Modelling Frequency, Attestation, and Corpus-Based Information with O nto L ex- F r AC (2022)

Abstract: OntoLex-Lemon has become a de facto standard for lexical resources in the web of data. This paper provides the first overall description of the emerging OntoLex module for Frequency, Attestations, and Corpus-Based Information (OntoLex-FrAC) that is intended to complement OntoLex-Lemon with the necessary vocabulary to represent major types of information found in or automatically derived from corpora, for applications in both language technology and the language sciences.

## background the ontolex-lemon vocabulary has become the dominant vocabulary for modelling machinereadable dictionaries on the web of data, i.e., by means of rdf. and indeed, publishing lexical resources in rdf has a number of advantages, including the ease of integration of dictionary information not only with ontologies and knowledge graphs (this was the original domain of application), but also with other lexical data. figure 1 illustrates the ontolex-lemon core vocabulary. primary data structures are ontolex: lexicalentry (lexeme), ontolex:form (word form), ontolex:lexicalsense (word figure 1 : ontolex-lemon core module sense), and ontolex:lexicalconcept (lexicalization-independent concept), so that lexical entries can be described, but also fine-grained differences in meaning and surface form. while these aspects are advanced, stable and widely used, there is no complete module described in the current literature that enables interoperability and integration between lexical and textual resources and the distributional semantics of words, lexical senses and concepts, and collocation properties. by employing the usage of l(l)od (linked linguistic open data) technologies, we describe the consolidation of ontolex-frac (frequency, attestation, and corpus information), an ontolex-lemon model that (1) addresses the requirements of corpus-based lexicography (frequency and collocation information) and digital philology (linking lexical resources with corpus data), and (2) provides a standard for encoding, storing, and exchanging vector representations of words along with their lexical concepts, senses, and lemmas. ## core concepts so far, the development of frac has been conducted in a bottom-up fashion, where uses cases were analyzed and sub-vocabularies for different phenomena have been proposed. this includes frequency and attestations @xcite , embeddings and similarity @xcite and collocations @xcite . we complement these efforts with a top-down perspective, and we suggest three top-level classes to structure the model as a whole. in addition to that, we provide an owl2/dl ontology to formalize the vocabulary. restructuring the module entails a number of minor revisions regarding naming and scope of properties and classes, however, we aimed to stay faithful to the original definitions while integrating them into a more coherent overall picture. the frac vocabulary is about information from or derived from corpora that can be included in machine-readable dictionaries and other forms of lexical or ontological resources, i.e., information about lexical forms (which can be counted), lexical entries (which can be illustrated with attestations or corpus examples), lexical senses or lexical concepts (which can be found as annotations in corpora). for these, frac introduced a generalization over the ontolex core elements (and any other entity frac-related information is to be expressed about), and introduced the notion of frac:observable, i.e., a lexical unit that can be observed in natural language, e.g., in a corpus. the corpus class was a another vocabulary element introduced with frac, and it is understood here in the more general sense of structured (collections of) primary data. 1 in addition to representing the primary data itself, it can also provide the total number of tokens in the corpus frac:total. the different frac sub-vocabularies then defined different concepts that define the relation between observables and the corpus (or anyuri) object. a novel contribution of our paper is that we introduce a generalization over these frac-specific classes. in analogy with frac:observable, we refer to this as frac:observation. an observation in this understanding is any information found in, based on or created from a corpus, and the observations supported by the frac vocabulary are corpus frequency, attestation, collocation, similarity and embeddings. we consider aggregate observations (frequency, collocations, embeddings, similarity clusters) to be observations in their own right, as long as their characteristics are solely defined by the underlying data. frac observations have a number of common properties: ## frac observations we propose four main classes as subclasses of frac:observation, i.e., frequency, attestation, collocations, embeddings, and similarity as summarized in fig. 2 . ## consolidation and outlook the revised frac vocabulary proposed in this paper introduces the novel class frac: observation as a generalization over different kinds of phenomena that can be observed from or derived from corpus data and that are relevant for lexical resources. with small changes to previously proposed vocabulary elements, this revised top-level structure can be seamlessly applied to use cases and sample data featured in previous publications on frac. the following changes have been proposed: we illustrate the applicability of this model to a number of examples, mostly with reference to the brown corpus. we argue that with this in formation, it becomes possible for the first time to encode both the majority of lexical information derived from the brown corpus in a unified way, and to thus integrate those resources on a technical level. with frac, all these different aspects can be encoded in rdf and this representation can be the basis to define interoperable apis for different web services, apis or applications to produce, consume or integrate such data. at the moment, the state of the art in this area is probably best represented by the proprietary sketchengine apis, whose responses are, however, do not come with any guarantees for long-term stability or reproducibility. furthermore, a number of aspects are not well-supported by sketchengine: this includes, for example, the online reference to individual attestations (sketchengine only provides resolvable uris for query responses, but not for the individual matches), or the retrieval of embeddings (provided by sketchengine but only as data dumps, not integrated in the api). another possible application is to provide dumps of corpusderived information (of attestations, embeddings, collocations, similarity clusters or frequency lists) along with the associated lexical graph. as it provides uniform data structures on the basis of web standards, frac represents the fun-dament to develop consistent access protocols for the unified access, public exposure, exchange and integration of heterogeneous data as currently provided, for example, via the linguistic linked open data cloud @xcite , libraries such as nltk @xcite or via portals such as huggingface @xcite . at the same time, frac accomodates the needs of digital lexicography and the language sciences, and has partially been motivated by applicability to philological data @xcite and multimedia content @xcite . as a result of applying ontolex and frac, resources developed in lexicography become accessible and re-usable in the context of language technology, and resources and solutions developed in language technology become applicable to lexicographic and linguistic data and research challenges. the second novel contribution of this paper is that we provide an owl2/dl ontology as formalization of the revised frac vocabulary. this allows to automatically validate frac data, to detect inconsistencies and to perform reasoning (inferences) over frac data. in particular, types (classes) of observations and observables can be automatically (rdfs-)inferred from domain and range constraints of properties such as frac:frequency, frac:attestation, frac:embedding, etc., so that this information is in fact optional in data exchange. likewise, formal owl2/dl axioms allow users to define application-specific subclasses of frac:corpusfrequency, etc., so that these can be used as a short hand for specific bundles of observations with frac:corpus, rdf:value, dc:description and other properties that constrain the corpus under consideration or that define hyperparameters used in the extraction process. it is important to note that -to the best of our knowledge -no rdf vocabulary of similar scope was in existence prior to frac, and that the integration with ontolex facilitates a relatively wide application across different disciplines and research networks. it is less certain whether there are comparable pre-rdf vocabularies in existence. we mentioned the sketchengine api and exchange formats, but as far as open (community) standards are concerned, we are not aware of any related work of similar scope. nevertheless, aspects of corpusdriven lexicography have been addressed in the lexical markup framework @xcite for attestation) and the tei guidelines @xcite , for collocation), but we are not aware of any formal standard for embeddings or machinereadable similarity scores. by extending ontolex with a designated module for frequency, attestation and corpus-based information in lexical resources, we are thus breaking novel ground.