Professional ethics committees currently lack structured tools to identify relevant ethical concepts from complex narratives and compare them against prior decisions. ProEthica analyzes professional ethical scenarios against established codes and precedent cases. The system uses large language models (LLMs), leveraging their natural language processing capabilities to extract nine types of components (Roles, Principles, Obligations, States, Resources, Actions, Events, Capabilities, and Constraints) from case text and scenario descriptions. Domain-specific ontologies provide precise definitions that constrain LLM output to match formal concept specifications, ensuring consistency across extraction and validation. Case-based reasoning identifies precedent cases and analogous situations relevant to the scenario under analysis. The current implementation processes engineering ethics cases with complete provenance tracking and ontology-driven validation. The framework supports extension to other professional domains with established codes and precedent systems.

Abstract Advertisements have always been a part of our cultural heritage, and this also applies to online web advertisements. Unlike print ads, there are serious technical challenges involved in archiving and successfully replaying ads displayed in web pages. To explore these challenges, we created a small dataset of 250 web ads. Ultimately, we collected and archived 279 ads, which we classified into 5 different categories: combination ads, image ads, embedded web page ads, video ads, and text‐only ads. In our sample, combination ads were the most prevalent and, not surprisingly, text‐only ads were the easiest to archive and replay. During the course of this study, we encountered five major problems in archiving and replaying the web ads. We detail the issues uncovered and provide suggestions for ameliorating the replay of some web ads, in addition to other dynamically loaded embedded web resources.

This paper presents a use case exploring the application of the Archival Resource Key (ARK) persistent identifier for promoting and maintaining ontologies. In particular we look at improving computation with an in-house ontology server in the context of temporally aligned vocabularies. This effort demonstrates the utility of ARKs in preparing historical ontologies for computational archival science.