into Kinbank in order to extend its coverage. And
vice versa, we see potential in using Kinbank data in order to cross-validate
and possibly to extend the Indonesian terms we collected (as the three
Indonesian languages of our study are also covered by Kinbank). There is,
however, an important methodological difference between the our and Kinbank’s
way of representing terms: Kinbank does not explicitly indicate lexical gaps.
For example, our work considers the concept of son of father’s brother as
pronounced by a male speaker to be a lexical gap in Javanese, while Kinbank
maps the Javanese term _sedulur misan_ , simply meaning _cousin_ , to this and
95 other meanings. Our work, instead, identifies the Javanese term as the
general meaning of _cousin_ and considers all other (more specific) cousin
terms as lexical gaps. This distinction is useful in comparative linguistics
and cross-lingual applications where the explicit indication of the lack of
precise meaning equivalence can be exploited.

Concepticon (List et al.,, 2016) is ‘a resource for linking concept lists’
frequently used in comparative linguistics. The _concept sets_ of Concepticon
serve the same purpose as the supra-lingual concepts of the UKC in our study,
namely to provide meaning-based mappings among lists of terms (aka _concept
lists_ in Concepticon) across languages. As of mid-2023, Concepticon consists
of nearly 4,000 concept sets, principally targeting core vocabularies (basic-
level categories) that are the main subject of study of historical and
comparative linguistics. Concepticon is under continuous development and has
more recently evolved from a flat list of meanings to a hierarchy with
broader–narrower relations. At the time of writing, the kinship domain seems
to be partially represented in Concepticon: while sibling or grandparent
relations are widely covered, fine-grained cousin relationships are mostly
missing from it. The UKC, which contains over 100,000 supra-lingual concepts
and a wide range of lexical and lexico-semantic relations, was a more suitable
resource for our study due to its more complete coverage of the kinship domain
and its explicit support for representing term untranslatability via lexical
gaps.

Multilingual computational applications being in the core of our focus, we
also review relevant resources from computational linguistics. For NLP
applications, the most popular and widely-known representation of lexico-
semantic knowledge is that of _wordnets_ that follow the general structure of
the original English _Princeton WordNet_ (Miller,, 1995). The wordnet
expansion approach by Fellbaum and Vossen, (2012)—an expert-driven lexicon
translation effort—is frequently used to produce new wordnets for lower-
resourced languages: this approach consists of ‘translating’ (i.e. finding
lexicalizations for) English WordNet concepts (‘synsets’ in wordnet
terminology) into the target language. While this is a straightforward
approach that produces resources that remain cross-lingually linked, its
downside is that the translation approach cannot involve concepts and words
specific to the target language and not present in the source language (which
in most cases is English). In cases of diverse conceptualisations of the
world, the translation approach often results in incorrect approximations. To
take the example of Arabic, both versions of the Arabic Wordnet (Elkateb et
al.,, 2006; Abouenour et al.,, 2013) map the English synset of _uncle_ (“the
brother of your father or mother; the husband of your aunt”) to the Arabic
synset of عم, which means “the brother of your father.”

A similar situation is observed for Indonesian. As far as we know, the only
Indonesian Wordnet currently accessible is Bahasa Wordnet—a bilingual Wordnet
for standard Indonesian and Malay languages (Noor et al.,, 2011). It was
formed by merging three different wordnets (one in Indonesian and two in
Malay) developed mainly by the same expansion approach from PWN. Due to this
approach, many English words that have no equivalents in Indonesian are
incorrectly mapped, resulting in meaning loss. For example, in Bahasa Wordnet,
the English word _sister_ , which means “a female person who has the same
parents as another person,” was mapped to the Indonesian word _kakak_ which
means “elder sibling.”

Finally, we mention MultiWordNet as an early effort at improving the
representation of linguistic diversity in multilingual lexical databases
(Pianta et al.,, 2002). It is a multilingual lexicon that was built using the
merge method that, contrary to the translation-based expand approach presented
above, maps together existing high-quality bilingual dictionaries.
MultiWordNet explicitly represents lexical gaps in its Italian and Hebrew
wordnets: about 1,000 in Italian and about 300 in Hebrew (Bentivogli and
Pianta,, 2000; Ordan and Wintner,, 2007). MultiWordNet, however, is a
discontinued effort that does not cover the kinship domain and is thus was not
suitable for our purposes.

The methodology we present in Section 4 follows neither the expansion nor the
merge approach but a third one, more adapted to diversity-aware lexicography:
our starting point is a supra-lingual, diversity-aware conceptualization of
the domain of study (kinship in our case). The task of _contribution
collection_ is performed by native speakers with respect to the supra-lingual
concept hierarchy based on evidence from comparative linguistics and covering
a wide range of languages. While there is no guarantee that our initial
conceptualization is complete—indeed, it was not the case in our study—it is
less biased towards the concepts of a single language and speaker community
than the expansion approach.

##  8 Conclusions and Future Work

Our paper formally captures lexical diversity across languages and dialects by
representing language- or dialect-specific concepts and linguistic gaps. It
introduces a systematic method to produce such data in a human-based manner
from one semantic domain rather than from general domains, as the efforts of
covering the WordNet domains (Magnini and Cavaglià,, 2000) that have been
conducted in building these wordnets, Mongolian (Batsuren et al.,, 2019),
Unified Scottish Gaelic(Bella et al.,, 2020), and MultiWordNet(Pianta et al.,,
2002).

The method is verified through two large-scale case studies on kinship
terminology, a domain known to be diverse across languages and cultures: one
case study deals with seven Arabic dialects, while the other one with three
Indonesian languages. The experiments show that our method outperforms the
existing methods in terms of the quantity of explored gaps and words and the
quality of results. Overall efforts resulted in 1619 gaps, and 223 words were
identified in 10 languages and dialects. Moreover, 22 new word meanings with
respect to the imported list of independent-language concepts from the UKC are
explored in this research.

In future work, we plan to automate the method presented in this paper and
apply it to new languages, such as the rest of the Arabic dialects and
Indonesian language, as well as to new domains that are known to be diverse,
such as body parts, food, color, or visual objects (Giunchiglia and Bagchi,,
2021; Giunchiglia et al.,, 2023).

Finally, diversity-aware lexicons such as the UKC (which includes our produced
datasets) provide essential information to cross-lingual applications, such as
multilingual NLP tasks or cross-lingual language models. In the future, we
plan to use this resource in implementing one such application, i.e., machine
translation.

## Conflict of Interest Statement

The authors declare no conflict of interest.

## Author Contributions

FG and GB conceptualized and supervised the study. GB and HK imported and
formatted the dataset of inputs. HK wrote the original manuscript draft and
performed the Arabic experiments. AF and HK validated the collected Arabic
data at the lexicon level. SD performed the Indonesian experiments and
validated the results at the lexicon level. GB validated the identified
diverse data at the concept level. FG, GB, AF, and HK analyzed the Arabic and
Indonesian data. FG, GB, AF, SD, and HK reviewed and edited the manuscript.
All authors contributed to the research and approved the submitted version.

## Acknowledgments

We thank the University of Trento and Palestine Technical University—Kadoori
for their support.

## Data Availability Statement

The diversity-aware datasets of the kinship generated and analyzed for this
study can be found in the GitHub repository
(https://github.com/HadiPTUK/kinship_dialect).

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