In order to access indigenous, regional knowledge contained in language corpora, semantic tools and network methods are most typically employed. In this paper we present an approach for the identification of dialectal variations of words, or words that do not pertain to High German, on the example of non-standard language legacy collection questionnaires of the Bavarian Dialects in Austria (DBÖ). Based on selected cultural categories relevant to the wider project context, common words from each of these cultural categories and their lemmas using GermaLemma were identified. Through word embedding models the semantic vicinity of each word was explored, followed by the use of German Wordnet (Germanet) and the Hunspell tool. Whilst none of these tools have a comprehensive coverage of standard German words, they serve as an indication of dialects in specific semantic hierarchies. Methods and tools applied in this study may serve as an example for other similar projects dealing with non-standard or endangered language collections, aiming to access, analyze and ultimately preserve native regional language heritage.

Semantic enrichment of historical images to build interactive AI systems for the Digital Humanities domain has recently gained significant attention. However, before implementing any semantic enrichment tool for building AI systems, it is also crucial to analyse the quality and richness of the existing datasets and understand the areas where semantic enrichment is most required. Here, we propose an approach to conducting a preliminary analysis of selected historical images from the Europeana platform using existing linked data quality assessment tools. The analysis targets food images by collecting metadata provided from curators such as Galleries, Libraries, Archives and Museums (GLAMs) and cultural aggregators such as Europeana. We identified metrics to evaluate the quality of the metadata associated with food-related images which are harvested from the Europeana platform. In this paper, we present the food-image dataset, the associated metadata and our proposed method for the assessment. The results of our assessment will be used to guide the current effort to semantically enrich the images and build high-quality metadata using Computer Vision.

In this paper we present a semantic enrichment approach for linking two distinct data sets: the ÖBL (Austrian Biographical Dictionary) and the DBÖ (Database of Bavarian Dialects in Austria). Although the data sets are different in their content and in the structuring of data, they contain similar common “entities” such as names of persons. Here we describe the semantic enrichment process of how these data sets can be inter-linked through URIs (Uniform Resource Identifiers) taking person names as a concrete example. Moreover, we also point to societal benefits of applying such semantic enrichment methods in order to open and connect our resources to various services.