A model of a universal linguistic knowledge graph for Turkic languages

Abstract

The article describes a new model of a universal linguistic knowledge graph for Turkic languages as a basis for creating ontolinguistic resources. In the model, world knowledge is formalized as an ontological core that integrates taxonomic and situational ontologies (by analogy with WordNet and FrameNet) and is closely interrelated with linguistic data from Turkic languages. The relevance of the work is determined by the low-resource nature of most Turkic languages and the loss of effectiveness of neural network approaches in processing agglutinative languages with rich morphology. The goal of the project is to develop a new model of a universal linguistic knowledge graph for Turkic languages. The objectives are: integrating world knowledge and deep linguistic information at all levels of the language system; constructing the graph according to the principle of a semiotic vertical; ensuring the morphemocentricity of the model; and integrating it with international language standards. The foundational method is a pragmatically-oriented approach that focuses the research domain on the structural and functional features of Turkic languages. The knowledge graph is constructed according to the principle of a semiotic vertical that unites five levels – phonological, morphological, syntactic, semantic, and pragmatic – as well as three inter-level bridges (morphonological, morphosyntactic, and morphosemantic). A key feature of the model is its morphemocentricity: the morpheme acts as the basic linguistic unit, connected with units of all language levels of the model, which fundamentally distinguishes this approach from word-centric models for fusional languages. The model is integrated with international standards: IPA, Universal Dependencies, UDS, UMR, FrameNet, WordNet, and DiAML, each of which is used to describe linguistic elements and relations at its own level. The model has already found practical application in several software products, such as the linguistic portal “Turkic Morpheme” and the new version of the Tatar corpus “Tugan Tel”. Prospects include improving the interpretability of LLMs, improving the quality of machine translation, and unifying annotations of electronic corpora.