Towards Sense to Sense Linking across DB nary Languages (2025)

Abstract: Since 2012, the DBnary project extracts lexical information from different Wiktionary language editions (26 editions in 2025) and makes it available to the community as queryable RDF data (modeled using ontolex-lemon ontology). This dataset contains more than 12M translations linking languages at the level of Lexical Entries. This paper presents an effort to automatically link the DBnary languages at the Lexical Sense level. For this we explore different ways to compute cross-lingual semantic similarity, using multilingual language models.

## introduction even in the era of large language models pretrained in unsupervised settings, lexical resources (lr) are still in use and have proved useful for advancing various natural language processing (nlp) tasks. for instance, such resources may enhance the quality of machine translation by providing accurate cross-lingual mappings, thus improving translation fidelity @xcite . they are also of importance for end users that easily refer to them through on-line browsing or mobile dictionary apps. since 2012, the dbnary dataset foot_0 extracts lexical data from 26 wiktionary language editions foot_1 and makes it available as an rdf dataset. extracted from one of the most important community built lexical resource, it contains lexical entries in many languages, along with definitions, lexico-semantic relations, translation, among other lexical information. one of the shortcomings of the current dataset lies in the lack of semantic alignment between language editions. the final objective of this work will be twofold: 1. providing cross-lingual links at the lexical sense level, based on the translations available at the entry or surface form level, and 2. associating each dbnary lexical sense with an embedding in a unique multilingual vector space. in this paper, we explore the use of multilingual neural language models for the computation of a cross-lingual semantic similarity measure that we use to align existing translation pairs at the semantic level. after describing the current way dbnary dataset models cross-lingual links (section 2), we will define the task at hand and the related work we borrow from (section 3), then describe a gold standard dataset we built to evaluate different approaches (section 3.2). we proceed with the experiments (section 4) and results (section 5) and discuss shortcomings of the approaches for the systematic modelling of translations at the semantic level and for the distribution of sense embeddings to the end users (section 6). ## the dbnary dataset dbnary @xcite is a large multilingual lexical dataset extracted from 26 language editions of the wiktionary project. it is made available following the lexical linked open data principles using ontolex-lemon model in rdf format, following @xcite . overall, this dataset describes 7.9m lexical entries, accounting for 6.5m lexical senses, usually described with a textual definition. additionally, it contains 12.3m translation pairs. the ontolex foot_2 model, is a community standard for machine-readable lexical resources that has been adopted by many data providers for its ability to ensure fair principles, 4 and the dominant vocabulary for modeling machine-readable dictionaries as linked data. the goal of ontolex is to represent lexical resources as a knowledge graph, allowing the integration of information from different dictionaries and to facilitate the exchange, storage, and reusability of lexical information. the ontolex-lemon model is a w3c community report consisting of a core model, along with additional modules (mainly, lime for metadata, synsem for the description of syntax and semantics, decomp for decomposition of terms into subterms and vartrans to represent lexico-semantic and translation relations). the atomic unit of information of wiktionary is a page, where several lexical entries may be described. such entries usually share their canonical form (which usually corresponds to the name of the page). the organisation of entries in the page and the structure and content of such entries differ according to the wiktionary language editions, but usually contain definitions of the senses and a set of lexical information (etymology, morphology, lexico-semantic derivations, ...), along with translations in other languages. as an example, the cat eng 5 page in the english edition describes 6 entries (3 nouns, 2 verbs and 1 adjective). other lexical entries in other languages are also described and extracted in dbnary; however, for this article, we will focus only on the lexical entries of the language edition (what we call the endolex). dbnary uses ontolex core model to represent the extracted data, hence, it gives a common representation of lexical data, despite the differences in the way such lexical information is organised in 5 in this article we will use entry lg to denote the page named "entry" in the edition for language lg, this particular page is available at https://en.wiktionary.org/wiki/ cat each edition. ad-hoc classes were added to be able to represent pages (dbnary:page) and also to represent translations pairs (dbnary:translation), along with glosses (dbnary:gloss) that are often used to provide users with context to disambiguate the lexical sense for which a lexical information stands. the dbnary resulting model is shown in figure 1 . (b) the wiktionary data is often insufficient to decide if the target of a translation is a lexical entry in the target language, and, if it is, which one (in case of homonymy), also, there are usually no information allowing to decide on the target sense that is involved in a translation. figure 2 : a graphical representation of the cross-lingual linking strategies, according to the semantic level of the links and inspired by the vauquois triangle @xcite ) according to @xcite . the ontolex model defines the vartrans extension to encode relations in general and translations in particular. however, dbnary authors chose a nonstandard representation. the reason is illustrated in figure 2 . figure 2a shows that the vartrans module may be used either to link two lexical entries or two lexical senses together. however, in wiktionary, each language edition is independent of the other, and the available translations are given as strings, with no guarantee that they correspond to a lexical entry in the target edition. for example, persignar cat is translated in english as "cross oneself on forehead, lips, and heart", which does not correspond to a valid english lexical entry. also, since wiktionary is an ongoing collaborative dictionary, the target page may not exist while being a perfectly valid lexical entry in the target lexicon. finally, even if the page exists, it is not possible to systematically decide which lexical entry is the target of the translation. for all these reasons, dbnary decided to reify each translation pair using dbnary:translation class where the source lexical entry is usually known and the target is represented as a surface form rather than as a link to a lexical entry in the target lexicon (in blue in figure 2b ). many translations are also associated with their source lexical sense (in dotted green in figure 2b ) that are selected using @xcite with an accuracy of 0.82 to 0.96 f1 score. ## linking language editions at the lexical sense level the task we address in this paper is the following: "how can we efficiently identify the correct source and target lexical sense(s) that are involved in available translation pairs?" the main objective of this work is to include such links using the vartrans module in dbnary and, as a by-product, provide the multilingual lexical sense embeddings that allowed for this linking. with 12m translations and more than 6m lexical senses with definitions that continuously change while the language communities collaboratively correct and expand the editions, dbnary extracts a new version twice a month. so, links must be recomputed at each extraction, and the efficiency of the method should be assessed both in terms of performance and in terms of frugality in computing resources. ## results all tasks are evaluated using standard set-matching metrics, i.e. recall, precision and f1 score. all scores are computed using the optimal hyperparameters setting. ## discussions and limitations monolingual evaluation shows that it is still difficult to surpass @xcite which is based solely on string distance computation, with a very small energy cost for all languages. in this task, the added value of some models may not justify the energy cost. thanks to the manually built gold standard, we also showed that the original results were underestimated. in multilingual settings, sentence embedding models yield the best scores. however, it is not entirely clear whether the overall performance is sufficient to create links of high quality for inclusion in a distributed dataset. more detailed analysis should be conducted to understand what makes this task so challenging. the structure of definitions (frequently structured as genus-differencia) may differ significantly from the structure of sentences used to train the sentence embedding models. moreover, lexical senses of the same entry are expected to share most of their semantics, and the task at hand should focus more on their differences rather than on their similarities. openai models delivered the best results in both monolingual and multilingual tasks. however, we cannot rely on these embeddings for our use case. the first reason is a general concern that arises when openai models or chat services are used in a research setting. the fact that openai embeddings, architectures, and datasets are closed source and largely unexplained prevents us from gaining any understanding of the reasons behind their success, nor can we determine whether the success on the gold standard is generalisable to real-world data. although we are relatively confident that our (newly created) dataset was not part of those model training, we also have a particular reason to exclude openai from our work. our objective is twofold: 1) to provide senseto-sense translation links and 2) to distribute the embeddings associated with each lexical sense, so that end users can compute similarities at the lexical sense level. for the first objective, the best model should be used if its energy cost is reasonable on the scale of the full dbnary dataset. however, providing embeddings tied to a closed-source, proprietary model would tie dbnary users to the model provider for their own use case. this would force any user of the dbnary embeddings to pay openai to exploit them in their use cases. finally we should also note two limitations of this preliminary work. the first one comes from the dataset that only shows translation from english to other languages. other sources should be added to the dataset. moreover, more entries should be added to get more chinese and russian translations in order to have more significant results. the second limitation comes from the main methodology. in this preliminary work, we take the glosses as a starting point for sense-to-sense link computation. the monolingual task identify the source lexical sense from the gloss, then the multilingual task is performed using the identified source lexical sense to identified the target one. this means that we can only deal with translations that are associated to a gloss. in english this rep-resents 96.8% of the available translations. for all other translations, the sense-to-sense cross-lingual link cannot be computed with this process and 3.2% of the available translations will be disregarded (see appendix a for full statistics on the availability of glosses in all dbnary extracted languages). among all dbnary languages, the proportion of translations that are associated to a textual gloss are very imbalanced from 0% to 99.6%. as an example, with this methodology we will only handle 34.6% of the translations of french words. ## conclusion this preliminary study is a first step towards a better cross-lingual link modelling in the dbnary dataset. although some results are encouraging, additional work is required to achieve our goal. focussing on our first objective (identifying lexical senses involved in translation pairs), definition embeddings bring a lot but will certainly benefit of other approaches. if we succeed in computing a cross-lingual semantic similarity measure between dbnary lexical senses, we will be able to provide such crosslingual links, but also distribute embeddings for each lexical sense in the dataset. such embeddings could be used to query for semantically related senses and could be reused by end-users in downstream tasks. many applications would benefit from these: browsing dbnary with direct access to close senses, bootstrapping/aligning models with lexical sense embedding rather than embeddings associated to surface forms, or linking such senses with several ontologies. however, more work should be done before achieving results that are good enough for the computed data to be distributed along with the original dbnary data. for this, we need to further study the similarity measures we may apply for such definitions that could take into account the specificities of definition and better discriminate between definitions that share much semantics but differ on specific aspects. other solutions should also be investigated to handle all definitions that are not associated with a source gloss, in order to benefit from the richness of the original wiktionary data.