Broaden Your Horizon! Play with Semantics via a Knowledge
Graph-Based Approach
Pasquale Esposito
1 a
, Crescenzo Mazzone
, Maria Angela Pellegrino
2 b
and Vittorio Scarano
2 c
Dipartimento di Studi Umanistici, Universit
a degli Studi di Salerno, Fisciano (SA), Italy
Dipartimento di Informatica, Universit
a degli Studi di Salerno, Fisciano (SA), Italy
Lexical Semantics, Knowledge Graph, Semantic Web, Word Senses, User Evaluation, Italian.
(Lexical) semantics is the study of the meaning of words by looking at either the word itself or exploring its
neighborhood. Hence, lexicons, synonyms, and analogies can be easily represented as semantic networks,
also known as knowledge graphs, to represent words and their connections. While knowledge graphs can be
perceived as a natural and intuitive representation for modeling and exploring words, directly accessing them
via standard query languages, such as SPARQL, is cumbersome, mainly for lay users. This article explores
the possibility of “playing with semantics” via a knowledge graph-based approach to let end-users explore
lexical-semantic relations without explicitly formulating SPARQL queries. We evaluated the accuracy and
the coverage of users’ expectations by inquiring about 27 Italian native speakers and compared quantitative
and qualitative results. According to the performed evaluation, knowledge graphs have the potential to fulfill
users’ satisfaction, but multiple source results must be merged to guarantee high coverage and accuracy.
Semantics is the study of the meaning of words and
how linguistic signs mediate between concepts and
forms (Pustejovsky, 2016). Semantics studies are di-
vided into two sub-branches: logical and lexical se-
mantics. The present study mainly focuses on the lat-
ter and investigates how the relations among units of
meaning interact with their biological mental repre-
sentations. In other words, this study relies on how
native speakers perceive sense out of a combination of
units of meaning organized in sentences. Since words
are inventories of arbitrary signs, people can interpret
words or their combinations differently. Semantics
can provide a series of instruments to understand bet-
ter complex cases of words with multiple meanings
and connections in their lexical-semantic network.
Semantic networks’ linguistic-traditional and for-
mal representation can be considered similar to the in-
formation technology representation of semantic net-
works, a.k.a. Knowledge Graphs (KGs). Linguistics’
lexical semantics establishes connections and similar-
ities among words by looking at the neighborhood of
the network, i.e., words that occur within natural sen-
tences. Similarly, KGs represent semantic relations
between concepts in a directed graph consisting of
vertices, a.k.a. concepts, and edges, a.k.a. semantic
relations between concepts (Sowa, 2006).
KGs, and Semantic Web technologies in gen-
eral, are helpful in representing words and their sur-
roundings as semantic networks to let users explore
words in context, and they are crucial for knowl-
edge management and information retrieval (De Do-
nato et al., 2020). (Mitchell et al., 2008) showed
that brain imaging studies have different spatial neu-
ral activation patterns while thinking about pictures
or words. This phenomenon is widely debated in
psycho-linguistic literature and massively explored by
many experiments eager to explore neuronal seman-
tic networks, such as those conducted by (Wang et al.,
2022; Emma James and Henderson, 2023). However,
KG query languages are complex for lay users (Var-
gas et al., 2019; Bellini et al., 2014). Hence, KG
querying mechanisms should mask syntactical com-
plexities, allowing users to pose queries easily to ex-
ploit semantic network content.
This paper explores the opportunity to “play with
(lexical) semantics”, meaning that users are supported
in querying a word, retrieving its meaning, and ex-
ploring all the words connected to it as synonyms,
Esposito, P., Mazzone, C., Pellegrino, M. and Scarano, V.
Broaden Your Horizon! Play with Semantics via a Knowledge Graph-Based Approach.
DOI: 10.5220/0012620700003693
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 16th International Conference on Computer Supported Education (CSEDU 2024) - Volume 1, pages 380-387
ISBN: 978-989-758-697-2; ISSN: 2184-5026
Proceedings Copyright © 2024 by SCITEPRESS Science and Technology Publications, Lda.
analogies, and hierarchies. This word exploration is
enabled via a KG-based approach by implicitly pos-
ing SPARQL queries while masking underlying com-
plexity. To verify if and to what extent the pro-
posed prototype, referred to as LexicalEngine, sat-
isfies users’ expectations, we tested it on 27 Italian
native speakers and collected their opinions via an
online questionnaire. While LexicalEngine cov-
ers a wide range of semantic relations, such as def-
initions, synonyms, and word hierarchies, we focus
on word senses in this contribution. In particular,
we investigated if the semantics modeled via KGs
can disambiguate terms by providing end-users with
a complete and accurate pool of word senses suffi-
ciently close to the one proposed by (adult) native
speakers. We compared word senses automatically
retrieved by KGs with those proposed by partici-
pants. We verified if LexicalEngine returns word
senses suggested by adult Italian speakers by measur-
ing the coverage metric. Supposing that there are
word senses foreseen by Italian speakers and not re-
turned by LexicalEngine, those words would affect
the coverage metric. The higher the coverage, the
greater the guarantee that an automatic KG-driven ap-
proach would also return any word sense suggested
by an Italian speaker. Besides the coverage, provid-
ing learners with tip support and avoiding informa-
tion load by proposing out-of-topic and inaccurate
suggestions is crucial. Hence, we reflected on word
senses proposed by LexicalEngine but not foreseen
by Italian speakers. For those word senses, we asked
participants to assess their accuracy by reporting if
and to what extent users considered those meanings
as valid. The higher the accuracy, the greater the in-
spirational power of the proposed prototype, able to
propose valid meanings, even not immediately fore-
seen by native speakers. The higher the assessed met-
rics, the greater the guarantee that end-users, such
as (young) learners, can be successfully supported in
self-learning lexical semantics via a KG-driven ap-
proach. By formulating those directions as research
questions (RQs), we explored the following aspects:
- To what extent do KGs cover word senses be-
longing to Italian native speakers’ mental lexicon?
- Can KGs inspire end-users by proposing un-
common but accurate word senses? To what extent?
According to the collected results,
LexicalEngine reaches a considerable cover-
age of users’ expectations in terms of word senses
envisioned by (adult) Italian native speakers by
combining multiple KG lexical-semantic features and
their data (RQ
). On the contrary, participants are
skeptical about uncommon word senses suggested
by LexicalEngine, that require to be justified to
be appreciated and understood (RQ
). This article’s
contribution goes toward acknowledging the use-
fulness of exploiting KGs in playing with lexical
semantics, envisioning applicability in self-learning
and education settings.
The rest of the article is structured as follows. Sec-
tion 2 clarifies the used terminology and overviews re-
lated work. Section 3 presents LexicalEngine. Sec-
tion 4 describes the performed qualitative and quan-
titative assessment, while Section 5 and Section 6 re-
port and discuss the collected results. Finally, the arti-
cle concludes with final remarks and future directions.
This section defines terms that will be used in the fol-
lowing and overviews related work regarding sources
and tools to support lexical semantics via KGs.
2.1 Terminology
- In linguistics, a word sense is one of the mean-
ings of a word. For example, a dictionary may
have different senses of the word play, each hav-
ing a different meaning based on the context of the
word’s usage in a sentence. Play is generally de-
fined as “be engaged in an activity for enjoyment
and recreation rather than a serious or practical
purpose”. However, it may be used as play a role
as a synonym of acting or in combination with a
sport to mean take part in (a sport).
- Hyponymy and hypernymy refer to relationships
between a general term and the more specific
terms that fall under the category of the general
term. For example, the colors red, green, blue, and
yellow are hyponyms. They fall under the general
term of color, which is the hypernym.
- In linguistics, meronymy is a semantic rela-
tion between a meronym denoting a part and a
holonym denoting a whole. In simpler terms,
a meronym is in a part-of relationship with its
holonym. For example, the finger is a meronym
for hand, which is its holonym. Holonymy is the
converse of meronymy.
- Synonymy refers to words that are pronounced
and spelled differently but share the same mean-
ing, e.g., words like car and automobile are close
synonyms that can be exchanged without compro-
mising the conceptual meaning despite having dif-
ferent stylistic functions. Similarly, one can also
have synonymic multi-word expressions as in the
case of play a role which is a synonym of acting.
Broaden Your Horizon! Play with Semantics via a Knowledge Graph-Based Approach
Table 1: Comparison between lexical KGs in terms of supported semantic relations and user interface.
BabelNet Wikidata DBnary DBpedia ConceptNet
Word senses
Interface Web, Mobile & APIs SPARQL endpoint Web SPARQL endpoint Web
- This study refers to analogy in its role as rhetor-
ical figure. The analogical associative process is
a conceptual-based comparison between different
unit(s) of meaning, which, as a sort of semantic
trait, transfer from one concept to another. We
acknowledge that analogy is not included in the
set of lexical semantic relations. However, in line
with the procedure followed for the study, which
is based on a language-based associative evalua-
tion as the analogical comparison, it has been in-
cluded for the purpose of furthering a line of rea-
soning or drawing an inference.
2.2 Related Work
Literature offers some other examples of digital en-
vironments and web interfaces supporting lexical se-
mantics exploration. Fabula (Martines, 2017) scaf-
folds professional writers to author stories supported
by narrative suggestions pertinent to the user’s typed
words. Similarly, Communics (Rutta et al., 2020)
supports the creation of comics with predefined sen-
tences to overcome the “blank page syndrome”. Nov-
elette (Addone et al., 2021) offers a suggestion mech-
anism of analogies, synonyms, and rhymes based on a
user-defined word by querying BabelNet (Navigli and
Ponzetto, 2012) and WordAssociations
Focusing on KGs modeling lexicon and seman-
tics, Wikidata (Vrande
c and Kr
otzsch, 2014) and
DBpedia (Lehmann et al., 2015) are free, open, and
general-purpose KGs acting as central storage for
Wikimedia project data. Besides general content,
they can be easily queried to retrieve word synonyms,
definitions, and taxonomies. (Rodosthenous et al.,
2020; Rodosthenous et al., 2019) retrieve lexical re-
lations via ConceptNet (Speer et al., 2018) which
is a freely available commonsense KG and natural-
language-processing toolkit that supports many prac-
tical textual-reasoning tasks over real-world docu-
ments and does not assume that words fall into
“synsets”, sets of synonyms that are completely in-
terchangeable. Nevertheless, WordNet (Miller, 1995)
assumes a synset-based word organization where a
set of synonyms and their related senses are linked
by semantic relations. Similarly, many other differ-
ent linguistic semantic networks have been proposed
directly relying on WordNet. To name a few, Babel-
Net (Navigli and Ponzetto, 2012) extends the notion
of synset to contain multilingual lexicalizations. DB-
nary (S
erasset, 2012) models multilingual lexical data
curating word senses and translations.
Table 1 compares different semantic networks re-
garding supported semantic relations and user in-
terfaces to explore them. Most of them provide
end-users with a web interface covering a wide
range of semantic relations. Following this line,
LexicalEngine automatically retrieves semantic as-
sociations and rhetorical figures by querying multi-
ple KGs at once while masking technical challenges
in posing SPARQL queries and providing end-users
with a unified result which can be explored via a web
interface. This approach detaches our research from
the exclusive quantitative and metric-based assess-
ment of KGs and includes the qualitative evaluation
based on speakers’ linguistic expectations which are
subsequently assessed later in the process.
3 LexicalEngine - SEMANTICS
The proposed prototype, LexicalEngine, is a web-
based interface (freely available online
for test-
ing and demonstrative purposes) and API mecha-
to let end-users and developers retrieve se-
mantic relations, such as word senses, term defini-
LexicalEngine demo:
CSEDU 2024 - 16th International Conference on Computer Supported Education
tions, synonyms, homonyms, hypernyms, hyponyms,
meronyms, translations, images, and emoticons by
implicitly querying semantic networks as BabelNet,
Wikidata, DBpedia, DBnary, ConceptNet. The rest
of this section describes the interface and the working
mechanism of LexicalEngine, provides the reader
with technical details, and outlines some scenarios in
which LexicalEngine can be used.
Interface and Working Mechanism. A general
workflow is graphically reported in Figure 1.
Figure 1: Workflow of LiteralEngine to retrieve word-
related semantic relations by querying multiple KG and vi-
sually rendering results to end-users.
LexicalEngine requires a user-defined word as in-
put. Supposing that end-users want to explore cuore,
the Italian translation of heart. In the learning mode,
as visible in Figure 2, LexicalEngine also requires
the semantic relation of interest. Let us hypothesize
that end-users are interested in looking for synonyms.
Each user-defined request is transformed into a set of
SPARQL queries or API requests over all the KGs
supporting the configured semantic relation, accord-
ing to Table 1. In the case of synonyms, all the
supported KGs are queried in parallel. Results are
asynchronously collected by keeping the correspon-
dence between the source and results. Results are
parsed by a dedicated module, referred to as results
formatter in Figure 1 that takes care of visually ren-
dering results to end-users via unsorted lists or word
clouds, as widely explored in the literature to engage
learners (Shu et al., 2020). Back to our example,
Figure 2 reports the interface looking for Italian
synonyms of heart, showing that it can be interpreted
as a center, a metaphor for love, or the heart as an
Not all the semantic relations are covered by all
the KGs, as reported in Table 1. In that case, only
KGs supporting the user-requested semantic relation
are queried. Moreover, even if a service is supported
by a given source, the reply should not be given for
granted. For instance, it may happen that the user-
defined word is not covered by all the sources, as hap-
pens in the reported use case. Looking at Figure 2,
only two KGs returned results, even if synonyms is a
service covered by all the queried KGs.
Technical Details. As a web-interface,
LexicalEngine logic is written in JavaScript.
The web interface relies on the LexicalEngine
API mechanism. Hence, any user request per-
formed by the visual interface is converted in an
LexicalEngine API call of the corresponding ser-
vice. For each semantic relation relying on multiple
KGs, KG queries are run in parallel. At the moment,
English, Italian, French, German, and Holland are
supported, but introducing other languages only re-
quires dealing with a few translations of the interface
components, such as the covered semantic relations
and setting options. However, it is crucial to check the
language coverage of the queried sources. Besides
the ones that are currently supported, sources can be
customized by verifying semantic relations covered
by the introduced KG and performing a dedicated
call accordingly. LexicalEngine distinguishes
between learning and inspiring mode as a result of
the performed evaluation. While in learning mode,
end-users are aware of the semantic relation that
links suggestions to the user-defined request, in the
inspiring mode, suggestions derived from different
semantic relations and sources are just merged
together. No sorting is applied. The source code of
LexicalEngine is freely available on GitHub
Use Cases. The synonym lookup scenario is an ex-
ample to improve writing skills and avoid repeti-
tions. Moreover, in the evaluation stage, we also
asked participants about the application context of
LexicalEngine they foresee. Surprisingly, partici-
pants envisioned exploiting the proposed prototype to
edit text, learn semantics and novel word senses, and
argue. Moreover, such a system has been proposed
by teachers to overcome the blank page syndrome in
storytelling, where suggestions are not merely consid-
ered as an opportunity to avoid repetitions but should
inspire storytellers by letting them play on words and
explore semantic relations. Supposing that the ed-
ucators assign a topic to learners that can be sum-
marized in a single word, e.g., field, and learners
can investigate suggestions automatically generated
by LexicalEngine, such as landing field, battlefield,
lines of business, or even field of operation.
Broaden Your Horizon! Play with Semantics via a Knowledge Graph-Based Approach
Figure 2: LiteralEngine interface while looking for synonyms of heart (cuore in Italian).
This section reports the methodology, participants,
and metrics of the performed evaluation aiming to
verify if KGs can automatically propose complete
) and accurate (RQ
) word senses if compared
with those foreseen by (adult) Italian native speakers.
4.1 Participants
The evaluation has been performed asynchronously
by collecting participants’ replies via an anonymous
questionnaire administered via Google Forms
. The
link to the questionnaire has been sent to the authors’
contacts by encouraging them to share it. 27 Italian
native speakers joined the evaluation, voluntarily and
for free, 52% females. The sample is heterogeneous
in terms of regions of provenance: 70% from the
Campania region, 18% from the Lombardia region,
and 11% from the Lazio region. The sample is hetero-
geneous also concerning the age range ranging from
18 to over 50, mainly between 18-25 (41%) and over
50% (22%), while the remaining is equally distributed
over the remaining age range. The educational quali-
fication of the participants corresponds at least to the
diploma, and they are students or workers in a wide
range of companies or institutions. Hence, the sam-
ple is heterogeneous regarding age, profession, edu-
cational title, and provenance to reduce as many bi-
ases as possible in the evaluation results. All the par-
ticipants assessed their interest in using sophisticated
language, grading it at least 3 out of 5. It emphasizes
that participants are interested in curating their vocab-
ulary even if they are not linguists by profession.
4.2 Methodology
The authors agreed on 10 Italian words for the eval-
uation, choosing among those frequently occurring
in written and spoken language provided with multi-
ple word senses. Literal translations of the evaluated
words are tree, field, organ, time/weather (both
tempo in Italian), star, heart, sun, fishing/peach
(both pesca in Italian), belief/cupboard (both
credenza in Italian), and wing. For each word, word
senses have been retrieved by querying BabelNet and
ConceptNet, while synonyms have been retrieved by
querying DBpedia, Wikidata, DBnary, BabelNet, and
ConceptNet. Participants are first invited to think
about word senses for each proposed word without
any support. Subsequently, they are asked to evaluate
senses returned by LiteralSearch. The assessment
phase involves qualitatively evaluating the word units
from the speaker’s perspective. The consequent grad-
CSEDU 2024 - 16th International Conference on Computer Supported Education
ing of the proposed ones relies on the participants’
perspective as native speakers. The grading is based
on a three-value scale: 0 value if unrelated, 0.5 if par-
tially related, and 1 if wholly related. Finally, par-
ticipants are invited to quantitatively summarize their
opinions at an overall level regarding coverage of their
expectations, gaining inspiration, originality, and the
perceived completeness for the proposed word senses
on a 5-point Likert scale, and free to leave a qualita-
tive opinion via open-ended questions.
4.3 Metrics
As a result of the conducted protocol, for each of the
10 tested words, we have two pools of word senses:
those proposed by participants (WS
) and those re-
trieved via LexicalSearch (W S
). The two pools
are not disjoint. Hence, we have the following sets:
a set of word senses matching participants’ mean-
ing expectations and covered by LexicalSearch,
i.e., W S
a set of word senses matching participants’
meaning expectations but uncovered by
LexicalSearch, i.e., W S
\W S
a set of word senses proposed by LexicalSearch
but absent in participants’ lexical perspective, i.e.,
\W S
According to the RQs, we measured both the cov-
erage of users’ expectations and the accuracy of un-
foreseen word senses through a qualitative analysis
of the data provided by the LexicalSearch. The
metrics chosen to evaluate the results obtained with
the assessment are based on cognitive sciences and
mental lexicon principles. Citing (Read, 1993), na-
tive speakers have remarkably stable patterns of word
association, which can be taken to reflect the sophis-
ticated lexical semantic networks they have devel-
oped through language acquisition. While the cover-
age has been computed by comparing S
with W S
the accuracy is computed by looking at grades as-
sessed by participants concerning S
. For each word,
we checked the overlap between users’ defined word
senses and those retrieved by LexicalSearch. If
the ones returned by LexicalSearch do not cover
a user-defined word sense (S
), we checked if syn-
onyms cover it. For each word sense proposed by
LexicalSearch and not foreseen by any evalua-
tor (S
), we collected users’ opinions on its accu-
racy and checked official Italian dictionaries (Devoto,
2016; Sabatini, 1997; De Mauro, 1999)
to see if the
word sense could be justified.
Grande dizionario italiano dell’uso accessible online
Coverage (Connected to Rq
). All the participants
proposed at least a word sense for each word, with a
maximum of 5 proposals. By aggregating the word
senses proposed by all the participants, at least two
word senses are collected for each word. Figure 3 re-
ports the coverage of users’ expectations. It is worth
noting that the evaluated words are on the x-axis on
the chart represented in Figure 3, and its column mod-
els the percentage of users’ expectations satisfied by
word senses and those covered by synonyms and the
percentage of not satisfied expectations. Each user-
defined word sense is weighted in terms of people
suggesting the same word sense.
Figure 3: Users’ expectations coverage.
Not all user-defined proposals can be correctly
considered word senses as they also cover synonyms
and analogies. To name a few examples, some par-
ticipants attached brands to some words, such as All
stars to star or the name of a laundry detergent or
oranges to sun, or proper names of song authored by
the Italian singer Al Bano to sun or the one of the ro-
mances authored by De Amicis to Heart. After nam-
ing some word senses, evaluators usually moved to
metaphors or analogies referring to famous sayings.
For instance, some of them attached handsome to sun
referring to the saying You are as beautiful as the sun,
or they attached money to time referring to the saying
Time is money. Some proposals went in the direction
of analyzing the anatomy of the words. For instance,
some evaluators attached palindrome to wing as its
Italian translation, ala, is a palindrome. Those cases
negatively affected the coverage.
Accuracy (Connected to Rq
). Only in two cases
LiteralSearch returned a proposal not covered by
any evaluator. In both cases, evaluators considered
them not related. However, by exploring the reason
at the basis of the proposals, we discovered that the
suggestion attached to ala is hat as it is the name of
Broaden Your Horizon! Play with Semantics via a Knowledge Graph-Based Approach
the hat brim and the suggestion attached to pesca is
a specific fishing net traditionally used in an Italian
region. Hence, both proposals are considered valid.
Overall Assessment. Participants graded inspira-
tion, satisfaction, originality, and coverage using a 5-
point Likert scale. The mean value is close to 4 and
the confidence interval reaches a minimum close to
3.5 in all the cases, as reported in Table 2.
Table 2: Statistics of the overall grades auto-assessed by
participants reported in terms of minimum (min) and maxi-
mum (max) score, mean value, standard deviation (,
and confidence interval (C.I.) with α = 0.05.
Inspiration Satisfaction Originality Coverage
Range 1-5 1-5 1-5 1-5
Min 2.00 3.00 2.00 2.00
Mean 3.77 4.15 3.81 3.96
St. dev. 0.91 0.78 0.98 1.00
Max 5.00 5.00 5.00 5.00
C.I. [3.42, 4.12] [3.85, 4.45] [3.43, 4.19] [3.58, 4.34]
At Least Half of the Users’ Expectations Are Sat-
isfied. RQ
verifies if word senses automatically re-
trieved by querying KGs cover users’ expectations be-
longing to Italian native speakers’ mental lexicon. As
visible in Figure 3, at least half of the users’ expec-
tations are satisfied in all cases, and the percentage
of coverage is at least equal to 75% if we consider
both word senses and synonyms. It lets us assessing
that word senses automatically retrieved by KGs guar-
antee sufficient/high coverage of adult Italian native
speakers’ expectations. It becomes crucial in the case
of at-a-distance learning to ensure that (young) schol-
ars can be effectively supported by automatic tools
without requiring the support of adults.
Suggestions Require Rationales. LiteralSearch
proposed word senses not foreseen by native speak-
ers in just a few cases. However, those sugges-
tions have been considered inaccurate by participants
), even if they can be justified by looking at Ital-
ian dictionaries. It suggested that it is not always ob-
vious to understand why any out-of-context word is
proposed as a word sense by LiteralSearch.Hence,
suggestions should be attached to exemplary sen-
tences or definitions to better understand the link be-
tween the user-selected word and the returned sug-
gestion. This insight will drive future enhancements
of the LiteralSearch prototype.
Not a Single Winner! Looking at the suggestions
returned by each KG, there is no single resource able
to satisfy users’ expectations fully. Hence, it is neces-
sary to combine results returned by multiple KGs to
reach the current coverage of users’ expectations. It
is worth noting that according to how the evaluation
has been performed, LiteralSearch compared with
each queried source. Since suggestions from multiple
KGs outperforms each resource queried in isolation,
LiteralSearch outperforms related works.
Inspiration Requires Relaxing Semantic Relations
Classification. By both considering suggestions re-
turned as word senses and synonyms, we reached a
wider coverage of users’ expectations. In fact, na-
tive speakers also often jump among word senses,
analogies, and synonyms without separating seman-
tic relations. While in the learning setting, scholars
must have a clear definition and unambiguous exam-
ples of semantic relations, when we hypothesize to
use LiteralSearch to overcome the blank page syn-
drome, suggestions returned by different semantic re-
lations might be merged into a unified set to inspire
users without any hard classifications. Consequently,
learning and inspiration modalities have been sepa-
rately proposed in LiteralSearch.
This article explores the coverage and accuracy per-
ceived by Italian native speakers in playing with lex-
ical semantics via a KG-based approach. The per-
formed evaluation relies on LiteralSearch, a web-
based interface to retrieve semantic relations starting
from a user-defined word, which reaches a good level
of coverage of users’ expectations (RQ
). Hence,
KGs are promising sources to learn and inspire orig-
inal meanings while letting them learn unforeseen
word senses (RQ
). Being an interface for searching
lexical semantic repositories in a very user friendly
fashion, our approach represent a starting point ap-
proach to word-networks exploration. As seen in
(Emma James and Henderson, 2023), it is well-
established that prior knowledge affects new learn-
ing and semantic neighbors also affect the acquisi-
tion of new words. Hence, the proposed approach
can broaden learners’ horizons by suggesting novel
meanings and word interpretations by exploiting word
senses and synonyms. Learning lexical semantics via
KGs has the potential to be useful also for young
learners or foreigners to explore and learn about word
meanings and to get inspiration to overcome the blank
page syndrome in storytelling. To conclude, this
CSEDU 2024 - 16th International Conference on Computer Supported Education
study envision a bidirectional human-to-data effort to
both enhance speakers linguistic competence and KG
data access. Moreover in detail, the former is justi-
fied by a high-quality support to explore, retrieve, and
learn about word senses and lexical relations. On the
other side, synsets do not distinguish formalism, sug-
gesting as synonyms words that cannot really be used
as alternatives. Further human efforts are required to
explicitly tag words in synsets by their formalism.
Limitations and Future Directions. KGs are usu-
ally well-curated for English speakers. Even if the
queried KGs aim to provide end-users with multilin-
gual semantic networks, further effort should be in-
vested in wider coverage of other languages, such as
Italian. As already stated while discussing results,
suggestions require to be contextualized and justified.
It will result in enhancing LiteralSearch sugges-
tions by providing users with examples, descriptions,
or justifications in terms of retrieved semantic relation
in the direction of explaining proposed suggestions.
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Broaden Your Horizon! Play with Semantics via a Knowledge Graph-Based Approach