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.

This paper describes the ongoing work in converting the lexicographic collection of a non-standard German language dataset (Bavarian Dialects) into a Linguistic Linked Open Data (LLOD) format. The collection is divided into two: questionnaire dataset (DBÖ) which contains details of the questionnaires, questions, collectors, paper slips etc., and the lexical dataset (WBÖ) which contains lexical entries (answers) collected in response to the questions. In its current form, the lexical dataset is available in a TEI/XML format separately from the questionnaire dataset. This paper presents the mapping of the lexical entries in the TEI/XML format into LLOD format using the Ontolex-Lemon model. The paper shows how the data in TEI/XML format is transformed into LLOD and produces a lexicon for Bavarian Dialects. It also presents the approach used to interlink the original questions with the lexical entries. The resulting lexicon complements the questionnaire dataset, which is already in a LLOD format, by semantically interlinking the original questions with the answers and vice-versa.