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Voice assistants have become embedded in people’s private spaces and domestic lives where they gather enormous amounts of personal information which is why they evoke serious privacy concerns. The paper reports the findings from a mixed-method study with 65 digital natives, their attitudes to privacy and actual and intended behaviour in privacy-sensitive situations and contexts. It also presents their recommendations to governments or organisations with regard to protecting their data. The results show that the majority are concerned about privacy but are willing to disclose personal data if the benefits outweigh the risks. The prevailing attitude is one characterised by uncertainty about what happens with their data, powerlessness about controlling their use, mistrust in big tech companies and uneasiness about the lack of transparency. Few take steps to self-manage their privacy, but rely on the government to take measures at the political and regulatory level. The respondents, however, show scant awareness of existing or planned legislation such as the GDPR and the Digital Services Act, respectively. A few participants are anxious to defend the analogue world and limit digitalization in general which in their opinion only opens the gate to surveillance and misuse.

The advent of digitalization exposes enterprises to an ongoing transformation with the challenge to quickly capture relevant aspects of changes. This brings the demand to create or adapt domain-specific modeling languages (DSMLs) efficiently and in a timely manner, which, on the contrary, is a complex and timeconsuming engineering task. This is not just due to the required high expertise in both knowledge engineering and targeted domain. It is also due to the sequential approach that still characterizes the accommodation of new requirements in modeling language engineering. In this paper we present a DSML adaptation approach where agility is fostered by merging engineering phases in a single modeling environment. This is supported by ontology concepts, which are tightly coupled with DSML constructs. Hence, a modeling environment is being developed that enables a modeling language to be adapted on-the-fly. An initial set of operators is presented for the rapid and efficient adaptation of both syntax and semantics of modeling languages. The approach allows modeling languages to be quickly released for usage.

On-To-Knowledge builds an ontology-based tool environment to improve knowledge management, dealing with large numbers of heterogeneous, distributed, and semi-structured documents typically found in large company intranets and the World Wide Web. The project's target results are: (i) a toolset for semantic information processing and user access; (ii) OIL, an ontology-based inference layer for the World Wide Web; (iii) an associated methodology and validation by industrial case studies. This chapter offers an overview of the On-To-Knowledge approach to knowledge management.