The article deals with data wrangling in a multilingual collection intended for diachronic analysis and linguistic linked open data modelling for tracing concept change over time.
The article deals with data wrangling in a multilingual collection intended for diachronic analysis and linguistic linked open data modelling for tracing concept change over time. Two types of static word embeddings are used: word2vec (French and Hebrew data sets), and fastText (Latin and Lithuanian data sets). We model examples from these embeddings via the OntoLex-FrAC formalism. To address the challenge of heterogeneity, we use a minimalist workflow design allowing for both convergence and flexibility in attaining the project goals.
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