Academic Activities

Little-known lexicographic resources can be of tremendous value to users once digitised. By extending the digitisation efforts for a lexicographic resource, converting the human readable digital object to a state that is also machine-readable, structured data can be created that is semantically interoperable, thereby enabling the lexicographic resource to access, and be accessed by, other semantically interoperable resources. The purpose of this study is to formulate a process when converting a lexicographic resource in print form to a machine-readable bilingual lexicographic resource applying linguistic linked data principles, using the English-Xhosa Dictionary for Nurses as a case study. This is accomplished by creating a linked data framework, in which data are expressed in the form of RDF triples and URIs, in a manner which allows for extensibility to a multilingual resource. Click languages with characters not typically represented by the Roman alphabet are also considered. The purpose of this linked data framework is to define each lexical entry as “historically dynamic”, instead of “ontologically static” (Rafferty, 2016:5). For a framework which has instances in constant evolution, focus is thus given to the management of provenance and linked data generation thereof. The output is an implementation framework which provides methodological guidelines for similar language resources in the interdisciplinary field of Library and Information Science.

When modelling linguistic resources as Linked Data, the identification of languages using language tags and language codes is a mandatory task. IETF’s BCP 47 defines the standard for tags, and ISO 639 provides the codes. However, these codes are insufficient for the identification of diatopic variation within a language and, also, for different historical language stages. This weakness hampers the accurate identification of data, which in turn leads to ambiguity when extending, aggregating and re-using this data—a key notion of Linked Open Data and the Semantic Web. We show the limitations of language identification with a case study of French linguistic data from both a diachronic and a diatopic perspective. Our exemplary data derives from dictionaries of Old French, Middle French, and of Modern French dialects, and from a Modern French linguistic atlas. For each exemplar, we propose a solution using the privateuse sub-tag of BCP 47’s language tag, staying within the boundaries of existing standards. Using a predefined pattern for the privateuse sub-tag, the solutions enable a dialect, a patois, in combination with a time period, to be defined and identified. This can lead to shared agreement of language tags that will increase interoperability within the context of Linked Data.

Several annotation models have been proposed to enable a multilingual Semantic Web. Such models hone in on the word and its morphology and assume the language tag and URI comes from external resources. These resources, such as ISO 639 and Glottolog, have limited coverage of the world’s languages and have a very limited thesaurus-like structure at best, which hampers language annotation, hence constraining research in Digital Humanities and other fields. To resolve this ‘outsourced’ task of the current models, we developed a model for representing information about languages, the Model for Language Annotation (MoLA), such that basic language information can be recorded consistently and therewith queried and analyzed as well. This includes the various types of languages, families, and the relations among them. MoLA is formalized in OWL so that it can integrate with Linguistic Linked Data resources. Sufficient coverage of MoLA is demonstrated with the use case of French.

In recent years, the modeling of data from linguistic resources with Resource Description Framework (RDF), following the Linked Data paradigm and using the OntoLex-Lemon vocabulary, has become a prevalent method to create datasets for a multilingual web of data. An important aspect of data modeling is the use of language tags to mark lexicons, lexemes, word senses, etc. of a linguistic dataset. However, attempts to model data from lesser-known languages show significant shortcomings with the authoritative list of language codes by ISO 639: for many lesser-known languages spoken by minorities and also for historical stages of languages, language codes, the basis of language tags, are simply not available. This paper discusses these shortcomings based on the examples of three such languages, i.e., two varieties of click languages of Southern Africa together with Old French, and suggests solutions for the issues identified.

The English-Xhosa Dictionary for Nurses (EXDN) is a bilingual, unidirectional printed dictionary in the public domain, with English and isiXhosa as the language pair. By extending the digitisation efforts of EXDN from a human-readable digital object to a machine-readable state, using Resource Description Framework (RDF) as the data model, semantically interoperable structured data can be created, thus enabling EXDN’s data to be reused, aggregated and integrated with other language resources, where it can serve as a potential aid in the development of future language resources for isiXhosa, an under-resourced language in South Africa. The methodological guidelines for the construction of a Linguistic Linked Data framework (LLDF) for a lexicographic resource, as applied to EXDN, are described, where an LLDF can be defined as a framework: (1) which describes data in RDF, (2) using a model designed for the representation of linguistic information, (3) which adheres to Linked Data principles, and (4) which supports versioning, allowing for change. The result is a bidirectional lexicographic resource, previously bounded and static, now unbounded and evolving, with the ability to extend to multilingualism.

The English-Xhosa Dictionary for Nurses is a unidirectional dictionary with English and isiXhosa as the language pair, published in 1935 and recently converted to Linguistic Linked Data. Using the Ontolex-Lemon model, an ontological framework was created, where the purpose was to present each lexical entry as “historically dynamic” instead of “ontologically static” (Veltman, 2006:6, cited in Rafferty, 2016:5), therefore the provenance information and generation of linked data for an ontological framework with instances constantly evolving was given particular attention. The output is a framework which provides guidelines for similar applications regarding URI patterns, provenance, versioning, and the generation of RDF data.