Reconstructing Language History by Using a Phonological Ontology. An Analysis of G erman Surnames (2023)

Abstract: This paper applies the ontology-baseddialectometric technique of Engsterhold(2020) to surnames. The method wasoriginally developed for phonetic analyses. However, as will be shown, it is also suitedfor the study of graphemic representations. Based on data from the German SurnameAtlas (DFA), the method is optimized forgraphemic analysis and illustrated with anexample case.

## method in order to process large amounts of data and, at the same time, apply the inferences based on the ontology, phonontology makes use of semantic web technologies. the data is organized in a triplestore graph database (graphdb) foot_0 using the resource description framework (rdf) and the web ontology language (owl) as a language for describing the rules that are implemented in the phonetic ontology. the classification of the data is based on cluster analyses. this needs the transformation of data, which is performed via one-hot encoding into a data set that generates a multidimensional feature vector for all locations and for all sounds. subsequently, the data are standardized using z-transformation. in order to optimize classification results, principal component analysis (pca) is performed so that the resulting data set has fewer dimensions but still explains most of the variance in the data set. the clustering algorithms used are k-means, ward's agglomerative clustering (ward), and the gaussian mixture model (gmm). in our study we added k-medoids and spectral clustering (k-nearest neighbor, sc-knn). since no ground truth or verification dataset is available, the evaluation of the cluster analysis is limited to intrinsic metrics. thus, primarily cluster stability is evaluated. for this purpose, the silhouette coefficient (sc) and the calinski-harabasz index (ch) are used. in addition, bootstrapping and k-fold methods are used to generate pseudo-ground truths, which can then be used to evaluate classification results (cf. engsterhold 2020). in this way, cluster analysis based on the phonontology allows the semi-automated investigation of sound properties across all tokens and phenomena of a given corpus. this provides a deeper insight into the sound-related structure of a study area under discussion. the method is free of interpretative assumptions and designed for large data sets. it offers the possibility to highlight and evaluate structures in a chaotic-looking data set. the architecture is similar to the built-up of the phoible database @xcite . the classification methodology is similar to the methods described in @xcite . ## conclusion the paper has shown that the ontology-based analysis technique provides a tool which allows to investigate the regional distribution of phonological characteristics in the german surnames. at the same time, it is possible to detect the spatial extension of historical sound changes that are mirrored in the surnames. we assume that the surnames not only represent fossils of historical spoken language but also developments in regional and transregional writing traditions. a characteristic of our approach is the multidimensional processing of the surname data, which provides a variety of starting points for further research. depending on how the data set is filtered, different perspectives are possible. either the diachronic and diatopic developments of a single historical reference sound are investigated, or the analysis is broadened to describe the major graphemic and phonological features of the surname landscapes of germany.