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Current AI technologies and data representations often reflect the popular or majority vote. This is an inherent artefact of the frequency bias of many statistical analysis methods that are used to create for example knowledge graphs, resulting in simplified representations of the world in which diverse perspectives are underrepresented. With the use of AI-infused tools ever increasing, as well as the diverse audiences using these tools, this bias needs to be addressed in both the algorithms analysing data, as well as in the resulting representations. In this problems to solve before you die submission, we explain the implications of the lack of polyvocality and contextual knowledge in the semantic web. We identify three challenges for the Semantic Web community on dealing with various voices and perspectives as well as our vision for addressing it.

Historical newspapers provide a lens on customs and habits of the past. For example, recipes published in newspapers highlight what and how we ate and thought about food. The challenge here is that newspaper data is often unstructured and highly varied. Digitised historical newspapers add an additional challenge, namely that of fluctuations in OCR quality. Therefore, it is difficult to locate and extract recipes from them. We present our approach based on distant supervision and automatically extracted lexicons to identify recipes in digitised historical newspapers, to generate recipe tags, and to extract ingredient information. We provide OCR quality indicators and their impact on the extraction process. We enrich the recipes with links to information on the ingredients. Our research shows how natural language processing, machine learning, and semantic web can be combined to construct a rich dataset from heterogeneous newspapers for the historical analysis of food culture.